Study 1
library(tidyverse)
library(easystats)
library(patchwork)
library(lavaan)
library(ggraph)
library(tidySEM)
library(EGAnet)
library(haven)
ias_vars <- c(
"Heart", "Hungry", "Breathing", "Thirsty", "Urinate", "Defecate", "Taste", "Vomit", "Sneeze", "Cough", "Temp",
"Sex_arousal", "Wind", "Burp", "Muscles", "Bruise", "Pain", "Blood_Sugar", "Affective_touch", "Tickle", "Itch"
)df1a <- haven::read_sav("../data/Murphy2020/Study 1.sav") |>
mutate_all(as.numeric) |>
mutate(Gender = as.character(ifelse(Gender == 1, "Male", ifelse(Gender == 2, "Female", "Other"))))
df1b <- haven::read_sav("../data/Murphy2020/Study 6 IAS.sav") |>
mutate_all(as.numeric) |>
mutate(Gender = as.character(ifelse(Gender == 1, "Male", ifelse(Gender == 2, "Female", "Other"))))load("../data/Gaggero2021/data.rda")
df2 <- data |>
mutate(Gender = as.character(gender)) |>
mutate(across(starts_with("IAS "), as.numeric)) |>
rename(
Age=age, Heart = `IAS 1`, Hungry = `IAS 2`, Breathing = `IAS 3`, Thirsty = `IAS 4`,
Urinate = `IAS 5`, Defecate = `IAS 6`, Taste = `IAS 7`, Vomit = `IAS 8`, Sneeze = `IAS 9`,
Cough = `IAS 10`, Temp = `IAS 11`, Sex_arousal = `IAS 12`, Wind = `IAS 13`, Burp = `IAS 14`,
Muscles = `IAS 15`, Bruise = `IAS 16`, Pain = `IAS 17`, Blood_Sugar = `IAS 18`,
Affective_touch = `IAS 19`, Tickle = `IAS 20`, Itch = `IAS 21`,
MAIA_Trusting = MAIA_BodyTrusting)
rm(data)
df2$BPQ_BodyAwareness <- rowMeans(select(df2, `BPQ 1`:`BPQ 26`), na.rm = TRUE)
df2$BPQ_AutonomicReactivity <- rowMeans(select(df2, `BPQ 27`:`BPQ 46`), na.rm = TRUE)df3 <- haven::read_sav("../data/Campos2022/Dataset_Test.sav") |>
mutate_all(as.numeric) |>
mutate(Gender = as.character(ifelse(Sex == 1, "Male", ifelse(Sex == 0, "Female", "Other")))) |>
rename(
Heart = IAS1, Hungry = IAS2, Breathing = IAS3, Thirsty = IAS4,
Urinate = IAS5, Defecate = IAS6, Taste = IAS7, Vomit = IAS8, Sneeze = IAS9,
Cough = IAS10, Temp = IAS11, Sex_arousal = IAS12, Wind = IAS13, Burp = IAS14,
Muscles = IAS15, Bruise = IAS16, Pain = IAS17, Blood_Sugar = IAS18,
Affective_touch = IAS19, Tickle = IAS20, Itch = IAS21,
TAS_EOT = TAS_ExternallyOrientedThinking, TAS_DIF = TAS_IdentifyingFeelings, TAS_DDF = TAS_DescribingFeelings
)
df3$BPQ_BodyAwareness <- rowMeans(select(df3, BPQ1:BPQ26), na.rm = TRUE)
df3$BPQ_AutonomicReactivity <- rowMeans(select(df3, BPQ27:BPQ46), na.rm = TRUE)df4 <- haven::read_sav("../data/Todd2022/CompleteData_new.sav") |>
rename_with(\(x) str_remove(x, "IAS_"), .cols = starts_with("IAS_")) |>
rename(
Heart = IAS1, Hungry = IAS2, Breathing = IAS3, Thirsty = IAS4,
Urinate = IAS5, Defecate = IAS6, Taste = IAS7, Vomit = IAS8, Sneeze = IAS9,
Cough = IAS10, Temp = IAS11, Sex_arousal = IAS12, Wind = IAS13, Burp = IAS14,
Muscles = IAS15, Bruise = IAS16, Pain = IAS17, Blood_Sugar = IAS18,
Affective_touch = IAS19, Tickle = IAS20, Itch = IAS21
)
df4$MAIA_AttentionReg <- rowMeans(select(df4, starts_with("MAIA_AR")), na.rm = TRUE)
df4$MAIA_BodyListening <- rowMeans(select(df4, starts_with("MAIA_BL")), na.rm = TRUE)
df4$MAIA_Trusting <- rowMeans(select(df4, starts_with("MAIA_TR")), na.rm = TRUE)
df4$MAIA_EmoAwareness <- rowMeans(select(df4, starts_with("MAIA_EA")), na.rm = TRUE)
df4$MAIA_SelfReg<- rowMeans(select(df4, starts_with("MAIA_SR")), na.rm = TRUE)
df4$MAIA_Noticing <- rowMeans(select(df4, starts_with("MAIA_NT")), na.rm = TRUE)
df4$BPQ_BodyAwareness <- rowMeans(select(df4, starts_with("BPQ_")), na.rm = TRUE)df5_attention <- readxl::read_excel("../data/Arslanova2022/Participants2.xlsx",
sheet = "FINAL") |>
filter(ActorHR == 1) |>
select(Subj.ID, Gender, Age) |>
mutate(Gender = as.numeric(Gender), Age = as.numeric(Age)) |>
filter(!is.na(Age)) |> #error on the documentation of this participant - attention check failed but not noted
mutate(Gender = case_when(
Gender== 0 ~ "Male",
Gender== 1 ~ "Female" # based on paper reporting 65 women
))New names:
• `Why No?` -> `Why No?...4`
• `Why No?` -> `Why No?...6`
• `` -> `...12`
• `` -> `...18`
• `` -> `...19`
• `` -> `...20`
• `` -> `...21`
• `` -> `...22`
• `` -> `...23`
• `` -> `...24`
• `` -> `...25`
• `` -> `...26`
• `` -> `...27`
• `` -> `...28`df5 <- readxl::read_excel("../data/Arslanova2022/Murphy_Iacc_scale.xlsx") |>
filter(str_detect(Question.Key, "IAC_")) |>
filter(str_detect(Question.Key, "-quantised")) |>
pivot_wider(names_from = Question.Key, values_from = Response) |>
rename_with(\(x) str_remove(x, "IAC_"), .cols = starts_with("IAC_")) |>
rename_with(\(x) str_remove(x, "-quantised"), .cols = everything()) |>
rename(Sex_arousal = SexuallyAroused, Itch = Itchy, Sex_arousal = SexuallyAroused,
Temp = HotCold, Tickle = Ticklish, Breathing= BreatheFast,
Affective_touch= PleasantAffectionate, Blood_Sugar= BloodSugar,
Muscles=TiredMuscles, Heart= FastHeart, Taste=Tastes) |>
rename(Subj.ID = "Participant.Public.ID") |>
select(-starts_with("Participant"))
df5 <- df5 |>
filter(Subj.ID %in% df5_attention$Subj.ID) |>
select(-Subj.ID) |>
mutate(across(everything(), as.numeric))df6 <- haven::read_sav("../data/Brand2022/LatentVariableApproach.sav") |>
select(-SERIAL) |>
mutate_all(as.numeric) |>
mutate(Gender = as.character(ifelse(GENDER == 2, "Male", ifelse(GENDER == 1, "Female", "Other")))) |>
rename(
Age=AGE, Heart = IAS_01, Hungry = IAS_02, Breathing = IAS_03, Thirsty = IAS_04,
Urinate = IAS_05, Defecate = IAS_06, Taste = IAS_07, Vomit = IAS_08, Sneeze = IAS_09,
Cough = IAS_10, Temp = IAS_11, Sex_arousal = IAS_12, Wind = IAS_13, Burp = IAS_14,
Muscles = IAS_15, Bruise = IAS_16, Pain = IAS_17, Blood_Sugar = IAS_18,
Affective_touch = IAS_19, Tickle = IAS_20, Itch = IAS_21,
PHQ4_Depression= PHQ2_sum,
PHQ4_Anxiety = GAD2_sum
) df7a <- haven::read_sav("../data/Brand2023/Data_Giessen.sav") |>
select(-COUNTRY_OTHER) |>
mutate_all(as.numeric) |>
mutate(Gender = as.character(ifelse(SEX == 1, "Male", ifelse(SEX == 2, "Female", "Other")))) |>
rename(
Age=AGE, Heart = IAS_01, Hungry = IAS_02, Breathing = IAS_03, Thirsty = IAS_04,
Urinate = IAS_05, Defecate = IAS_06, Taste = IAS_07, Vomit = IAS_08, Sneeze = IAS_09,
Cough = IAS_10, Temp = IAS_11, Sex_arousal = IAS_12, Wind = IAS_13, Burp = IAS_14,
Muscles = IAS_15, Bruise = IAS_16, Pain = IAS_17, Blood_Sugar = IAS_18,
Affective_touch = IAS_19, Tickle = IAS_20, Itch = IAS_21,
MAIA_Noticing = MAIA_NOTICING, MAIA_NotDistracting = MAIA_NON_DISTRACTING,
MAIA_NotWorrying = MAIA_NOT_WORRYING, MAIA_AttentionReg = mAIA_ATTENTION_REGULATION,
MAIA_SelfReg = MAIA_SELF_REGULATION, MAIA_BodyListening = MAIA_BODY_LISTENING,
MAIA_Trusting = MAIA_TRUSTING, MAIA_EmoAwareness = MAIA_EMOTIONAL_AWARENESS,
STAIT_Sum = STAI_T_Sumscore
) |>
mutate(across(starts_with("MAIA_"), \(x) x - 1),
across(starts_with("NEOFFI_"), \(x) datawizard::rescale(x, to = c(0, 1)))) # Original scale 1-6
df7a$STAIT_Mean <- df7a$STAIT_Sum/20
df7a$Neuroticism <- rowMeans(select(df7a,NEOFFI_01:NEOFFI_12))
df7b <- haven::read_sav("../data/Brand2023/Data_Mainz.sav") |>
select(-COUNTRY_OTHER, -EDUCATION_OTHER, -Sample) |>
mutate_all(as.numeric) |>
mutate(Gender = as.character(ifelse(GENDER == 2, "Male", ifelse(GENDER == 1, "Female", "Other")))) |>
rename(
Age=AGE, Heart = IAS_01, Hungry = IAS_02, Breathing = IAS_03, Thirsty = IAS_04,
Urinate = IAS_05, Defecate = IAS_06, Taste = IAS_07, Vomit = IAS_08, Sneeze = IAS_09,
Cough = IAS_10, Temp = IAS_11, Sex_arousal = IAS_12, Wind = IAS_13, Burp = IAS_14,
Muscles = IAS_15, Bruise = IAS_16, Pain = IAS_17, Blood_Sugar = IAS_18,
Affective_touch = IAS_19, Tickle = IAS_20, Itch = IAS_21,
MAIA_Noticing = maiaNotice, MAIA_NotDistracting = maiaNonDistracting,
MAIA_NotWorrying = maiaNotWorrying, MAIA_AttentionReg = maiaAttentionRegulation,
MAIA_SelfReg = maiaSelfRegulation, MAIA_BodyListening = maiaBodyListening,
MAIA_Trusting = maiaTrusting, MAIA_EmoAwareness = maiaEmotionalAwareness) |>
mutate(across(starts_with("MAIA_"), \(x) datawizard::rescale(x, to = c(0, 5), range = c(1, 5))),# Original scale 1-5
across(starts_with("PHQ15_"), \(x) datawizard::rescale(x, to = c(0,2), range = c(1,3))),
across(PHQ9_01:PHQ9_09, \(x) datawizard::rescale(x, to = c(0,3), range =c(1,4))))
df7b$BPQ_BodyAwareness <- rowMeans(select(df7b, BPQ_01:BPQ_12), na.rm = TRUE)
df7b$PHQ15_Sum <-rowMeans(select(df7b,PHQ15_01:PHQ15_15recode), na.rm = TRUE)
df7b$PHQ9_Sum <- rowMeans(select(df7b, PHQ9_01:PHQ9_09), na.rm = TRUE)
df7b$STAIT_Mean <- df7b$STAIT_Sum/20
df7c <- haven::read_sav("../data/Brand2023/Data_PotVie.sav") |>
select(-ID) |>
mutate_all(as.numeric) |>
mutate(Gender = as.character(ifelse(GENDER == 2, "Male", ifelse(GENDER == 1, "Female", "Other")))) |>
rename(
Age=AGE, Heart = IA02_01, Hungry = IA02_02, Breathing = IA02_03, Thirsty = IA02_04,
Urinate = IA02_05, Defecate = IA02_06, Taste = IA02_07, Vomit = IA02_08, Sneeze = IA02_09,
Cough = IA02_10, Temp = IA02_11, Sex_arousal = IA02_12, Wind = IA02_13, Burp = IA02_14,
Muscles = IA02_15, Bruise = IA02_16, Pain = IA02_17, Blood_Sugar = IA02_18,
Affective_touch = IA02_19, Tickle = IA02_20, Itch = IA02_21,
MAIA_Noticing = MAIA_noticing, MAIA_NotDistracting = MAIA_notdis,
MAIA_NotWorrying = MAIA_notwor, MAIA_AttentionReg = MAIA_attreg,
MAIA_SelfReg = MAIA_selfreg, MAIA_BodyListening = MAIA_bodlis,
MAIA_Trusting = MAIA_trust, MAIA_EmoAwareness = MAIA_emoaw,
ICQ = ICQ_sum,
STAIT_Sum = STAIT_sum,
TAS_DIF = TAS_ident, TAS_EOT = TAS_ext, TAS_DDF = TAS_des) |>
mutate(across(starts_with("MAIA_"), \(x) datawizard::rescale(x, to = c(0, 5), range = c(0, 6)))) # Original scale 0-6
df7c$BPQ_BodyAwareness <- df7c$BPQ_sum / 26
df7c$STAIT_Mean <- df7c$STAIT_Sum/20df8a <- haven::read_sav("../data/Lin2023/Study 1 & 3.sav") |>
select(-sex) |>
mutate_all(as.numeric) |>
mutate(Gender = as.character(ifelse(sex_dummy == 1, "Male", ifelse(sex_dummy == 0, "Female", "Other")))) |>
rename(
Age = age,
Heart = Heart, Hungry = HUNGR, Breathing = BREAT, Thirsty = Thirs,
Urinate = URINA, Defecate = Defec, Taste = TASTE, Vomit = VOMIT, Sneeze = Sneez,
Cough = COUGH, Temp = TEMPE, Sex_arousal = SEXAR, Wind = WIND, Burp = Burp,
Muscles = MUSCL, Bruise = Bruis, Pain = PAIN, Blood_Sugar = BloSu,
Affective_touch = Touch, Itch = ITCH
)
df8a$BPQ_BodyAwareness <- rowMeans(select(df8a,BPQ1:BPQ12), na.rm = FALSE)
df8b <- haven::read_sav("../data/Lin2023/Study 2.sav") |>
mutate(Gender = as.character(ifelse(Sex == "男", "Male", ifelse(Sex == "女", "Female", "Other")))) |>
rename(
Heart = Heart, Hungry = HUNGR, Breathing = BREAT, Thirsty = Thirs,
Urinate = URINA, Defecate = Defec, Taste = TASTE, Vomit = VOMIT, Sneeze = Sneez,
Cough = COUGH, Temp = TEMPE, Sex_arousal = SEXAR, Wind = WIND, Burp = Burp,
Muscles = MUSCL, Bruise = Bruis, Pain = PAIN, Blood_Sugar = BloSu,
Affective_touch = Touch, Itch = ITCH,
PHQ15_Sum = PHQ15_sum, PHQ9_sum = PHQ9_sum)
df8b$PHQ9_Sum <- df8b$PHQ9_sum / 9
df8b$PHQ15_Sum <- df8b$PHQ15_Sum/ 15df9 <- read.csv("../data/VonMohr2023/DataSet_study3.csv")
df9 <- df9[complete.cases(select(df9, starts_with("IAS_"))), ]
df9 <- df9 |>
mutate(Gender = as.character(ifelse(GENDER == "Man", "Male", ifelse(GENDER == "Woman", "Female", "Other")))) |>
rename(
Age=AGE, Heart = IAS_1, Hungry = IAS_2, Breathing = IAS_3, Thirsty = IAS_4,
Urinate = IAS_5, Defecate = IAS_6, Taste = IAS_7, Vomit = IAS_8, Sneeze = IAS_9,
Cough = IAS_10, Temp = IAS_11, Sex_arousal = IAS_12, Wind = IAS_13, Burp = IAS_14,
Muscles = IAS_15, Bruise = IAS_16, Pain = IAS_17, Blood_Sugar = IAS_18,
Affective_touch = IAS_19, Tickle = IAS_20, Itch = IAS_21
) |>
mutate(across(starts_with("BFI_"), \(x) datawizard::rescale(x, to = c(0, 1)))) |>
mutate(
Neuroticism = (BFI_5 + BFI_10 + BFI_15)/3,
Agreeableness = (BFI_3 + BFI_6 + BFI_13)/3,
Extraversion = (BFI_2 + BFI_8 + BFI_12)/3,
Conscientiousness = (BFI_1 + BFI_7 + BFI_11)/3,
Openness = (BFI_4 + BFI_9 + BFI_14)/3)df10 <- read.csv("https://raw.githubusercontent.com/RealityBending/IllusionGameReliability/main/data/preprocessed_questionnaires.csv") |>
rename(
Gender = Sex, Heart = Item_IAS_Interoception_1, Hungry = Item_IAS_Interoception_2,
Breathing = Item_IAS_Interoception_3, Thirsty = Item_IAS_Interoception_4,
Temp = Item_IAS_Interoception_5, Sex_arousal = Item_IAS_Interoception_6,
Urinate = Item_IAS_Elimination_1, Defecate = Item_IAS_Elimination_2,
Vomit = Item_IAS_Elimination_3, Wind = Item_IAS_Expulsion_1,
Burp = Item_IAS_Expulsion_2, Sneeze = Item_IAS_Expulsion_3,
Muscles = Item_IAS_Nociception_1, Bruise = Item_IAS_Nociception_2,
Pain = Item_IAS_Nociception_3, Affective_touch = Item_IAS_Skin_1,
Tickle = Item_IAS_Skin_2, Itch = Item_IAS_Skin_3,
MAIA_EmoAwareness = MAIA_EmotionalAwareness, MAIA_AttentionReg = MAIA_AttentionRegulation,
MAIA_SelfReg = MAIA_SelfRegulation,
) |>
filter(!is.na(Urinate)) |>
datawizard::rescale(select=ias_vars, to = c(1, 5), range = c(0, 1)) |>
mutate(across(starts_with("MAIA_"), \(x) datawizard::rescale(x, to = c(0, 5))),
across(starts_with("PI_"), \(x) datawizard::rescale(x, to = c(0, 1))),
across(starts_with("Item_PHQ4_Depression_"), \(x) datawizard::rescale(x, to = c(1,4))),
across(starts_with("Item_PHQ4_Anxiety_"), \(x) datawizard::rescale(x, to = c(1,4))),
across(starts_with("IPIP6_"), \(x) datawizard::rescale(x, to = c(0,1))),
PHQ4_Depression = Item_PHQ4_Depression_3 + Item_PHQ4_Depression_4,
PHQ4_Anxiety = Item_PHQ4_Anxiety_1 + Item_PHQ4_Anxiety_2) |>
rename(
Agreeableness = IPIP6_Agreeableness,
Conscientiousness = IPIP6_Conscientiousness,
Extraversion = IPIP6_Extraversion,
HonestyHumility = IPIP6_HonestyHumility,
Neuroticism = IPIP6_Neuroticism,
Openness = IPIP6_Openness
)df11 <- read.csv("https://raw.githubusercontent.com/DominiqueMakowski/PHQ4R/main/study2/data/data.csv") |>
rename(
Heart = IAS_1, Hungry = IAS_2, Breathing = IAS_3, Thirsty = IAS_4,
Urinate = IAS_5, Defecate = IAS_6, Taste = IAS_7, Vomit = IAS_8, Sneeze = IAS_9,
Cough = IAS_10, Temp = IAS_11, Sex_arousal = IAS_12, Wind = IAS_13, Burp = IAS_14,
Muscles = IAS_15, Bruise = IAS_16, Pain = IAS_17, Blood_Sugar = IAS_18,
Affective_touch = IAS_19, Tickle = IAS_20, Itch = IAS_21,
typeSample = Sample,
BDI_Sum = BDI2_Total) |>
mutate(
MAIA2_NotDistracting_1_R = 1 - MAIA2_NotDistracting_1_R,
MAIA2_NotDistracting_2_R = 1 - MAIA2_NotDistracting_2_R,
MAIA2_NotDistracting_3_R = 1 - MAIA2_NotDistracting_3_R,
MAIA2_NotDistracting_4_R = 1 - MAIA2_NotDistracting_4_R,
MAIA2_NotDistracting_5_R = 1 - MAIA2_NotDistracting_5_R,
MAIA2_NotDistracting_6_R = 1 - MAIA2_NotDistracting_6_R,
MAIA2_NotWorrying_1_R = 1 - MAIA2_NotWorrying_1_R,
MAIA2_NotWorrying_2_R = 1 - MAIA2_NotWorrying_2_R,
MAIA2_NotWorrying_5_R = 1 - MAIA2_NotWorrying_5_R) |>
mutate(across(starts_with("MAIA2_"), \(x) datawizard::rescale(x, to = c(0, 5))),
across(starts_with("PHQ4_Depression_"), \(x) datawizard::rescale(x, to = c(1,4))),
across(starts_with("PHQ4_Anxiety_"), \(x) datawizard::rescale(x, to = c(1,4))),
across(starts_with("STAI5_"), \(x) datawizard::rescale(x, to = c(1, 4)))) |>
mutate(PHQ4_Depression = PHQ4_Depression_3 + PHQ4_Depression_4,
PHQ4_Anxiety = PHQ4_Anxiety_1 + PHQ4_Anxiety_2,
STAIT_Mean = (STAI5_1 + STAI5_2 + STAI5_3 + STAI5_4 + STAI5_4 + STAI5_5)/5) |>
datawizard::rescale(select=ias_vars, to = c(1, 5), range = c(0, 1))
# Compute subscales
df11$MAIA_AttentionReg <- rowMeans(select(df11 , starts_with("MAIA2_Attention")))
df11$MAIA_EmoAwareness <- rowMeans(select(df11 , starts_with("MAIA2_Emotional")))
df11$MAIA_Noticing <- rowMeans(select(df11 , starts_with("MAIA2_Noticing")))
df11$MAIA_BodyListening <- rowMeans(select(df11 , starts_with("MAIA2_BodyListening")))
df11$MAIA_NotDistracting<- rowMeans(select(df11 , starts_with("MAIA2_NotDistracting")))
df11$MAIA_NotWorrying<- rowMeans(select(df11 , starts_with("MAIA2_NotWorrying")))
df11$MAIA_SelfReg<- rowMeans(select(df11 , starts_with("MAIA2_SelfRegulation")))
df11$MAIA_Trusting<- rowMeans(select(df11 , starts_with("MAIA2_Trusting")))df12 <- read.csv("https://raw.githubusercontent.com/RealityBending/InteroceptionPrimals/refs/heads/main/data/data_participants.csv") |>
select("Participant" = "participant_id", "Gender", "Age", "Ethnicity", starts_with(c("IAS","MAIA","PI"))) |>
rename(
Heart = IAS_1, Hungry = IAS_2, Breathing = IAS_3, Thirsty = IAS_4,
Urinate = IAS_5, Defecate = IAS_6, Taste = IAS_7, Vomit = IAS_8, Sneeze = IAS_9,
Cough = IAS_10, Temp = IAS_11, Sex_arousal = IAS_12, Wind = IAS_13, Burp = IAS_14,
Muscles = IAS_15, Bruise = IAS_16, Pain = IAS_17, Blood_Sugar = IAS_18,
Affective_touch = IAS_19, Tickle = IAS_20, Itch = IAS_21,
MAIA_EmoAwareness = MAIA_EmotionalAwareness,
MAIA_AttentionReg = MAIA_AttentionRegulation,
MAIA_SelfReg = MAIA_SelfRegulation
) |>
filter(!is.na(Heart)) |> # participant is outlier and had total IAS scores below 0.4
datawizard::rescale(select=ias_vars, to = c(1, 5), range = c(0, 1)) |>
mutate(across(starts_with("MAIA_"), \(x) datawizard::rescale(x, to = c(0, 5)))) df13 <- read.csv("https://raw.githubusercontent.com/RealityBending/InteroceptionScale/refs/heads/main/study2/data/rawdata_participants.csv") |>
select("Participant", "Gender", "Age", "Ethnicity", starts_with(c("IAS","MAIA","PI", "PHQ4"))) |>
rename(
Heart = IAS_1, Hungry = IAS_2, Breathing = IAS_3, Thirsty = IAS_4,
Urinate = IAS_5, Defecate = IAS_6, Taste = IAS_7, Vomit = IAS_8, Sneeze = IAS_9,
Cough = IAS_10, Temp = IAS_11, Sex_arousal = IAS_12, Wind = IAS_13, Burp = IAS_14,
Muscles = IAS_15, Bruise = IAS_16, Pain = IAS_17, Blood_Sugar = IAS_18,
Affective_touch = IAS_19, Tickle = IAS_20, Itch = IAS_21
) |>
filter(Participant %in% read.csv("https://raw.githubusercontent.com/RealityBending/InteroceptionScale/refs/heads/main/study2/data/data_participants.csv")$Participant) |>
mutate(
MAIA_NotDistracting_1_R = 1 - MAIA_NotDistracting_1_R,
MAIA_NotDistracting_2_R = 1 - MAIA_NotDistracting_2_R,
MAIA_NotDistracting_3_R = 1 - MAIA_NotDistracting_3_R,
MAIA_NotDistracting_4_R = 1 - MAIA_NotDistracting_4_R,
MAIA_NotDistracting_5_R = 1 - MAIA_NotDistracting_5_R,
MAIA_NotDistracting_6_R = 1 - MAIA_NotDistracting_6_R,
MAIA_NotWorrying_1_R = 1 - MAIA_NotWorrying_1_R,
MAIA_NotWorrying_2_R = 1 - MAIA_NotWorrying_2_R,
MAIA_NotWorrying_5_R = 1 - MAIA_NotWorrying_5_R) |>
mutate(across(starts_with("MAIA_"), \(x) datawizard::rescale(x, to = c(0, 5))),
across(starts_with("PI18_"), \(x) datawizard::rescale(x, to = c(0,1)))) |>
mutate(
PI_Alive = (PI18_GA_2 + PI18_GA_14 + (1-PI18_A_12_R) + PI18_A_16 + PI18_A_4)/5,
PI_Good = ((1-PI18_GE_5_R) + (1-PI18_GS_13_R) + PI18_GA_14 + PI18_GE_8 + PI18_GA_2 + PI18_GE_1 + (1-PI18_GE_9_R) + (1-PI18_GS_10_R) + (1-PI18_GE_6_R) + PI18_GS_15 + PI18_GS_17 + (1-PI18_GS_11_R) + PI18_GS_3 + PI18_GE_18 + PI18_GE_7)/ 15,
PI_Safe = ((1-PI18_GS_13_R) + (1-PI18_GS_10_R) + PI18_GS_15 + PI18_GS_17 + (1-PI18_GS_11_R) + PI18_GS_3)/ 6,
PI_Enticing = ((1-PI18_GE_5_R) + PI18_GE_8 + PI18_GE_1 +(1-PI18_GE_9_R) + (1-PI18_GE_6_R) + PI18_GE_18 + PI18_GE_7)/7)
# Compute subscales
df13$MAIA_AttentionReg <- rowMeans(select(df13 , starts_with("MAIA_Attention")))
df13$MAIA_EmoAwareness <- rowMeans(select(df13 , starts_with("MAIA_Emotional")))
df13$MAIA_Noticing <- rowMeans(select(df13 , starts_with("MAIA_Noticing")))
df13$MAIA_BodyListening <- rowMeans(select(df13 , starts_with("MAIA_BodyListening")))
df13$MAIA_NotDistracting<- rowMeans(select(df13 , starts_with("MAIA_NotDistracting")))
df13$MAIA_NotWorrying<- rowMeans(select(df13 , starts_with("MAIA_NotWorrying")))
df13$MAIA_SelfReg<- rowMeans(select(df13 , starts_with("MAIA_SelfRegulation")))
df13$MAIA_Trusting<- rowMeans(select(df13 , starts_with("MAIA_Trusting")))
df13$PHQ4_Depression <- (df13$PHQ4_Depression_4 + df13$PHQ4_Depression_3)/2
df13$PHQ4_Anxiety <- (df13$PHQ4_Anxiety_2 + df13$PHQ4_Anxiety_1)/2# pi_vars <- c("PI_Enticing", "PI_Alive", "PI_Safe", "PI_Good", "PI_Changing",
# "PI_Hierarchical", "PI_Understandable", "PI_AboutMe", "PI_Abundant",
# "PI_Acceptable", "PI_Beautiful", "PI_Cooperative", "PI_Funny",
# "PI_Harmless", "PI_Improvable", "PI_Intentional", "PI_Interconnected",
# "PI_Interesting", "PI_Just", "PI_Meaningful", "PI_NeedsMe",
# "PI_Pleasurable", "PI_Progressing", "PI_Regenerative", "PI_Stable",
# "PI_WorthExploring")
#
#
# correlation::correlation(
# df12 [, "IAS_Total", drop = FALSE],
# data2 = select(df12 , all_of(pi_vars)), p_adjust = "none"
# ) |>
# # Formatting correlation results
# mutate(val = paste0(insight::format_value(r), format_p(p, stars_only = TRUE))) |>
#
# # Releveling factors for visualization
# mutate(Parameter2 = fct_relevel(Parameter2, rev(pi_vars)),
# Parameter1 = fct_relevel(Parameter1, "IAS_Total")) |>
#
# # Plotting the correlation matrix
# ggplot(aes(x = Parameter1, y = Parameter2)) +
# geom_tile(aes(fill = r), color = "white") +
# geom_text(aes(label = val), size = 3) +
# labs(title = "Correlation Matrix") +
# scale_fill_gradient2(low = "blue", high = "red", mid = "white", limit = c(-1, 1), midpoint = 0, guide = guide_colourbar(ticks = FALSE)) +
# theme_minimal() +
# theme(
# legend.title = element_blank(),
# axis.title.x = element_blank(),
# axis.title.y = element_blank(),
# axis.text.x = element_text(hjust = 1)
# )df14 <- haven::read_sav("../data/Giulia/Interoceptive Attention ESM.sav") |>
select(AGE, GENDER, contains("IAS_ACC"), -IAS_ACC_20, starts_with(c("MAIA", "TAS20", "DEP", "ANX")), -ANX_21, -TAS20_21) |> #anx_21 , tas20_21 are attention checks
mutate_all(as.numeric) |>
rename(
Age = AGE, Gender = GENDER,
Heart = IAS_ACC_1, Hungry = IAS_ACC_2, Breathing = IAS_ACC_3, Thirsty = IAS_ACC_4,
Urinate = IAS_ACC_5, Defecate = IAS_ACC_6, Taste = IAS_ACC_7, Vomit = IAS_ACC_8, Sneeze = IAS_ACC_9,
Cough = IAS_ACC_10, Temp = IAS_ACC_11, Sex_arousal = IAS_ACC_12, Wind = IAS_ACC_13, Burp = IAS_ACC_14,
Muscles = IAS_ACC_15, Bruise = IAS_ACC_16, Pain = IAS_ACC_17, Blood_Sugar = IAS_ACC_18,
Affective_touch = IAS_ACC_19, Tickle =IAS_ACC_21, Itch = IAS_ACC_22,
MAIA_EmoAwareness =MAIA_Emotional, MAIA_SelfReg = MAIA_SelfRegulation, MAIA_AttentionReg = MAIA_Attention,
STAIT_Sum = ANX
) |>
mutate(Gender = case_when(
Gender == 1 ~ "Male",
Gender == 2 ~ "Female",
Gender %in% c(3, 5) ~ "Other",
Gender == 4 ~ "NA"),
TAS_DIF = TAS20_1 + TAS20_3 + TAS20_6 + TAS20_7 + TAS20_9 + TAS20_13 + TAS20_14,
TAS_DDF = TAS20_2 + (6-TAS20_4) + TAS20_11 + TAS20_12 + TAS20_17,
TAS_EOT = (6-TAS20_5) + TAS20_8 + (6-TAS20_10) + TAS20_15 + TAS20_16 + (6-TAS20_18) + (6-TAS20_19) + TAS20_20)
df14$STAIT_Mean <- df14$STAIT_Sum/20df15 <- haven::read_sav("../data/Giulia/Sleep and Exteroceptive Interoceptive Sensitivity.sav") |> # 165 participants
filter(Total_Attention_Fail == 0) |> # 32 participants removed based on failing one or more checks
select(AGE, GENDER, contains("IAS_ACC"), ) |>
mutate_all(as.numeric) |>
rename( Age = AGE, Gender = GENDER,
Heart = IAS_ACC_1, Hungry = IAS_ACC_2, Breathing = IAS_ACC_3, Thirsty = IAS_ACC_4, Urinate = IAS_ACC_5, Defecate = IAS_ACC_6, Taste = IAS_ACC_7,
Vomit = IAS_ACC_8, Sneeze = IAS_ACC_9, Cough = IAS_ACC_10, Temp = IAS_ACC_11, Sex_arousal = IAS_ACC_12, Wind = IAS_ACC_13, Burp = IAS_ACC_14,
Muscles = IAS_ACC_15, Bruise = IAS_ACC_16, Pain = IAS_ACC_17, Blood_Sugar = IAS_ACC_18, Affective_touch = IAS_ACC_19, Tickle =IAS_ACC_20,
Itch = IAS_ACC_21 ) |>
mutate(Gender = case_when(
Gender == 1 ~ "Male",
Gender == 2 ~"Female",
Gender %in% c(3, 5) ~ "Other",
Gender == 4 ~ "NA", )) |>
na.omit()df16 <- read.csv("../data/Scharte2025/data_IAS_rs.csv") |>
rename(Age= age, Gender = gender_identity, Education = education,
Heart = ias1, Hungry = ias2, Breathing = ias3, Thirsty = ias4, Urinate = ias5,
Defecate=ias6, Taste = ias7,Vomit = ias8, Sneeze = ias9, Cough = ias10,
Temp = ias11, Sex_arousal = ias12, Wind = ias13, Burp = ias14,Muscles = ias15,
Bruise = ias16, Pain = ias17, Blood_Sugar = ias18, Affective_touch = ias19,
Tickle =ias20, Itch = ias21,
) |>
mutate(Gender = case_when(
Gender == 1 ~ "Male",
Gender == 2 ~ "Female",
Gender %in% c(3, 4, 5) ~ "Other",
Gender == 6 ~ "NA"),
Education = case_when(
Education == 6 ~ "Bachelor",
Education == 7 ~ "Master",
Education == 8 ~ "Doctorate",
Education == 2 | Education == 3 ~ "High School", # GCSE + A-levels (16-18 years old)
Education == 4 | Education == 5 ~ "Other", # NVQ and foundation year
Education == 1 ~ "No education"),
MAIA_Noticing = (maia1 + maia2 + maia3 + maia4)/4,
MAIA_NotDistracting = (maia5_rev + maia6_rev + maia7_rev +maia8_rev + maia9_rev + maia10_rev)/6,
MAIA_NotWorrying = (maia11_rev +maia12_rev + maia13)/3,
MAIA_AttentionReg = (maia16 + maia17 + maia18 + maia19 + maia20 + maia21 + maia22)/7,
MAIA_EmoAwareness = (maia23 + maia24 + maia25 + maia26 +maia27)/5,
MAIA_SelfReg = (maia28 + maia29 + maia30 + maia31)/4,
MAIA_BodyListening = (maia32 + maia33 +maia34)/3,
MAIA_Trusting = (maia35 + maia36 + maia37)/3 ,
GAD7_Sum = rowSums(across(starts_with("gad1_")), na.rm = TRUE),
PHQ9_Sum = rowMeans(across(starts_with("phq1_")), na.rm = TRUE)) |>
filter (!Age == 2004.00, !Age == 1999.00, # misreported age
att == 2) # only those that select 'no' the attention checkdf17 <- haven::read_sav("../data/Petzke2024/ETUDE1_main study.sav") |>
mutate_all(as.numeric) |>
select(age_to_be_completed_that_year, Gender, starts_with(c("IA0", "PH01")), PHQ_15_total, DIF_total, DDF_total, EOT_total, PHQ_4_total, BPQ_total, starts_with("BP01_")) |>
rename(Age= age_to_be_completed_that_year,
Heart = IA01_01, Hungry = IA01_02, Breathing = IA01_03, Thirsty = IA01_04, Urinate = IA01_05,
Defecate=IA01_06, Taste = IA01_07,Vomit = IA01_08, Sneeze = IA01_09, Cough = IA01_10,
Temp = IA01_11, Sex_arousal = IA01_12, Wind = IA01_13, Burp = IA01_14,Muscles = IA01_15,
Bruise = IA01_16, Pain = IA01_17, Blood_Sugar = IA01_18, Affective_touch = IA01_19,
Tickle =IA01_20, Itch = IA01_21,
TAS_DIF = DIF_total, TAS_DDF = DDF_total, TAS_EOT = EOT_total,
PHQ15_Sum = PHQ_15_total,
PHQ4_Sum = PHQ_4_total,
BPQ_BodyAwareness = BPQ_total,
) |>
mutate(Gender = case_when(
Gender == 1 ~ "Male",
Gender == 2 ~ "Female",
Gender == 3 ~ "Other"))|>
mutate(
Age_code = zap_labels(Age), # remove SPSS labels, get raw numeric codes
Age = case_when(
Age_code == -9 ~ NA_real_,
TRUE ~ 2024 - (2000 + (21 - Age_code)) # compute actual age
))Warning: There were 6 warnings in `mutate()`.
The first warning was:
ℹ In argument: `id = .Primitive("as.double")(id)`.
Caused by warning:
! NAs introduced by coercion
ℹ Run `dplyr::last_dplyr_warnings()` to see the 5 remaining warnings.# df17$BPQ_BodyAwarenessManual <- rowMeans(df17[, grep("^BP01_", names(df17))], na.rm = TRUE)
#Ranges
df17$PHQ15_Sum <- df17$PHQ15_Sum/15
df17$BPQ_BodyAwareness <- df17$BPQ_BodyAwareness/12
df17$PHQ4_Depression <- df17$PH01_01 + df17$PH01_02
df17$PHQ4_Anxiety <- df17$PH01_03 + df17$PH01_04# Save all
data <- list(df1a=df1a, df1b=df1b, df2=df2, df3=df3, df4=df4, df5=df5, df6=df6, df7a=df7a, df7b=df7b, df7c=df7c, df8a=df8a, df8b=df8b, df9=df9, df10=df10, df11 =df11 , df12 =df12 , df13 = df13 , df14 =df14 , df15 =df15, df16 = df16, df17 = df17)
save(data, file = "../data/data.RData")Total N = 33526 .
desc <- function(df){
summary <- df |> summarise(
n = n(),
mean = mean(Age, na.rm = TRUE),
sd = sd(Age, na.rm = TRUE),
min = min(Age, na.rm = TRUE),
max = max(Age, na.rm = TRUE),
female = sum(Gender == "Female", na.rm = TRUE)
)
return(summary)
}
desc_df1a <- desc(df1a)
desc_df1b <- desc(df1b)
desc_df2 <- desc(df2)
desc_df3 <- desc(df3)
#desc_df4 <- desc(df4) # no demographic data available
desc_df5 <- desc(df5_attention)
desc_df6 <- desc(df6)
desc_df7a <- desc(df7a)
desc_df7b <- desc(df7b)
desc_df7c <- desc(df7c)
desc_df8a <- desc(df8a)
desc_df8b <- desc(df8b)
desc_df9 <- desc(df9)
desc_df10 <- desc(df10)
desc_df11 <- desc(df11)
desc_df12 <- desc(df12)
desc_df13 <- desc(df13)
desc_df14 <- desc(df14)
desc_df15 <- desc(df15)
desc_df16 <- desc(df16)
desc_df17 <- desc(df17)
desc_total <- rbind(desc_df1a, desc_df1b, desc_df2, desc_df3, desc_df5, desc_df6, desc_df7a, desc_df7b, desc_df7c, desc_df8a, desc_df8b, desc_df9, desc_df10, desc_df11 , desc_df12 , desc_df13 , desc_df14 , desc_df15, desc_df16, desc_df17)
## calculate weighted mean
total_mean <- sum(desc_total$mean * desc_total$n) / sum(desc_total$n)
total_sd <- sum(desc_total$sd * desc_total$n) / sum(desc_total$n)
perce_female <- sum(desc_total$female)/sum(desc_total$n) * 100library(gt)
# APA style ####
gt_apastyle <- function(gt_table, font.size=12) {
gt_table |>
gt::opt_table_lines(extent = "none") %>%
gt::tab_options(
heading.border.bottom.width = 2,
heading.border.bottom.color = "black",
heading.border.bottom.style = "solid",
table.border.top.color = "black",
table.border.top.style = "solid",
table.border.top.width = 2,
table_body.hlines.color = "white",
table_body.border.top.color = "black",
table_body.border.top.style = "solid",
table_body.border.top.width = 2,
heading.title.font.size = font.size,
table.font.size = font.size,
heading.subtitle.font.size = font.size,
table_body.border.bottom.color = "black",
table_body.border.bottom.width = 2,
table_body.border.bottom.style = "solid",
column_labels.border.bottom.color = "black",
column_labels.border.bottom.style = "solid",
column_labels.border.bottom.width = 1,
latex.use_longtable = FALSE
) |>
gt::opt_table_font(font = "times")
}
make_age <- function(age) {
age <- as.numeric(age)
mean(age, na.rm = TRUE) |>
insight::format_value(digits=1) |>
paste0(" ± ",
insight::format_value(sd(age, na.rm = TRUE), digits=1))
}
# Create the dataframe
table <- data.frame(
Sample = c('', 'Sample 1a', 'Sample 1b', 'Sample 2', 'Sample 3', 'Sample 4', 'Sample 5', 'Sample 6', '', 'Sample 7a', 'Sample 7b', 'Sample 7c', '', 'Sample 8a', 'Sample 8b', 'Sample 9', 'Sample 10', 'Sample 11', 'Sample 12', 'Sample 13', 'Sample 14', 'Sample 15', 'Sample 16', 'Sample 17', 'Sample 18'),
Reference = c('Murphy et al., (2020)', '', '', 'Gaggero et al., (2021)', 'Campos et al., (2022)', 'Todd et al., (2022)', 'Arslanova et al., (2022)', 'Brand et al., (2022)', 'Brand et al., (2023)', '', '', '', 'Lin et al., (2023)', '', '', 'VonMohr et al., (2023)', 'Makowski et al., (2023a)', 'Makowski et al., (2023b)', 'Makowski et al., (2023c)', 'Makowski et al., (2024)', 'Poerio et al., (2024)', 'Poerio et al., unpublished', 'Arjona et al., unpublished', 'Petzke et al., (2024)', 'Total'),
Language = c('', 'English', 'English' , 'English and Italian', 'Portuguese', 'English', 'English', 'German', '', 'German', 'German', 'German', '', 'Chinese', 'Chinese', 'English', 'English', 'English', 'English', 'English', 'English', 'English','English', 'German', ''),
N = c('', nrow(df1a), nrow(df1b), nrow(df2), nrow(df3), nrow(df4), nrow(df5_attention), nrow(df6),'', nrow(df7a), nrow(df7b), 802,'', nrow(df8a), nrow(df8b), nrow(df9), nrow(df10), nrow(df11), nrow(df12), nrow(df13), nrow(df14), nrow(df15), nrow(df16), nrow(df17), 33526),
'Difference' = c('','', '', '', '', '', '', '', '', '', '', '', '', 'Collapsed "Itch" and "Tickling"', 'Collapsed "Itch" and "Tickling"', '', 'Analog scales. No Temperature, Blood sugar and Cough items', 'Analog scales', 'Analog scales', '', '', '', '', '', ''),
Age = c('', make_age(df1a$Age), make_age(df1b$Age), make_age(df2$Age), make_age(df3$Age), "48.6.6 ± 14.1*", make_age(df5_attention$Age), make_age(df6$Age),'', make_age(df7a$Age), make_age(df7b$Age), make_age(df7c$Age),'', make_age(df8a$Age), make_age(df8b$Age), make_age(df9$Age), make_age(df10$Age), make_age(df11$Age), make_age(df12$Age), make_age(df13$Age), make_age(df14$Age), make_age(df15$Age), make_age(df16$Age), make_age(df17$Age), '47.96 ± 13.1'),
Range = c('', '18-69', '18-91', '18-58', '18-72', '18-92*', '18-73', '18-78', '', '18-79', '16-81', '18-72','', '16-60', '20-60', '18-93', '18-73', '17-76','18-50', '17, 87', '18-57', '18-60', '18-79', '22-69', '17-93'),
Female_Percentage = c('', '69.4%', '70.1%', '60.3%', '59.6%', '50%*', '46.8%', '78.7%', '', '79.5%', '77.7%', '68.9%', '', '57.0%', '56.2%', '73.2%', '50.3%', '53.0%', '76%', '57.3%', '74.8%', '75.9%', '67.7%', '68.5%', '71.3%'),
Availability= c('osf.io/3m5nh', '', '', 'osf.io/5x9sg', 'osf.io/j6ef3', 'osf.io/ms354', 'osf.io/mp3cy', 'osf.io/xwz6g', 'osf.io/3f2h6', '', '', '','osf.io/3eztd','', '', 'osf.io/7p9u5', 'github.com/RealityBending/IllusionGameReliability', 'github.com/DominiqueMakowski/PHQ4R', 'github.com/RealityBending/InteroceptionPrimals',
'github.com/RealityBending/InteroceptionScale',
'osf.io/49wbv', '','', 'osf.io/seru4', '')
)
table_apa <- table |>
gt() |>
cols_align(align = c("right"), columns = "Age") |>
cols_label(Age = "Age (Mean ± SD)", Female_Percentage = "Female %") |>
tab_footnote("Note.*Information taken from the sample description of relevant paper rather than recomputed.")
gt_apastyle(table_apa, font.size=12)
# gtsave(gt_apastyle(table_apa, font.size=9), "figures/table1.tex")
saveRDS(table, "figures/table1.rds")
# gtsave(table_apa, "figures/table1.png")The items with the differing distribution pattern (with a lower mode around 2/5) are “Affective Touch”, “Blood Sugar” and “Bruise”.
dens1a <- estimate_density(select(df1a, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 1a")
dens1b <- estimate_density(select(df1b, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 1b")
dens2 <- estimate_density(select(df2, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 2")
dens3 <- estimate_density(select(df3, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 3")
dens4 <- estimate_density(select(df4, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 4")
dens5 <- estimate_density(select(df5, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 5")
dens6 <- estimate_density(select(df6, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 6")
dens7a <- estimate_density(select(df7a, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 7a")
dens7b <- estimate_density(select(df7b, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 7b")
dens7c <- estimate_density(select(df7c, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 7c")
dens8a <- estimate_density(select(df8a, all_of(setdiff(ias_vars, "Tickle"))), method = "kernSmooth") |>
mutate(Sample = "Sample 8a")
dens8b <- estimate_density(select(df8b, all_of(setdiff(ias_vars, "Tickle"))), method = "kernSmooth") |>
mutate(Sample = "Sample 8b")
dens9 <- estimate_density(select(df9, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 9")
dens10 <- estimate_density(select(df10, all_of(setdiff(ias_vars, c("Taste", "Cough", "Blood_Sugar")))), method = "kernSmooth") |>
mutate(Sample = "Sample 10")
dens11<- estimate_density(select(df11, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 11")
dens12 <- estimate_density(select(df12, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 12")
dens13 <- estimate_density(select(df13, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 13")
dens14 <- estimate_density(select(df14, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 14")
dens15 <- estimate_density(select(df15, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 15")
dens16 <- estimate_density(select(df16, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 16")
dens17 <- estimate_density(select(df17, all_of(ias_vars)), method = "kernSmooth") |>
mutate(Sample = "Sample 17")
p1_data <- rbind(dens1a, dens1b, dens2, dens3, dens4, dens5, dens6, dens7a, dens7b, dens7c, dens8a, dens8b, dens9, dens10, dens11 , dens12, dens13, dens14, dens15, dens16, dens17)
linetype <- setNames(rep("solid", length(ias_vars)), ias_vars)
linetype["Affective touch"] <- "dotted"
linetype["Blood sugar"] <- "dashed"
linetype["Sex arousal"] <- "solid"
linetype["Bruise"] <- "dashed"
# proportion of non-1 and non-5 values
p1_data |>
filter(x > 1 & x < 5) |> # Keep only x values strictly between 1 and 5
summarise(proportion = sum(y) / sum(p1_data$y)) # Normalize by total density proportion
1 0.9996777p1 <- p1_data |>
mutate(Sample = fct_relevel(Sample, "Sample 1a", "Sample 1b", "Sample 2", "Sample 3", "Sample 4", "Sample 5", "Sample 6", "Sample 7a", "Sample 7b", "Sample 7c", "Sample 8a", "Sample 8b", "Sample 9", "Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14", "Sample 15", 'Sample 16', 'Sample 17'),
Parameter = ifelse(Parameter == "Affective_touch", "Affective touch", Parameter),
Parameter = ifelse(Parameter == "Blood_Sugar", "Blood sugar", Parameter),
Parameter = ifelse(Parameter == "Sex_arousal", "Sex arousal", Parameter)) |>
# mutate(Dashed = ifelse(Parameter %in% c("Affective_touch", "Blood_Sugar", "Bruise"), TRUE, FALSE)) |>
ggplot(aes(x = x, y = y, color = Parameter)) +
geom_line(aes(linetype = Parameter), linewidth = 0.5) +
scale_color_material_d() +
scale_linetype_manual(values = linetype) +
labs(x = "Response", y = "Distribution", color = "Item", linetype = "Item", title = "Item Distribution") +
guides(color = guide_legend(ncol = 1)) +
facet_wrap(~Sample, scales = "free_y", nrow = 3) +
theme_minimal() +
theme(axis.text.y = element_blank(),
axis.title.y = element_blank(),
plot.title = element_text(face="bold"))
p1
# ONLY IAS DATA
data1a <- select(df1a, all_of(dens1a$Parameter))
data1b <- select(df1b, all_of(dens1b$Parameter))
data2 <- select(df2, all_of(dens2$Parameter))
data3 <- select(df3, all_of(dens3$Parameter))
data4 <- select(df4, all_of(dens4$Parameter))
data5 <- select(df5, all_of(dens5$Parameter))
data6 <- select(df6, all_of(dens6$Parameter))
data7a <- select(df7a, all_of(dens7a$Parameter))
data7b <- select(df7b, all_of(dens7b$Parameter))
data7c <- select(df7c, all_of(dens7c$Parameter))
data8a <- select(df8a, all_of(dens8a$Parameter))
data8b <- select(df8b, all_of(dens8b$Parameter))
data9 <- select(df9, all_of(dens9$Parameter))
data10 <- select(df10, all_of(dens10$Parameter))
data11 <- select(df11, all_of(dens11$Parameter))
data12 <- select(df12, all_of(dens12$Parameter))
data13 <- select(df13, all_of(dens13$Parameter))
data14 <- select(df14, all_of(dens14$Parameter))
data15 <- select(df15, all_of(dens15$Parameter))
data16 <- select(df16, all_of(dens16$Parameter))
data17 <- select(df17, all_of(dens17$Parameter))
data_all <- rbind(
data1a, data1b, data2, data3, data4, data5, data6, data7a, data7b, data7c,
mutate(data8a, Tickle = NA),
mutate(data8b, Tickle = NA), data9,
mutate(data10, Taste = NA, Cough = NA, Blood_Sugar = NA),
data11, data12 , data13, data14, data15, data16, data17
) An overall positive intercorrelation pattern, with no clear structure emerging.
make_correlation <- function(df) {
correlation::correlation(df, redundant = TRUE) |>
correlation::cor_sort() |>
# correlation::cor_lower() |>
mutate(val = paste0(insight::format_value(r), format_p(p, stars_only = TRUE)),
r = ifelse(Parameter1 == Parameter2, NA, r),
lbl = ifelse(Parameter1 == Parameter2, "", format_value(r, zap_small = TRUE, lead_zero = FALSE)),
lbl = ifelse(p > .05, "", lbl),
Param1 = fct_relevel(str_replace(Parameter1, "_", " "), str_replace(levels(Parameter1), "_", " ")),
Param2 = fct_relevel(str_replace(Parameter2, "_", " "), str_replace(levels(Parameter2), "_", " "))) |>
# mutate(Parameter2 = fct_rev(Parameter2)) |>
ggplot(aes(x = Param1, y = Param2)) +
geom_tile(aes(fill = r), color = "white") +
geom_text(aes(label = lbl), size = 3) +
labs(title = "Correlation Matrix", subtitle = paste0("N = ", nrow(df))) +
scale_fill_gradientn(
colors = c("white", "#FFF9C4", "#FFF59D", "#FFEB3B", "#FFCA28", "#FF9800", "#FF5722",
"#F44336", "#E91E63", "#9C27B0", "#673AB7", "#512DA8", "#311B92"),
na.value = "#263238",
limits = c(0.05, 0.75),
guide = guide_colorbar(ticks.colour = NA)
) +
# scale_fill_metro_c(limit = c(0, 0.75), guide = guide_colourbar(ticks = FALSE)) +
theme_minimal() +
theme(
legend.title = element_blank(),
legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(face = "bold")
)
}
p2 <- make_correlation(data_all)
p2
fig1 <- p1 / p2
ggsave("figures/Figure1.png", fig1, width=10, height=12, dpi=300, bg="white")make_correlation(data1a)
make_correlation(data1b)
make_correlation(data2)
make_correlation(data3)
make_correlation(data6)
make_correlation(data5)
make_correlation(data6)
make_correlation(data7a)
make_correlation(data7b)
make_correlation(data7c)
make_correlation(data8a)
make_correlation(data8b)
make_correlation(data9)
make_correlation(data10)
make_correlation(data11)
make_correlation(data12)
make_correlation(data13)
make_correlation(data14)
make_correlation(data15)
make_correlation(data16)
make_correlation(data17)
Unique Variable Analysis (Christensen, Garrido, & Golino, 2023) uses the weighted topological overlap measure (Nowick et al., 2009) on an estimated network. Values greater than 0.25 are determined to have considerable local dependence (i.e., redundancy) that should be handled (variables with the highest maximum weighted topological overlap to all other variables (other than the one it is redundant with) should be removed).
uva0 <- EGAnet::UVA(data = data_all, cut.off = 0.3)
uva0Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Tickle Itch 0.361
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Wind Burp 0.288
Urinate Defecate 0.265
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Sneeze Cough 0.228
Heart Breathing 0.227
Hungry Thirsty 0.217uva0$keep_remove$keep
[1] "Itch"
$remove
[1] "Tickle"uva1a <- EGAnet::UVA(data = data1a, cut.off = 0.3)
uva1aVariable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Tickle Itch 0.286
Wind Burp 0.275
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Sneeze Cough 0.244
Urinate Defecate 0.241uva1a$keep_removeNULLuva1b <- EGAnet::UVA(data = data1b, cut.off = 0.3)
uva1bVariable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Tickle Itch 0.350
Sneeze Cough 0.317
Wind Burp 0.309
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Urinate Defecate 0.278
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)uva1b$keep_remove$keep
[1] "Cough" "Burp" "Itch"
$remove
[1] "Sneeze" "Wind" "Tickle"uva2 <- EGAnet::UVA(data = data2, cut.off = 0.3)
uva2Variable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Tickle Itch 0.283
Heart Breathing 0.252
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Sneeze Cough 0.230
Urinate Defecate 0.223
Hungry Thirsty 0.200uva2$keep_removeNULLuva3 <- EGAnet::UVA(data = data3, cut.off = 0.3)
uva3Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Tickle Itch 0.445
Sneeze Cough 0.318
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Wind Burp 0.256
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Bruise Blood_Sugar 0.219
Urinate Defecate 0.217
Heart Breathing 0.208uva3$keep_remove$keep
[1] "Sneeze" "Itch"
$remove
[1] "Cough" "Tickle"uva4 <- EGAnet::UVA(data = data6, cut.off = 0.3)
uva4Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Urinate Defecate 0.416
Sneeze Cough 0.332
Wind Burp 0.314
Tickle Itch 0.303
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Hungry Thirsty 0.274
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Heart Breathing 0.237uva4$keep_remove$keep
[1] "Urinate" "Sneeze" "Wind" "Itch"
$remove
[1] "Defecate" "Cough" "Burp" "Tickle" uva5 <- EGAnet::UVA(data = data5, cut.off = 0.3)Warning: Some variables did not belong to a dimension: Blood_Sugar, Affective_touch
Use caution: These variables have been removed from the TEFI calculationuva5Variable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Tickle Itch 0.247
Bruise Itch 0.246
Breathing Vomit 0.207uva5$keep_removeNULLuva6 <- EGAnet::UVA(data = data6, cut.off = 0.3)
uva6Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Urinate Defecate 0.416
Sneeze Cough 0.332
Wind Burp 0.314
Tickle Itch 0.303
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Hungry Thirsty 0.274
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Heart Breathing 0.237uva6$keep_remove$keep
[1] "Urinate" "Sneeze" "Wind" "Itch"
$remove
[1] "Defecate" "Cough" "Burp" "Tickle" uva7a <- EGAnet::UVA(data = data7a, cut.off = 0.3)
uva7aVariable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Sneeze Cough 0.434
Tickle Itch 0.376
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Wind Burp 0.295
Urinate Defecate 0.294
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)uva7a$keep_remove$keep
[1] "Sneeze" "Tickle"
$remove
[1] "Cough" "Itch" uva7b <- EGAnet::UVA(data = data7b, cut.off = 0.3)
uva7bVariable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Urinate Defecate 0.317
Tickle Itch 0.311
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Heart Breathing 0.281
Sneeze Cough 0.261
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Wind Burp 0.237uva7b$keep_remove$keep
[1] "Defecate" "Itch"
$remove
[1] "Urinate" "Tickle" uva7c <- EGAnet::UVA(data = data7c, cut.off = 0.3)
uva7cVariable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Tickle Itch 0.457
Sneeze Cough 0.302
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Wind Burp 0.280
Urinate Defecate 0.254
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Hungry Thirsty 0.242
Vomit Sneeze 0.224
Heart Breathing 0.218uva7c$keep_remove$keep
[1] "Cough" "Itch"
$remove
[1] "Sneeze" "Tickle"uva8a <- EGAnet::UVA(data = data8a, cut.off = 0.3)
uva8aVariable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Urinate Defecate 0.237
Heart Breathing 0.218
Hungry Thirsty 0.213uva8a$keep_removeNULLuva8b <- EGAnet::UVA(data = data8b, cut.off = 0.3)
uva8bVariable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Urinate Defecate 0.277
Heart Breathing 0.267
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Wind Burp 0.206uva8b$keep_removeNULLuva9 <- EGAnet::UVA(data = data9, cut.off = 0.3)
uva9Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Tickle Itch 0.379
Wind Burp 0.373
Urinate Defecate 0.341
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Heart Breathing 0.285
Sneeze Cough 0.278
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Hungry Thirsty 0.24uva9$keep_remove$keep
[1] "Defecate" "Wind" "Itch"
$remove
[1] "Urinate" "Burp" "Tickle" uva10 <- EGAnet::UVA(data = data10, cut.off = 0.3)
uva10Variable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Tickle Itch 0.297
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Heart Breathing 0.209uva10$keep_removeNULLuva11 <- EGAnet::UVA(data = data11 , cut.off = 0.3)
uva11 Variable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)uva11$keep_removeNULLuva12 <- EGAnet::UVA(data = data12, cut.off = 0.3)Warning: Some variables did not belong to a dimension: Cough
Use caution: These variables have been removed from the TEFI calculationuva12 Variable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)uva12$keep_removeNULLuva13 <- EGAnet::UVA(data = data13, cut.off = 0.3)
uva13 Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Heart Breathing 0.395
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Tickle Itch 0.229
Urinate Defecate 0.216
Wind Burp 0.209
Hungry Thirsty 0.204uva13$keep_remove$keep
[1] "Breathing"
$remove
[1] "Heart"uva14 <- EGAnet::UVA(data = data14, cut.off = 0.3)
uva14 Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Urinate Defecate 0.330
Heart Breathing 0.314
Wind Burp 0.302
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Muscles Pain 0.279
Tickle Itch 0.277
Hungry Thirsty 0.255
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Vomit Sneeze 0.201uva14$keep_remove$keep
[1] "Heart" "Defecate" "Burp"
$remove
[1] "Breathing" "Urinate" "Wind" uva15 <- EGAnet::UVA(data = data15, cut.off = 0.3)
uva15 Variable pairs with wTO > 0.30 (large-to-very large redundancy)
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Hungry Breathing 0.252
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Wind Burp 0.214
Temp Muscles 0.202uva15$keep_removeNULLuva16 <- EGAnet::UVA(data = data16, cut.off = 0.3)
uva16 Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Wind Burp 0.36
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Tickle Itch 0.280
Hungry Thirsty 0.261
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Sneeze Cough 0.246
Urinate Defecate 0.234
Bruise Blood_Sugar 0.214uva16$keep_remove$keep
[1] "Wind"
$remove
[1] "Burp"uva17 <- EGAnet::UVA(data = data17, cut.off = 0.3)
uva17 Variable pairs with wTO > 0.30 (large-to-very large redundancy)
node_i node_j wto
Sneeze Cough 0.341
Tickle Itch 0.341
----
Variable pairs with wTO > 0.25 (moderate-to-large redundancy)
node_i node_j wto
Urinate Defecate 0.267
----
Variable pairs with wTO > 0.20 (small-to-moderate redundancy)
node_i node_j wto
Wind Burp 0.237
Hungry Thirsty 0.209uva17$keep_remove$keep
[1] "Cough" "Tickle"
$remove
[1] "Sneeze" "Itch" rez_uva <- c(uva1a$keep_remove$remove,
uva1b$keep_remove$remove,
uva2$keep_remove$remove,
uva3$keep_remove$remove,
uva4$keep_remove$remove,
uva5$keep_remove$remove,
uva6$keep_remove$remove,
uva7a$keep_remove$remove,
uva7b$keep_remove$remove,
uva7c$keep_remove$remove,
uva8a$keep_remove$remove,
uva8b$keep_remove$remove,
uva9$keep_remove$remove,
uva10$keep_remove$remove,
uva11$keep_remove$remove,
uva12$keep_remove$remove,
uva13$keep_remove$remove,
uva14$keep_remove$remove,
uva15$keep_remove$remove,
uva16$keep_remove$remove,
uva17$keep_remove$remove)
sort(table(rez_uva), decreasing = TRUE) / length(rez_uva) rez_uva
Tickle Burp Cough Sneeze Urinate Defecate Itch
0.24137931 0.13793103 0.13793103 0.10344828 0.10344828 0.06896552 0.06896552
Wind Breathing Heart
0.06896552 0.03448276 0.03448276 Discarded the following items: - Tickle: not present in the same dataset and consistently flagged as redundant in UVA analysis.
data_all <- select(data_all, -Tickle)
data1a <- select(data1a, -Tickle)
data1b <- select(data1b, -Tickle)
data2 <- select(data2, -Tickle)
data3 <- select(data3, -Tickle)
data6 <- select(data6, -Tickle)
data5 <- select(data5, -Tickle)
# data6 <- select(data6, -Tickle)
data7a <- select(data7a, -Tickle)
data7b <- select(data7b, -Tickle)
data7c <- select(data7c, -Tickle)
# data8a <- select(data8a, -Tickle)
# data8b <- select(data8b, -Tickle)
data9 <- select(data9, -Tickle)
data10 <- select(data10, -Tickle)
data11 <- select(data11 , -Tickle)
data12 <- select(data12 , -Tickle)
data13 <- select(data13 , -Tickle)
data14 <- select(data14 , -Tickle)
data15 <- select(data15 , -Tickle)
data16 <- select(data16 , -Tickle)
data17 <- select(data17 , -Tickle)colors <- c("Heart" = "#F44336", "Breathing" = "#FF5722",
"Hungry" = "#FF9800", "Thirsty" = "#FFC107",
"Burp" = "#4CAF50", "Wind" = "#009688",
"Urinate" = "#63824C", "Defecate" = "#795548",
"Cough" = "#8BC34A", "Sneeze" = "#CDDC39",
"Bruise" = "#673AB7", "Blood Sugar" = "#3F51B5",
"Muscles" = "#2196F3", "Pain" = "#00BCD4",
"Sex arousal" = "#FF4081", "Temp" = "#9C27B0",
"Vomit" = "#76FF03", "Taste" = "#00E676",
"Affective touch" = "#FF1744", "Itch" = "#D500F9")make_hclust <- function(data) {
rez_pvclust <- pvclust::pvclust(data,
method.hclust = "complete",
method.dist = "correlation",
nboot = 1000, quiet=TRUE, parallel=TRUE)
dendrogram <- as.dist(1 - cor(data, use = "pairwise.complete.obs")) |>
hclust(method = "complete") |>
tidygraph::as_tbl_graph()
# Process Nodes
nodes <- as.list(dendrogram)$nodes |>
mutate(
Size = ifelse(label != "", 10, NA),
Item = str_replace(label, "_", " "),
idx = 1:nrow(as.list(dendrogram)$nodes))
# Central node
nodes[nodes$height == max(nodes$height), c("Item", "Size")] <- data.frame(Item="Central", Size=15)
# Process Edges
edges <- as.list(dendrogram)$edges
edges$linewidth = datawizard::rescale(nodes[edges$from, ]$height, to = c(0.1, 1))
p <- tidygraph::tbl_graph(nodes = nodes, edges = edges) |>
ggraph(layout = "dendrogram", circular = TRUE) +
# geom_edge_diagonal(strength = 0.7, linewidth = 1) +
geom_edge_elbow2(aes(edge_width=linewidth), color="#212121") +
geom_node_point(aes(filter=Item %in% c("Central"), size = Size), color="#212121") +
# geom_node_point(aes(filter=Group %in% c("Visceroception", "Awareness", "Deficit"), color=Group, size = Size)) +
geom_node_text(aes(
label = ifelse(Item != "", Item, NA),
x = x * 1.10,
y = y * 1.10,
filter = label != "",
angle = ifelse(
x >= 0,
asin(y) * 360 / 2 / pi,
360 - asin(y) * 360 / 2 / pi
),
hjust = ifelse(
x >= 0, 0, 1
))) +
geom_node_point(aes(filter = label != "", color=Item, size=Size), alpha = 1) +
# geom_node_text(aes(label=idx)) + # Debug
scale_edge_width_continuous(range=c(1, 3), guide = "none") +
scale_size_continuous(range=c(10, 10), guide = "none") +
scale_color_manual(values = colors,
breaks = names(colors)) +
ggtitle("Hierarchical Clustering Analysis (HCA)", subtitle = "Method = Correlation") +
coord_equal(clip = "off", xlim = c(-1.25, 1.25), ylim = c(-1.25, 1.25)) +
theme_void() +
# guides(color = guide_legend(override.aes = list(size = c(7.5, 5, 5, 3.5, 7.5, 5, 5, 3.5, 3.5, 3.5, 7.5, 5, 5, 3.5)))) +
theme(legend.text = ggtext::element_markdown(),
legend.title = element_blank(),
legend.position = "none",
# plot.title = element_blank(),
# plot.subtitle = element_blank()
plot.title = element_text(face="bold"))
list(pvclust = rez_pvclust, p = p)
}
rez_hclust <- make_hclust(data_all)
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
rez_hclust$p
make_ega <- function(data, cols = c("C1" = "#F44336", "C2" = "#FF9800", "C3" = "#795548", "C4" = "#CDDC39", "C5" = "#2196F3", "C6" = "#009688", "C7" = "#9C27B0")) {
rez <- EGAnet::bootEGA(
data = data,
seed = 123,
model = "glasso",
algorithm = "leiden",
EGA.type = "hierEGA",
type = "resampling",
plot.itemStability = FALSE,
verbose = FALSE,
allow.singleton = TRUE,
loading.method = "original"
)
table <- EGAnet::net.loads(rez$EGA$lower_order)$std |>
as.data.frame() |>
rownames_to_column("Item") |>
gt::gt() |>
gt::tab_header(title = "EGA Loadings") |>
gt::data_color(
columns = -Item,
method = "numeric",
colors = scales::col_numeric(
palette = c("red", "white", "green"),
domain = c(-1, 0, 1)
)) |>
gt::fmt_auto()
nodes <- rez$EGA$lower_order$dim.variables |>
rename(name = items) |>
mutate(dimension = paste0("C", dimension),
size = apply(EGAnet::net.loads(rez$EGA$lower_order)$std, 1, max)[name])
loadings <- rez$EGA$lower_order$network |>
as.data.frame() |>
rownames_to_column("to") |>
pivot_longer(-c(to), names_to = "from", values_to = "Loading") |>
filter(Loading > quantile(Loading, 1/3))
g <- tidygraph::tbl_graph(nodes = nodes, edges = loadings, directed = FALSE) |>
mutate(name = str_replace(name, "_", " "),
name = ifelse(name == "Sex arousal", "Sex", name))
set.seed(1)
layout <- ggraph::create_layout(g, layout = "fr", weights = abs(loadings$Loading))
xrange <- max(layout$x) - min(layout$x)
yrange <- max(layout$y) - min(layout$y)
xmin <- min(layout$x) - xrange * 0.05
xmax <- max(layout$x) + xrange * 0.05
ymin <- min(layout$y) - yrange * 0.05
ymax <- max(layout$y) + yrange * 0.05
p <- layout |>
ggraph() +
geom_edge_bend(aes(edge_width = Loading, edge_alpha = Loading), color = "#212121",
strength = 0.3) +
geom_node_point(aes(size = size, color = dimension), alpha = 0.95) +
geom_node_text(aes(label = name), size = 3, color = "white", fontface = "bold") +
scale_size_continuous(range = c(23, 28)) +
scale_edge_width(range = c(0.3, 4)) +
scale_edge_alpha(range = c(0.1, 0.9)) +
scale_color_manual(values = cols) +
labs(title = "Exploratory Graph Analysis (EGA)", subtitle = "Method = Leiden") +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(face="bold")) +
coord_cartesian(xlim =c(xmin, xmax), ylim = c(ymin, ymax))
list(rez = rez, table = table, p = p)
}
rez_ega <- make_ega(data_all, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Cough"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]]))
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.526 | 0.036 | 0.008 | 0.069 | 0.037 | 0.019 | 0.112 |
| Breathing | 0.526 | 0.189 | 0.044 | 0.18 | 0.058 | 0.002 | 0.002 |
| Hungry | 0.129 | 0.522 | 0.074 | 0.123 | 0 | 0.004 | 0.04 |
| Thirsty | 0.093 | 0.522 | 0.124 | 0.108 | 0.009 | 0.013 | 0.01 |
| Urinate | 0.02 | 0.185 | 0.564 | 0.148 | 0.038 | 0.017 | 0 |
| Defecate | 0.043 | 0.057 | 0.564 | 0.153 | 0.115 | 0.041 | 0 |
| Pain | 0.036 | 0.107 | 0.052 | 0.393 | 0.045 | 0 | 0.131 |
| Temp | 0.026 | 0.091 | 0.088 | 0.363 | 0.125 | 0.03 | 0.006 |
| Muscles | 0.104 | 0.025 | 0.039 | 0.357 | 0.058 | 0.089 | 0.123 |
| Sex_arousal | 0.067 | 0.069 | 0.071 | 0.3 | 0.076 | 0.078 | 0 |
| Affective_touch | 0.036 | 0.001 | 0.035 | 0.279 | 0.05 | 0 | 0.202 |
| Taste | 0.07 | 0.027 | 0.061 | 0.229 | 0.116 | 0.04 | 0.054 |
| Sneeze | 0.032 | 0 | 0.052 | 0.107 | 0.564 | 0.085 | 0.046 |
| Cough | 0.039 | 0.011 | 0.011 | 0.129 | 0.446 | 0.161 | 0.091 |
| Vomit | 0.046 | 0 | 0.094 | 0.185 | 0.293 | 0.043 | 0.045 |
| Wind | 0.008 | 0.01 | 0.057 | 0.118 | 0.101 | 0.571 | 0.058 |
| Burp | 0.019 | 0.011 | 0.003 | 0.098 | 0.19 | 0.571 | 0.107 |
| Bruise | 0.031 | 0 | 0 | 0.18 | 0.052 | 0.08 | 0.444 |
| Blood_Sugar | 0.067 | 0.052 | 0 | 0.073 | 0.022 | 0.025 | 0.385 |
| Itch | 0.019 | 0 | 0 | 0.133 | 0.078 | 0.031 | 0.298 |
rez_ega$p
make_efa <- function(data, n = 3, cols = c("F1" = "#009688", "F2" = "#795548", "F3" = "red")) {
n_fac <- n_factors(data)
rez_efa <- factor_analysis(data, n = n, sort = TRUE, rotation = "oblimin")
# Exctract loadings
loadings <- attributes(rez_efa)$loadings_long |>
select(from = Component, to=Variable, Loading) |>
mutate(Type = "Loading",
to = str_replace(to, "_", " "),
from = str_replace(from, "MR", "F"))
# Reorder factors
fct_order <- c("Pain", "Muscles", "Blood Sugar", "Bruise",
"Affective touch", "Sex arousal", "Temp", "Itch", "Tickle",
"Thirsty", "Hungry", "Breathing", "Heart", "Defecate", "Urinate",
"Sneeze", "Cough", "Wind", "Burp", "Vomit", "Taste")
loadings <- loadings |>
mutate(to = fct_relevel(to, fct_order[fct_order %in% unique(loadings$to)])) |>
arrange(to) |>
mutate(to = as.character(to))
# Get correlations between factors
phi <- attributes(rez_efa)$model$Phi
phi[upper.tri(phi, diag = TRUE)] <- NA
phi <- phi |>
as.data.frame() |>
rownames_to_column("from") %>%
pivot_longer(-from, names_to = "to", values_to = "Loading") |>
filter(!is.na(Loading)) |>
mutate(Type = "Correlation",
from = str_replace(from, "MR", "F"),
to = str_replace(to, "MR", "F"))
# Reverse so that arcs are on the same side
phi[paste(phi$from, phi$to) %in% c("F3 F1"), c("from", "to")] <- data.frame(from = "F1", to = "F3")
phi[paste(phi$from, phi$to) %in% c("F2 F1"), c("from", "to")] <- data.frame(from = "F1", to = "F2")
phi[paste(phi$from, phi$to) %in% c("F3 F2"), c("from", "to")] <- data.frame(from = "F2", to = "F3")
phi[paste(phi$from, phi$to) %in% c("F4 F1"), c("from", "to")] <- data.frame(from = "F1", to = "F4")
phi[paste(phi$from, phi$to) %in% c("F4 F2"), c("from", "to")] <- data.frame(from = "F2", to = "F4")
phi[paste(phi$from, phi$to) %in% c("F4 F3"), c("from", "to")] <- data.frame(from = "F3", to = "F4")
loadings <- rbind(loadings, phi)
# Make layout
factor_names <- unique(loadings$from[loadings$Type == "Loading"])
item_names <- unique(loadings$to[loadings$Type == "Loading"])
nodes <- tibble(name = unique(c(loadings$from, loadings$to))) |>
mutate(type = case_when(
name %in% factor_names ~ "Factor",
name %in% item_names ~ "Item",
TRUE ~ "Other"
))
y_position <- seq(0.8, 0.2, length.out = length(factor_names))
layout <- nodes |>
mutate(x = ifelse(type == "Item", 0, 1),
y = case_when(
name == "F1" ~ y_position[1],
name == "F2" ~ y_position[2],
name == "F3" ~ y_position[3],
name == "F4" ~ y_position[4],
name == "F5" ~ y_position[5],
name == "F6" ~ y_position[6],
name == "F7" ~ y_position[7],
name == "F8" ~ y_position[8],
.default = NA
))
layout[layout$type == "Item", "y"] <- seq(0, 1, length.out = sum(layout$type == "Item"))
g <- tidygraph::as_tbl_graph(loadings, directed = TRUE) |>
left_join(layout, by = c("name"))
p_efa <- ggraph(g, layout = as.data.frame(layout)[c("x", "y")]) +
geom_edge_link(
aes(
edge_width = abs(Loading),
edge_alpha = abs(Loading),
# edge_color = Loading,
filter = Type == "Loading"
# label = insight::format_value(Loading, lead_zero = FALSE, zap_small = TRUE)
),
color = "#212121",
arrow = arrow(length = unit(2.5, "mm"), type = "closed"),
end_cap = circle(5, 'mm')
) +
geom_edge_arc(
aes(
edge_width = abs(Loading),
filter = Type == "Correlation",
label = insight::format_value(Loading, lead_zero = FALSE, zap_small = TRUE)
),
edge_color = "forestgreen",
angle_calc = "along",
label_dodge = unit(2.5, "mm"),
force_flip = TRUE,
strength = 0.2 # controls curvature
) +
geom_node_label(data=filter(layout, type == "Item"),
aes(fill = name),
label = paste(rep(" ", 20), collapse = ""),
size = 3,
hjust = 0.5,
nudge_x = -0.05,
label.r = unit(0.2, "lines"),
label.padding = unit(0.5, "lines"),
label.size = 0) +
geom_node_label(data=filter(layout, type == "Item"),
aes(label = name),
fill = NA,
size = 3,
hjust = 0.5,
vjust = 0.5,
color = "white",
fontface = "bold",
nudge_x = -0.05,
label.size = 0) +
geom_node_point(data=filter(layout, type == "Factor"),
aes(color = name),
size = 12) +
geom_node_text(data=filter(layout, type == "Factor"),
aes(label = name),
size = 4,
color = "white",
fontface = "bold") +
scale_edge_width(range = c(0.3, 2)) +
scale_edge_alpha(range = c(0.1, 1)) +
scale_fill_manual(values = colors) +
scale_color_manual(values = cols) +
labs(title = "Exploratory Factor Analysis (EFA)", subtitle = "Method = Oblimin") +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(face="bold")) +
coord_cartesian(xlim = c(-0.08, 1.08))
list(n_fac=n_fac, efa=rez_efa, p=p_efa)
}
rez_efa <- make_efa(data_all, n = 3, cols = c("F1" = colors[["Cough"]],
"F2" = colors[["Thirsty"]],
"F3" = colors[["Bruise"]]))Loading required namespace: GPArotationWarning in geom_node_label(data = filter(layout, type == "Item"), aes(fill =
name), : Ignoring unknown parameters: `label.size`Warning in geom_node_label(data = filter(layout, type == "Item"), aes(label =
name), : Ignoring unknown parameters: `label.size`plot(rez_efa$n_fac)
rez_efa$p
We discarded:
data_allf <- select(data_all, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data1af <- select(data1a, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data1bf <- select(data1b, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data2f <- select(data2, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data3f <- select(data3, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data4f <- select(data4, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data5f <- select(data5, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data6f <- select(data6, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data7af <- select(data7a, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data7bf <- select(data7b, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data7cf <- select(data7c, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data8af <- select(data8a, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data8bf <- select(data8b, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data9f <- select(data9, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data10f <- select(data10, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data11f <- select(data11, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data12f <- select(data12, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data13f <- select(data13, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data14f <- select(data14, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data15f <- select(data15, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data16f <- select(data16, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)
data17f <- select(data17, -Taste, -Affective_touch, -Vomit, -Itch,
-Sex_arousal, -Temp)rez_hclust <- make_hclust(data_allf)
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
rez_hclust$p
rez_ega <- make_ega(data_allf, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Cough"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.529 | 0.04 | 0.017 | 0.04 | 0.026 | 0.046 | 0.131 |
| Breathing | 0.529 | 0.199 | 0.062 | 0.056 | 0.011 | 0.169 | 0.017 |
| Hungry | 0.139 | 0.526 | 0.087 | 0.008 | 0.013 | 0.096 | 0.056 |
| Thirsty | 0.096 | 0.526 | 0.129 | 0.017 | 0.02 | 0.107 | 0.017 |
| Urinate | 0.031 | 0.197 | 0.572 | 0.043 | 0.027 | 0.114 | 0 |
| Defecate | 0.065 | 0.072 | 0.572 | 0.087 | 0.06 | 0.113 | 0 |
| Sneeze | 0.058 | 0.01 | 0.088 | 0.561 | 0.107 | 0.091 | 0.028 |
| Cough | 0.054 | 0.02 | 0.036 | 0.561 | 0.174 | 0.115 | 0.084 |
| Wind | 0.014 | 0.025 | 0.072 | 0.116 | 0.575 | 0.079 | 0.065 |
| Burp | 0.032 | 0.017 | 0.016 | 0.184 | 0.575 | 0.095 | 0.125 |
| Muscles | 0.126 | 0.037 | 0.067 | 0.083 | 0.107 | 0.493 | 0.154 |
| Pain | 0.053 | 0.134 | 0.087 | 0.065 | 0.01 | 0.493 | 0.19 |
| Bruise | 0.042 | 0 | 0 | 0.055 | 0.09 | 0.276 | 0.489 |
| Blood_Sugar | 0.077 | 0.06 | 0 | 0.022 | 0.035 | 0.059 | 0.489 |
rez_ega$p
rez_efa <- make_efa(data_allf, n = 3, cols =
c("F1" = colors[["Wind"]], "F2" = colors[["Defecate"]],
"F3" = colors[["Heart"]]))Warning in geom_node_label(data = filter(layout, type == "Item"), aes(fill =
name), : Ignoring unknown parameters: `label.size`Warning in geom_node_label(data = filter(layout, type == "Item"), aes(label =
name), : Ignoring unknown parameters: `label.size`plot(rez_efa$n_fac)
rez_efa$p
make_cfa <- function(data) {
# Main models
m_g <- '
IAS =~ Burp + Wind + Cough + Sneeze + Hungry + Thirsty + Urinate + Defecate + Heart + Breathing + Pain + Muscles + Bruise + Blood_Sugar
'
m_ega <- '
CoughSneeze =~ Cough + Sneeze
UrinateDefecate =~ Urinate + Defecate
BruiseBloodsugar =~ Bruise + Blood_Sugar
WindBurp =~ Wind + Burp
HeartBreath =~ Heart + Breathing
MusclesPain =~ Muscles + Pain
HungryThirsty =~ Hungry + Thirsty
'
m_efa <- '
UrinateDefecate =~ Urinate + Defecate
Sudden =~ Cough + Sneeze + Wind + Burp
Homeostasis =~ Heart + Breathing + Muscles + Pain + Hungry + Thirsty + Bruise + Blood_Sugar
'
m_hclust <- '
HeartBreath =~ Heart + Breathing
BruiseBloodsugar =~ Bruise + Blood_Sugar
HungryThirsty =~ Hungry + Thirsty
Sudden =~ Cough + Sneeze + Wind + Burp
Unpleasant =~ Urinate + Defecate + Muscles + Pain
'
# Test bifactor models
m_egag <- paste0(m_ega, "\nIAS =~ CoughSneeze + WindBurp + BruiseBloodsugar + UrinateDefecate + HeartBreath + MusclesPain")
m_efag <- paste0(m_efa, "\nIAS =~ UrinateDefecate + Sudden + Homeostasis")
m_hclustg <- paste0(m_hclust, "\nIAS =~ HeartBreath + BruiseBloodsugar + HungryThirsty + Sudden + Unpleasant")
fit_g <- lavaan::cfa(m_g, data = data, std.lv = TRUE)
fit_ega <- lavaan::cfa(m_ega, data = data, std.lv = TRUE)
fit_egag <- lavaan::cfa(m_egag, data = data, std.lv = TRUE)
fit_efa <- lavaan::cfa(m_efa, data = data, std.lv = TRUE)
fit_efag <- lavaan::cfa(m_efag, data = data, std.lv = TRUE)
fit_hclust <- lavaan::cfa(m_hclust, data = data, std.lv = TRUE)
fit_hclustg <- lavaan::cfa(m_hclustg, data = data, std.lv = TRUE)
# Test
bf <- test_bf(fit_ega, fit_g, fit_egag, fit_efa, fit_efag, fit_hclust, fit_hclustg) |>
as.data.frame() |>
select(Name = Model, BF)
# test <- lavaan::lavTestLRT(fit_g, fit_ega, fit_egag, fit_efa, fit_efag, fit_hclust, fit_hclustg)
test <- compare_performance(fit_g, fit_ega, fit_egag, fit_efa, fit_efag, fit_hclust, fit_hclustg,
metrics = c("BIC", "RMSEA", "Chi2", "CFI")) |>
select(-BIC_wt, -Model) |>
merge(bf, by = "Name") |>
arrange(BIC) |>
gt::gt() |>
gt::tab_header(title = "CFA Model Comparison") |>
gt::data_color(
columns = c(BIC, Chi2, RMSEA),
method = "numeric",
palette = c("green", "white")) |>
gt::data_color(
columns = c(CFI),
method = "numeric",
palette = c("white", "green")) |>
gt::data_color(
columns = c(BF),
method = "numeric",
palette = c("red", "yellow", "white", "yellow", "green"),
domain = c(0, 0.3, 1, 3, 10000000)) |>
gt::fmt_number(columns = c(RMSEA, Chi2, CFI), decimals = 3)
param <- parameters::parameters(fit_ega)
perf <- performance::performance(fit_ega)
# Graph
nodes <- tidySEM::get_nodes(fit_ega) |>
mutate(name = str_replace(name, "_", " "))
edges <- tidySEM::get_edges(fit_ega) |>
mutate(from = str_replace(from, "_", " "),
to = str_replace(to, "_", " "),
est_std = as.numeric(est_std)) |>
filter(from != to)
g <- tidygraph::tbl_graph(nodes = nodes, edges = edges, directed = TRUE)
set.seed(1)
layout <- ggraph::create_layout(g, layout = "fr", weights = est_std)
p_cfa <- ggraph(layout) +
geom_edge_bend(aes(edge_width = abs(est_std), edge_alpha = abs(est_std),
label = format_value(est_std, zap_small = TRUE, lead_zero = FALSE),
filter = arrow == "none"),
color = "forestgreen",
angle_calc = "along",
check_overlap = TRUE,
label_dodge = unit(0, "mm"),
label_size = unit(3, "mm"),
strength = 0.3) +
geom_edge_link(aes(edge_width = abs(est_std), edge_color = abs(est_std),
label = format_value(est_std, zap_small = TRUE, lead_zero = FALSE),
filter = arrow != "none"),
angle_calc = "along",
label_dodge = unit(3, "mm"),
arrow = arrow(length = unit(2.5, "mm"), type = "open", ends = "first"),
lineend = "round",
linejoin = "bevel",
start_cap = circle(9, 'mm')) +
geom_node_point(aes(color = name, shape=shape), alpha = 0.97, size = 20) +
geom_node_text(data = filter(layout, shape == "rect"), aes(label = name),
size = 3, color = "white", fontface = "bold") +
scale_size_continuous(range = c(10, 20)) +
scale_edge_width(range = c(0.3, 4)) +
scale_edge_alpha(range = c(0.1, 0.9)) +
scale_edge_color_gradient(low = "#EEEEEE", high = "#212121") +
scale_color_manual(values = c(colors,
"CoughSneeze" = "#CDDC39",
"UrinateDefecate" = "#795548", "BruiseBloodsugar" = "#673AB7",
"WindBurp" = "#009688", "HeartBreath" = "#F44336",
"MusclesPain" = "#2196F3", "HungryThirsty" = "#FF9800")) +
scale_shape_manual(values = c("oval" = 16, "rect" = 15)) +
labs(title = "Confirmatory Factor Analysis (CFA)", subtitle = "Method = Maximum Likelihood") +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(face="bold"))
p_cfa
list(test = test, param = param, perf = perf, p=p_cfa)
}
rez_cfa0 <- make_cfa(data_allf)Warning: Could not access model information.
Warning: Could not access model information.
Warning: Could not access model information.
Warning: Could not access model information.
Warning: Could not access model information.
Warning: Could not access model information.
Warning: Could not access model information.Warning: When comparing models, please note that probably not all models were fit
from same data.When comparing models, please note that probably not all models were fit
from same data.rez_cfa0$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 1110166 | 0.035 | 2,334.112 | 0.984 | NA |
| fit_egag | 1114563 | 0.054 | 6,876.395 | 0.952 | 0 |
| fit_hclust | 1121160 | 0.078 | 13,441.878 | 0.906 | 0 |
| fit_hclustg | 1122347 | 0.079 | 14,681.532 | 0.897 | 0 |
| fit_efag | 1124348 | 0.083 | 16,703.253 | 0.883 | 0 |
| fit_efa | 1124348 | 0.083 | 16,703.253 | 0.883 | 0 |
| fit_g | 1138944 | 0.111 | 31,330.120 | 0.781 | 0 |
rez_cfa0$perf# Indices of model performance
Chi2(56) | p (Chi2) | Baseline(91) | p (Baseline) | GFI | AGFI | NFI
-------------------------------------------------------------------------
2334.112 | < .001 | 1.425e+05 | < .001 | 0.990 | 0.981 | 0.984
Chi2(56) | NNFI | CFI | RMSEA | RMSEA CI | p (RMSEA) | RMR | SRMR
-----------------------------------------------------------------------------
2334.112 | 0.974 | 0.984 | 0.035 | [0.034, 0.036] | > .999 | 0.019 | 0.019
Chi2(56) | RFI | PNFI | IFI | RNI | Loglikelihood | AIC | BIC | BIC_adjusted
-------------------------------------------------------------------------------------------
2334.112 | 0.973 | 0.605 | 0.984 | 0.984 | -5.548e+05 | 1.1e+06 | 1.1e+06 | 1.110e+06rez_cfa0$param# Loading
Link | Coefficient | SE | 95% CI
-----------------------------------------------------------------------
CoughSneeze =~ Cough | 0.68 | 4.57e-03 | [0.67, 0.69]
CoughSneeze =~ Sneeze | 0.60 | 4.32e-03 | [0.59, 0.61]
UrinateDefecate =~ Urinate | 0.67 | 4.85e-03 | [0.66, 0.68]
UrinateDefecate =~ Defecate | 0.63 | 4.55e-03 | [0.62, 0.64]
BruiseBloodsugar =~ Bruise | 0.86 | 8.34e-03 | [0.85, 0.88]
BruiseBloodsugar =~ Blood_Sugar | 0.63 | 7.65e-03 | [0.61, 0.64]
WindBurp =~ Wind | 0.72 | 5.05e-03 | [0.71, 0.73]
WindBurp =~ Burp | 0.80 | 5.19e-03 | [0.79, 0.81]
HeartBreath =~ Heart | 0.57 | 5.82e-03 | [0.56, 0.58]
HeartBreath =~ Breathing | 0.69 | 5.68e-03 | [0.68, 0.70]
MusclesPain =~ Muscles | 0.58 | 4.63e-03 | [0.57, 0.59]
MusclesPain =~ Pain | 0.58 | 4.83e-03 | [0.57, 0.59]
HungryThirsty =~ Hungry | 0.73 | 6.34e-03 | [0.72, 0.74]
HungryThirsty =~ Thirsty | 0.73 | 6.19e-03 | [0.72, 0.74]
Link | z | p
-------------------------------------------------
CoughSneeze =~ Cough | 148.76 | < .001
CoughSneeze =~ Sneeze | 138.72 | < .001
UrinateDefecate =~ Urinate | 139.04 | < .001
UrinateDefecate =~ Defecate | 138.08 | < .001
BruiseBloodsugar =~ Bruise | 103.66 | < .001
BruiseBloodsugar =~ Blood_Sugar | 82.24 | < .001
WindBurp =~ Wind | 143.16 | < .001
WindBurp =~ Burp | 153.21 | < .001
HeartBreath =~ Heart | 97.94 | < .001
HeartBreath =~ Breathing | 121.97 | < .001
MusclesPain =~ Muscles | 126.04 | < .001
MusclesPain =~ Pain | 120.36 | < .001
HungryThirsty =~ Hungry | 114.88 | < .001
HungryThirsty =~ Thirsty | 118.19 | < .001
# Correlation
Link | Coefficient | SE | 95% CI
---------------------------------------------------------------------------
CoughSneeze ~~ UrinateDefecate | 0.56 | 5.86e-03 | [0.55, 0.57]
CoughSneeze ~~ BruiseBloodsugar | 0.55 | 7.21e-03 | [0.54, 0.56]
CoughSneeze ~~ WindBurp | 0.72 | 4.73e-03 | [0.72, 0.73]
CoughSneeze ~~ HeartBreath | 0.52 | 6.61e-03 | [0.51, 0.53]
CoughSneeze ~~ MusclesPain | 0.66 | 5.94e-03 | [0.65, 0.68]
CoughSneeze ~~ HungryThirsty | 0.46 | 6.89e-03 | [0.45, 0.48]
UrinateDefecate ~~ BruiseBloodsugar | 0.40 | 7.70e-03 | [0.38, 0.41]
UrinateDefecate ~~ WindBurp | 0.51 | 5.99e-03 | [0.50, 0.52]
UrinateDefecate ~~ HeartBreath | 0.53 | 6.60e-03 | [0.52, 0.54]
UrinateDefecate ~~ MusclesPain | 0.64 | 6.07e-03 | [0.63, 0.66]
UrinateDefecate ~~ HungryThirsty | 0.66 | 5.99e-03 | [0.64, 0.67]
BruiseBloodsugar ~~ WindBurp | 0.58 | 6.99e-03 | [0.56, 0.59]
BruiseBloodsugar ~~ HeartBreath | 0.50 | 7.93e-03 | [0.48, 0.52]
BruiseBloodsugar ~~ MusclesPain | 0.73 | 7.30e-03 | [0.72, 0.75]
BruiseBloodsugar ~~ HungryThirsty | 0.45 | 8.13e-03 | [0.44, 0.47]
WindBurp ~~ HeartBreath | 0.46 | 6.73e-03 | [0.44, 0.47]
WindBurp ~~ MusclesPain | 0.63 | 5.96e-03 | [0.62, 0.65]
WindBurp ~~ HungryThirsty | 0.44 | 6.86e-03 | [0.43, 0.45]
HeartBreath ~~ MusclesPain | 0.64 | 6.76e-03 | [0.63, 0.66]
HeartBreath ~~ HungryThirsty | 0.64 | 6.75e-03 | [0.63, 0.65]
MusclesPain ~~ HungryThirsty | 0.63 | 6.77e-03 | [0.62, 0.65]
Link | z | p
-----------------------------------------------------
CoughSneeze ~~ UrinateDefecate | 95.13 | < .001
CoughSneeze ~~ BruiseBloodsugar | 76.32 | < .001
CoughSneeze ~~ WindBurp | 153.13 | < .001
CoughSneeze ~~ HeartBreath | 78.74 | < .001
CoughSneeze ~~ MusclesPain | 111.88 | < .001
CoughSneeze ~~ HungryThirsty | 67.02 | < .001
UrinateDefecate ~~ BruiseBloodsugar | 51.46 | < .001
UrinateDefecate ~~ WindBurp | 84.78 | < .001
UrinateDefecate ~~ HeartBreath | 80.33 | < .001
UrinateDefecate ~~ MusclesPain | 106.28 | < .001
UrinateDefecate ~~ HungryThirsty | 109.51 | < .001
BruiseBloodsugar ~~ WindBurp | 82.78 | < .001
BruiseBloodsugar ~~ HeartBreath | 63.04 | < .001
BruiseBloodsugar ~~ MusclesPain | 100.58 | < .001
BruiseBloodsugar ~~ HungryThirsty | 55.88 | < .001
WindBurp ~~ HeartBreath | 67.88 | < .001
WindBurp ~~ MusclesPain | 106.23 | < .001
WindBurp ~~ HungryThirsty | 64.04 | < .001
HeartBreath ~~ MusclesPain | 95.23 | < .001
HeartBreath ~~ HungryThirsty | 94.69 | < .001
MusclesPain ~~ HungryThirsty | 93.63 | < .001rez_cfa0$p
The structural analysis seem to converge on the idea of small clusters (of pairs or triplets) that are potentially inter-related parts of larger clusters (although with unstable associations). Best way to assess the values of this new granular structure is to assess its predictive value against convergent measures, and see if it’s superior to a unique score (-> Study 2).
fig2 <- (rez_hclust$p | rez_ega$p) / (rez_efa$p | rez_cfa0$p)
ggsave("figures/Figure2.png", fig2, width=14, height=14, dpi=300, bg="white")rez_hclust <- make_hclust(data1a)
rez_ega <- make_ega(data1a, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Cough"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data1a, n = 4, cols = c("F1" = colors[["Itch"]], "F2" = colors[["Burp"]], "F3" = colors[["Defecate"]], "F4" = colors[["Hungry"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| Heart | 0.504 | 0.125 | 0.046 | 0 | 0.093 | 0.114 | 0 | 0 |
| Breathing | 0.504 | 0.14 | 0.032 | 0.098 | 0.158 | 0.123 | 0 | 0.036 |
| Hungry | 0.2 | 0.511 | 0.12 | 0.004 | 0.028 | 0.064 | 0 | 0.034 |
| Thirsty | 0.074 | 0.511 | 0.166 | 0 | 0.068 | 0.13 | 0.005 | 0.053 |
| Defecate | 0.044 | 0.093 | 0.632 | 0.107 | 0.077 | 0.036 | 0.059 | −0.045 |
| Urinate | 0.026 | 0.253 | 0.421 | 0.035 | 0.167 | 0.035 | 0.046 | −0.02 |
| Taste | 0.032 | 0.018 | 0.21 | 0.105 | 0.18 | 0.149 | 0.082 | 0.028 |
| Sneeze | 0 | 0.006 | 0.078 | 0.676 | 0.094 | 0.001 | 0.092 | 0.044 |
| Cough | 0.053 | 0 | 0.014 | 0.377 | 0.11 | 0 | 0.171 | 0.121 |
| Vomit | 0.088 | 0 | 0.177 | 0.299 | 0.066 | 0.067 | 0 | 0.054 |
| Muscles | 0.084 | 0.022 | 0.13 | 0.03 | 0.454 | 0.032 | 0.029 | 0.093 |
| Pain | 0.126 | 0.032 | 0.133 | 0.067 | 0.438 | 0.195 | 0 | 0.143 |
| Temp | 0.086 | 0.056 | 0.117 | 0.125 | 0.264 | 0.245 | 0 | 0.038 |
| Sex_arousal | 0.103 | 0.042 | 0.055 | 0.006 | 0.069 | 0.435 | 0.119 | 0.007 |
| Affective_touch | 0.059 | 0.086 | 0.058 | 0.027 | 0.205 | 0.435 | 0.011 | 0.152 |
| Wind | 0 | 0.007 | 0.189 | 0.051 | 0 | 0.261 | 0.571 | 0.058 |
| Burp | 0 | 0 | 0.007 | 0.203 | 0.034 | 0 | 0.571 | 0.105 |
| Blood_Sugar | 0.013 | 0 | −0.02 | 0.04 | 0.033 | 0.033 | 0.015 | 0.455 |
| Bruise | 0 | 0.108 | −0.044 | 0.08 | 0.193 | 0.044 | 0.068 | 0.401 |
| Itch | 0.034 | 0 | 0.027 | 0.076 | 0.074 | 0.224 | 0.07 | 0.388 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data1af)
rez_ega <- make_ega(data1af, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data1af, n = 3, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Urinate"]], "F3" = colors[["Bruise"]]))
rez_cfa1a <- make_cfa(data1af)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.511 | 0.128 | 0.036 | 0.009 | 0.01 | 0.108 | 0 |
| Breathing | 0.511 | 0.16 | 0.048 | 0.068 | 0.015 | 0.151 | 0.045 |
| Hungry | 0.202 | 0.514 | 0.105 | 0.015 | 0 | 0.042 | 0.048 |
| Thirsty | 0.091 | 0.514 | 0.178 | 0 | 0.018 | 0.041 | 0.089 |
| Urinate | 0.029 | 0.253 | 0.565 | 0.038 | 0.05 | 0.185 | −0.017 |
| Defecate | 0.08 | 0.108 | 0.565 | 0.098 | 0.087 | 0.09 | −0.042 |
| Sneeze | 0.033 | 0.02 | 0.12 | 0.574 | 0.117 | 0.091 | 0 |
| Cough | 0.071 | 0 | 0.021 | 0.574 | 0.171 | 0.06 | 0.157 |
| Wind | 0.021 | 0.024 | 0.136 | 0.071 | 0.577 | 0.028 | 0.089 |
| Burp | 0.012 | 0 | 0.008 | 0.22 | 0.577 | 0.041 | 0.089 |
| Muscles | 0.091 | 0.042 | 0.118 | 0.058 | 0.04 | 0.523 | 0.125 |
| Pain | 0.183 | 0.044 | 0.107 | 0.061 | 0.014 | 0.523 | 0.18 |
| Bruise | 0.01 | 0.128 | −0.031 | 0.059 | 0.093 | 0.222 | 0.502 |
| Blood_Sugar | 0.033 | 0 | −0.013 | 0.052 | 0.032 | 0.053 | 0.502 |
plot(rez_efa$n_fac)
rez_cfa1a$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 15870.94 | 0.026 | 72.825 | 0.988 | NA |
| fit_egag | 15892.96 | 0.059 | 180.403 | 0.924 | 1.654961e-05 |
| fit_hclustg | 15911.16 | 0.065 | 210.828 | 0.905 | 1.846821e-09 |
| fit_hclust | 15913.39 | 0.062 | 182.492 | 0.921 | 6.080789e-10 |
| fit_efa | 15941.59 | 0.073 | 253.472 | 0.877 | 4.576430e-16 |
| fit_efag | 15941.59 | 0.073 | 253.472 | 0.877 | 4.576430e-16 |
| fit_g | 16082.67 | 0.098 | 412.887 | 0.770 | 1.059993e-46 |
rez_hclust <- make_hclust(data1b)
rez_ega <- make_ega(data1b, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Defecate"]], "C3" = colors[["Bruise"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Temp"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data1b, n = 4, cols = c("F1" = colors[["Urinate"]], "F2" = colors[["Bruise"]],
"F3" = colors[["Sneeze"]], "F4" = colors[["Wind"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||
| Item | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Breathing | 0.42 | 0.118 | 0.065 | 0.016 | 0.037 |
| Hungry | 0.415 | 0.078 | 0.13 | 0 | 0.023 |
| Thirsty | 0.388 | 0.121 | 0.078 | 0.036 | 0.053 |
| Temp | 0.314 | 0 | 0.206 | 0.158 | 0.08 |
| Heart | 0.207 | 0.043 | 0.169 | 0.053 | 0.027 |
| Urinate | 0.214 | 0.574 | 0.028 | 0.098 | 0.016 |
| Defecate | 0.121 | 0.574 | 0.106 | 0.041 | 0.077 |
| Bruise | 0.055 | 0 | 0.45 | 0 | 0.094 |
| Pain | 0.154 | 0.051 | 0.444 | 0.049 | 0.062 |
| Itch | 0.054 | 0 | 0.353 | 0.098 | 0.12 |
| Affective_touch | 0.113 | 0 | 0.31 | 0 | 0.128 |
| Blood_Sugar | 0 | 0 | 0.303 | 0.037 | 0.049 |
| Muscles | 0.178 | 0.045 | 0.268 | 0.047 | 0.09 |
| Taste | 0.151 | 0.061 | 0.189 | 0.067 | 0.101 |
| Sneeze | 0.048 | 0.036 | 0.125 | 0.621 | 0.043 |
| Cough | 0.107 | 0.043 | 0.02 | 0.502 | 0.103 |
| Vomit | 0.095 | 0.064 | 0.116 | 0.228 | 0.126 |
| Wind | 0.017 | 0 | 0.037 | 0.09 | 0.651 |
| Burp | 0.034 | 0 | 0.27 | 0.113 | 0.465 |
| Sex_arousal | 0.154 | 0.093 | 0.24 | 0.061 | 0.202 |
plot(rez_efa$n_fac)
Note: CFA did not converge
rez_hclust <- make_hclust(data1bf)
rez_ega <- make_ega(data1af, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data1bf, n = 3, cols = c("F1" = colors[["Urinate"]], "F2" = colors[["Bruise"]],
"F3" = colors[["Cough"]], "F4" = colors[["Itch"]]))
# rez_cfa1b <- make_cfa(data1bf)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.511 | 0.128 | 0.036 | 0.009 | 0.01 | 0.108 | 0 |
| Breathing | 0.511 | 0.16 | 0.048 | 0.068 | 0.015 | 0.151 | 0.045 |
| Hungry | 0.202 | 0.514 | 0.105 | 0.015 | 0 | 0.042 | 0.048 |
| Thirsty | 0.091 | 0.514 | 0.178 | 0 | 0.018 | 0.041 | 0.089 |
| Urinate | 0.029 | 0.253 | 0.565 | 0.038 | 0.05 | 0.185 | −0.017 |
| Defecate | 0.08 | 0.108 | 0.565 | 0.098 | 0.087 | 0.09 | −0.042 |
| Sneeze | 0.033 | 0.02 | 0.12 | 0.574 | 0.117 | 0.091 | 0 |
| Cough | 0.071 | 0 | 0.021 | 0.574 | 0.171 | 0.06 | 0.157 |
| Wind | 0.021 | 0.024 | 0.136 | 0.071 | 0.577 | 0.028 | 0.089 |
| Burp | 0.012 | 0 | 0.008 | 0.22 | 0.577 | 0.041 | 0.089 |
| Muscles | 0.091 | 0.042 | 0.118 | 0.058 | 0.04 | 0.523 | 0.125 |
| Pain | 0.183 | 0.044 | 0.107 | 0.061 | 0.014 | 0.523 | 0.18 |
| Bruise | 0.01 | 0.128 | −0.031 | 0.059 | 0.093 | 0.222 | 0.502 |
| Blood_Sugar | 0.033 | 0 | −0.013 | 0.052 | 0.032 | 0.053 | 0.502 |
plot(rez_efa$n_fac)
# rez_cfa1b$testrez_hclust <- make_hclust(data2)
rez_ega <- make_ega(data2, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Urinate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Pain"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data2, n = 4, cols = c("F1" = colors[["Cough"]], "F2" = colors[["Bruise"]],
"F3" = colors[["Heart"]], "F4" = colors[["Sex arousal"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.539 | 0.057 | 0.053 | 0 | 0.051 | 0.067 | 0.047 |
| Breathing | 0.539 | 0.126 | 0.067 | 0.027 | 0.16 | 0 | 0.058 |
| Hungry | 0.096 | 0.524 | 0.066 | 0.028 | 0.18 | 0.026 | 0.069 |
| Thirsty | 0.074 | 0.524 | 0.166 | 0.035 | 0.027 | 0.091 | 0 |
| Defecate | 0.004 | 0.057 | 0.52 | 0.097 | 0.061 | 0.126 | 0 |
| Urinate | 0 | 0.175 | 0.511 | 0.01 | 0.065 | 0.021 | −0.013 |
| Sex_arousal | 0.04 | 0.089 | 0.306 | 0.04 | 0.135 | 0.051 | 0.086 |
| Vomit | 0.027 | 0.002 | 0.275 | 0.171 | 0.024 | 0.037 | 0.062 |
| Taste | 0.084 | 0 | 0.199 | 0.074 | 0.148 | 0.086 | 0.056 |
| Sneeze | 0.001 | 0.031 | 0.233 | 0.542 | 0.033 | 0.138 | 0.006 |
| Cough | 0.026 | 0.039 | 0.074 | 0.542 | 0.238 | 0.198 | 0.103 |
| Pain | 0.078 | 0.086 | 0.027 | 0.038 | 0.47 | 0.144 | 0.159 |
| Muscles | 0.061 | 0.027 | 0.072 | 0.074 | 0.337 | 0.076 | 0.09 |
| Temp | 0.058 | 0.095 | 0.213 | 0.138 | 0.299 | 0.055 | 0.039 |
| Wind | 0 | 0.026 | 0.162 | 0.156 | 0.142 | 0.51 | 0.044 |
| Burp | 0.058 | 0.082 | 0.051 | 0.13 | 0.113 | 0.51 | 0.125 |
| Blood_Sugar | 0.039 | 0 | 0.025 | 0 | 0 | 0 | 0.469 |
| Bruise | 0.009 | 0.03 | 0.006 | 0.023 | 0.229 | 0.086 | 0.375 |
| Itch | 0.038 | 0 | 0.042 | 0.1 | 0.073 | 0.091 | 0.332 |
| Affective_touch | 0.034 | 0.055 | 0.095 | 0 | 0.052 | 0.048 | 0.259 |
plot(rez_efa$n_fac)
Note: CFA did not converge
rez_hclust <- make_hclust(data2f)
rez_ega <- make_ega(data2f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data2f, n = 3, cols = c("F1" = colors[["Cough"]], "F2" = colors[["Bruise"]],
"F3" = colors[["Hungry"]]))
# rez_cfa2 <- make_cfa(data2f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.544 | 0.062 | 0.021 | 0 | 0.083 | 0.078 | 0.037 |
| Breathing | 0.544 | 0.148 | 0.019 | 0.066 | 0 | 0.165 | 0.06 |
| Hungry | 0.113 | 0.528 | 0.077 | 0.041 | 0.043 | 0.154 | 0.047 |
| Thirsty | 0.081 | 0.528 | 0.16 | 0.039 | 0.096 | 0.041 | 0 |
| Urinate | 0.013 | 0.21 | 0.567 | 0.058 | 0.06 | 0.031 | 0 |
| Defecate | 0.031 | 0.075 | 0.567 | 0.136 | 0.157 | 0.097 | 0 |
| Sneeze | 0.021 | 0.048 | 0.139 | 0.56 | 0.161 | 0.024 | 0 |
| Cough | 0.05 | 0.045 | 0.048 | 0.56 | 0.233 | 0.215 | 0.085 |
| Wind | 0.009 | 0.047 | 0.113 | 0.175 | 0.514 | 0.159 | 0.021 |
| Burp | 0.063 | 0.083 | 0.055 | 0.141 | 0.514 | 0.141 | 0.107 |
| Muscles | 0.077 | 0.044 | 0.046 | 0.087 | 0.083 | 0.479 | 0.097 |
| Pain | 0.097 | 0.108 | 0.037 | 0.073 | 0.166 | 0.479 | 0.18 |
| Bruise | 0.017 | 0.037 | 0 | 0.039 | 0.112 | 0.314 | 0.508 |
| Blood_Sugar | 0.064 | 0.005 | 0 | 0.027 | 0.013 | 0.01 | 0.508 |
plot(rez_efa$n_fac)
# rez_cfa2$testrez_hclust <- make_hclust(data3)
rez_ega <- make_ega(data3, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Urinate"]], "C3" = colors[["Pain"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Bruise"]], "C7" = colors[["Muscles"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data3, n = 4, cols = c("F1" = colors[["Cough"]], "F2" = colors[["Urinate"]],
"F3" = colors[["Blood Sugar"]], "F4" = colors[["Sex arousal"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Breathing | 0.618 | 0.154 | 0.01 | 0.005 | 0 | 0.046 |
| Heart | 0.349 | 0.002 | 0.146 | 0.043 | 0.024 | 0.128 |
| Sex_arousal | 0.27 | 0.122 | 0.127 | 0.06 | 0.177 | 0.022 |
| Urinate | 0.078 | 0.51 | 0.127 | 0.093 | 0 | 0 |
| Thirsty | 0 | 0.454 | 0.055 | 0.041 | 0 | 0.023 |
| Defecate | 0.121 | 0.425 | 0.063 | 0.108 | 0.007 | 0.048 |
| Hungry | 0.158 | 0.364 | 0.076 | 0.052 | 0.025 | 0.019 |
| Muscles | 0.122 | 0.025 | 0.506 | 0.082 | 0.031 | 0.167 |
| Pain | 0.022 | 0.153 | 0.356 | 0.091 | 0.036 | 0.151 |
| Taste | 0.095 | 0.033 | 0.221 | 0.129 | 0.028 | 0.064 |
| Cough | 0 | 0 | 0.061 | 0.65 | 0.118 | 0.047 |
| Sneeze | 0.061 | 0.049 | 0.078 | 0.589 | 0.066 | 0.053 |
| Temp | 0.019 | 0.169 | 0.216 | 0.246 | 0.085 | 0 |
| Vomit | 0.055 | 0.071 | 0.095 | 0.23 | 0.152 | 0 |
| Wind | 0.168 | 0.022 | 0.041 | 0.118 | 0.562 | 0.041 |
| Burp | 0.04 | 0.003 | 0.074 | 0.227 | 0.562 | 0.076 |
| Blood_Sugar | 0.108 | 0.008 | 0.019 | 0 | 0 | 0.48 |
| Itch | 0.053 | 0 | 0.068 | 0.031 | 0.112 | 0.395 |
| Bruise | 0 | 0 | 0.257 | 0.026 | 0.019 | 0.39 |
| Affective_touch | 0.065 | 0.074 | 0.178 | 0.034 | 0 | 0.295 |
plot(rez_efa$n_fac)
Note: CFA did not converge
rez_hclust <- make_hclust(data3f)
rez_ega <- make_ega(data3f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Urinate"]], "C3" = colors[["Sneeze"]], "C4" = colors[["Wind"]],
"C5" = colors[["Pain"]], "C6" = colors[["Bruise"]], "C7" = colors[["Muscles"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data3f, n = 3, cols = c("F1" = colors[["Cough"]], "F2" = colors[["Hungry"]],
"F3" = colors[["Blood Sugar"]]))
# rez_cfa3 <- make_cfa(data3f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.537 | 0.006 | 0.058 | 0.031 | 0.086 | 0.134 |
| Breathing | 0.537 | 0.199 | 0 | 0.031 | 0.066 | 0.015 |
| Urinate | 0.1 | 0.527 | 0.036 | 0.006 | 0.141 | 0 |
| Thirsty | 0 | 0.468 | 0 | 0 | 0.084 | 0.029 |
| Defecate | 0.035 | 0.44 | 0.067 | 0.067 | 0.096 | 0 |
| Hungry | 0.16 | 0.378 | 0.026 | 0.049 | 0.086 | 0 |
| Sneeze | 0.085 | 0.095 | 0.623 | 0.111 | 0.054 | 0 |
| Cough | 0 | 0.037 | 0.623 | 0.16 | 0.099 | 0.061 |
| Wind | 0.016 | 0.078 | 0.099 | 0.581 | 0.059 | 0 |
| Burp | 0.06 | 0.026 | 0.127 | 0.581 | 0.118 | 0.046 |
| Muscles | 0.122 | 0.064 | 0.054 | 0.075 | 0.515 | 0.141 |
| Pain | 0.014 | 0.19 | 0.04 | 0.055 | 0.515 | 0.125 |
| Bruise | 0 | 0 | 0.04 | 0.039 | 0.248 | 0.544 |
| Blood_Sugar | 0.154 | 0.021 | 0.003 | 0 | 0.058 | 0.544 |
plot(rez_efa$n_fac)
# rez_cfa3$testrez_hclust <- make_hclust(data4)
rez_ega <- make_ega(data4, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Pain"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Affective touch"]]))
rez_efa <- make_efa(data4, n = 4, cols = c("F1" = colors[["Wind"]], "F2" = colors[["Itch"]],
"F3" = colors[["Breathing"]], "F4" = colors[["Urinate"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| Heart | 0.617 | 0 | 0.021 | 0.044 | 0 | 0.01 | 0.151 | 0.043 |
| Breathing | 0.617 | 0.154 | 0.045 | 0 | 0.067 | 0.028 | 0.029 | 0.026 |
| Hungry | 0.07 | 0.56 | 0.151 | 0 | 0.094 | 0.022 | 0 | 0.007 |
| Thirsty | 0.049 | 0.56 | 0.106 | 0.009 | 0.051 | 0.014 | 0 | 0.023 |
| Urinate | 0 | 0.161 | 0.559 | 0.041 | 0.108 | 0.04 | −0.01 | 0 |
| Defecate | 0.016 | 0.02 | 0.524 | 0.08 | 0.072 | 0.093 | 0 | 0.034 |
| Taste | 0.035 | 0.072 | 0.169 | 0.08 | 0.131 | 0.089 | 0 | 0.08 |
| Sneeze | 0.003 | 0.01 | 0.036 | 0.587 | 0.016 | 0.117 | 0 | 0.07 |
| Cough | 0.005 | 0 | 0.1 | 0.462 | 0.063 | 0.203 | 0.065 | 0.04 |
| Vomit | 0.029 | 0 | 0.083 | 0.291 | 0.122 | 0.069 | 0.097 | 0.005 |
| Pain | 0.036 | 0.032 | 0.156 | 0.086 | 0.527 | 0.028 | 0.14 | 0.032 |
| Muscles | 0.002 | 0.023 | 0.069 | 0.071 | 0.449 | 0.136 | 0.06 | 0.048 |
| Temp | 0.019 | 0.102 | 0.117 | 0.045 | 0.372 | 0.148 | 0 | 0.051 |
| Wind | 0.006 | 0 | 0.073 | 0.107 | 0.085 | 0.604 | 0.039 | 0.024 |
| Burp | 0 | 0 | 0 | 0.178 | 0.093 | 0.451 | 0.02 | 0.02 |
| Sex_arousal | 0.022 | 0.035 | 0.146 | 0.067 | 0.102 | 0.186 | 0 | 0.085 |
| Bruise | 0.05 | 0 | 0 | 0.084 | 0.085 | 0.006 | 0.448 | 0.059 |
| Blood_Sugar | 0.028 | 0 | −0.006 | 0 | 0.018 | 0.027 | 0.448 | 0.143 |
| Tickle | 0.014 | 0 | 0.003 | 0.056 | 0.014 | 0.055 | 0.009 | 0.62 |
| Itch | 0.026 | 0 | 0.04 | 0.042 | 0.053 | 0.01 | 0.257 | 0.542 |
| Affective_touch | 0.023 | 0.035 | 0.093 | 0.027 | 0.076 | 0.091 | 0.159 | 0.275 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data4f)
rez_ega <- make_ega(data4f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data4f, n = 3, cols = c("F1" = colors[["Wind"]], "F2" = colors[["Breathing"]],
"F3" = colors[["Urinate"]]))
rez_cf4 <- make_cfa(data4f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.619 | 0 | 0.031 | 0.028 | 0.004 | 0 | 0.197 |
| Breathing | 0.619 | 0.173 | 0 | 0.003 | 0.027 | 0.086 | 0.038 |
| Hungry | 0.083 | 0.565 | 0.126 | 0 | 0 | 0.069 | 0.006 |
| Thirsty | 0.053 | 0.565 | 0.09 | 0.022 | 0 | 0.069 | 0.003 |
| Urinate | 0 | 0.185 | 0.582 | 0.061 | 0.017 | 0.112 | 0 |
| Defecate | 0.026 | 0.048 | 0.582 | 0.076 | 0.065 | 0.074 | 0.025 |
| Sneeze | 0.012 | 0.023 | 0.054 | 0.57 | 0.09 | 0.052 | 0.111 |
| Cough | 0.013 | 0 | 0.076 | 0.57 | 0.189 | 0.093 | 0.122 |
| Wind | 0.022 | 0 | 0.077 | 0.129 | 0.569 | 0.088 | 0.094 |
| Burp | 0.004 | 0 | 0 | 0.149 | 0.569 | 0.11 | 0.067 |
| Muscles | 0.011 | 0.058 | 0.041 | 0.068 | 0.131 | 0.556 | 0.152 |
| Pain | 0.054 | 0.074 | 0.124 | 0.067 | 0.055 | 0.556 | 0.137 |
| Blood_Sugar | 0.036 | 0 | 0 | 0 | 0.04 | 0.038 | 0.434 |
| Bruise | 0.058 | 0 | 0 | 0.051 | 0.011 | 0.121 | 0.351 |
| Tickle | 0.045 | 0.007 | 0.017 | 0.12 | 0.068 | 0.067 | 0.238 |
plot(rez_efa$n_fac)
rez_cf4$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 28320.01 | 0.025 | 83.537 | 0.991 | NA |
| fit_egag | 28365.84 | 0.052 | 222.983 | 0.950 | 1.118915e-10 |
| fit_hclust | 28472.07 | 0.067 | 309.152 | 0.920 | 9.577515e-34 |
| fit_hclustg | 28503.33 | 0.072 | 373.847 | 0.901 | 1.560998e-40 |
| fit_efag | 28732.35 | 0.096 | 616.239 | 0.822 | 2.902305e-90 |
| fit_efa | 28732.35 | 0.096 | 616.239 | 0.822 | 2.902305e-90 |
| fit_g | 29001.91 | 0.116 | 905.867 | 0.728 | 8.453539e-149 |
rez_hclust <- make_hclust(data5)
rez_ega <- make_ega(data5, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Sneeze"]], "C3" = colors[["Vomit"]], "C4" = colors[["Cough"]],
"C5" = colors[["Defecate"]], "C6" = colors[["Muscles"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data5, n = 8, cols = c("F1" = colors[["Urinate"]], "F2" = colors[["Itch"]],
"F3" = colors[["Burp"]], "F4" = colors[["Hungry"]],
"F5" = colors[["Affective touch"]], "F6" = colors[["Breathing"]], "F7" = colors[["Sex arousal"]], "F8" = colors[["Taste"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.435 | 0.01 | 0.075 | 0.039 | 0.089 | 0.164 | 0.046 |
| Thirsty | 0.435 | 0.154 | 0 | 0.094 | 0.084 | 0.16 | 0.037 |
| Burp | 0 | 0.454 | 0 | 0.071 | 0.082 | 0.117 | 0.042 |
| Sneeze | 0.084 | 0.323 | 0.069 | 0.131 | 0.024 | 0.052 | 0.112 |
| Hungry | 0.153 | 0.226 | 0.054 | 0.01 | 0.062 | 0.085 | −0.051 |
| Breathing | 0.057 | 0 | 0.517 | 0 | 0.087 | 0.115 | 0.049 |
| Vomit | 0.06 | 0.134 | 0.517 | 0.13 | 0.018 | 0.183 | 0 |
| Urinate | 0.089 | 0.014 | 0.129 | 0.534 | 0.143 | 0.006 | 0 |
| Cough | 0.114 | 0.202 | 0 | 0.404 | 0.059 | 0.252 | 0.085 |
| Pain | 0.074 | 0.09 | 0.044 | 0.404 | 0.023 | 0.246 | 0.055 |
| Taste | 0.152 | 0.026 | 0.094 | 0 | 0.343 | 0.266 | 0 |
| Affective_touch | 0 | 0.073 | 0 | 0.017 | 0.337 | 0.074 | 0.005 |
| Defecate | 0.086 | 0.068 | 0.012 | 0.154 | 0.3 | 0.069 | 0.015 |
| Blood_Sugar | 0.036 | 0.017 | 0 | 0 | 0.133 | 0.011 | 0 |
| Wind | 0.21 | 2.563 × 10−4 | 0.19 | 0.128 | 0.172 | 0.368 | 0.006 |
| Sex_arousal | 0.201 | 0.146 | 0 | 0.089 | 0.1 | 0.346 | 0 |
| Muscles | 0 | 0.059 | 0.082 | 0.056 | 0.131 | 0.293 | 0.096 |
| Temp | 0.162 | 0.105 | 0.065 | 0.155 | 0.065 | 0.245 | 0 |
| Bruise | 0.153 | 0.118 | 0 | 0.068 | 0.024 | 0.006 | 0.55 |
| Itch | 0 | 0.012 | 0.057 | 0.055 | 0 | 0.1 | 0.55 |
plot(rez_efa$n_fac)
Note: EGA errors. Note: CFA did not converge
rez_hclust <- make_hclust(data5f)
# rez_ega <- make_ega(data5f, cols = c(
# "C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
# "C5" = colors[["Cough"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data5f, n = 5, cols = c("F1" = colors[["Urinate"]], "F2" = colors[["Burp"]],
"F3" = colors[["Wind"]], "F4" = colors[["Thirsty"]],
"F5" = colors[["Sneeze"]]))
# rez_cfa5 <- make_cfa(data5f)
rez_hclust$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
plot(rez_efa$n_fac)
# rez_cfa5$testrez_hclust <- make_hclust(data6)
rez_ega <- make_ega(data6, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Cough"]], "C6" = colors[["Itch"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data6, n = 3, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Defecate"]],
"F3" = colors[["Hungry"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.524 | 0.034 | 0 | 0.086 | 0 | 7.366 × 10−4 | 0.172 |
| Breathing | 0.524 | 0.135 | 0 | 0.127 | 0.028 | 0.078 | 0.116 |
| Hungry | 0.124 | 0.554 | 0.023 | 0.124 | 0 | 0.086 | 0.028 |
| Thirsty | 0.072 | 0.554 | 0.095 | 0.045 | 0 | 0.045 | 0.007 |
| Urinate | 0 | 0.082 | 0.612 | 0.037 | 0.052 | 0.015 | 0 |
| Defecate | 0 | 0.071 | 0.612 | 0.096 | 0.053 | 0.205 | 0 |
| Wind | 0.025 | 0.014 | 0.059 | 0.545 | 0.057 | 0.044 | 0.043 |
| Burp | 0.105 | 0 | 0 | 0.537 | 0.13 | 0.004 | 0.178 |
| Sex_arousal | 0 | 0.099 | 0.031 | 0.288 | 0.048 | 0.185 | 0 |
| Muscles | 0.126 | 0.033 | 0 | 0.259 | 0.052 | 0.216 | 0.116 |
| Taste | 0.044 | 0.06 | 0.036 | 0.2 | 0.075 | 0.106 | 0 |
| Cough | 0.025 | 0 | 0 | 0.154 | 0.602 | 0.122 | 0.061 |
| Sneeze | 0 | 0 | 0.099 | 0.051 | 0.588 | 0.152 | 0 |
| Vomit | 0.014 | 0 | 0 | 0.156 | 0.253 | 0.099 | 0 |
| Itch | 0 | 0 | 0.06 | 0.102 | 0.087 | 0.509 | 0 |
| Pain | 9.275 × 10−4 | 0.077 | 0.015 | 0.1 | 0.048 | 0.431 | 0.24 |
| Affective_touch | 0 | 0.065 | 0.013 | 0.161 | 0.03 | 0.275 | 0.085 |
| Temp | 0.098 | 0 | 0.098 | 0.132 | 0.168 | 0.243 | 0 |
| Bruise | 0.135 | 0 | 0 | 0.087 | 0.016 | 0.147 | 0.46 |
| Blood_Sugar | 0.071 | 0.021 | 0 | 0.083 | 0.015 | 0.037 | 0.46 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data6f)
rez_ega <- make_ega(data6f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Bruise"]], "C7" = colors[["Pain"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data6f, n = 3, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Defecate"]],
"F3" = colors[["Thirsty"]]))
rez_cfa6 <- make_cfa(data6f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.525 | 0.036 | 0 | 0 | 0.022 | 0.174 |
| Breathing | 0.525 | 0.148 | 0.007 | 0.038 | 0.079 | 0.14 |
| Hungry | 0.141 | 0.559 | 0.039 | 0 | 0.039 | 0.116 |
| Thirsty | 0.076 | 0.559 | 0.1 | 0 | 0 | 0.079 |
| Urinate | 0 | 0.098 | 0.618 | 0.055 | 0 | 0.006 |
| Defecate | 0.011 | 0.082 | 0.618 | 0.086 | 0.085 | 0.104 |
| Sneeze | 0.005 | 0 | 0.138 | 0.627 | 0.013 | 0.101 |
| Cough | 0.055 | 0 | 0.008 | 0.627 | 0.148 | 0.146 |
| Wind | 0.036 | 0.047 | 0.08 | 0.02 | 0.604 | 0.11 |
| Burp | 0.109 | 0 | 0 | 0.126 | 0.604 | 0.225 |
| Bruise | 0.136 | 0 | 0 | 0.013 | 0.064 | 0.437 |
| Pain | 0.013 | 0.118 | 0.066 | 0.073 | 0.015 | 0.399 |
| Muscles | 0.152 | 0.047 | 0.018 | 0.08 | 0.145 | 0.284 |
| Blood_Sugar | 0.071 | 0.031 | 0 | 0.017 | 0.051 | 0.249 |
plot(rez_efa$n_fac)
rez_cfa6$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_egag | 20221.10 | 0.058 | 217.353 | 0.957 | 5.020192e+00 |
| fit_ega | 20224.33 | 0.046 | 130.586 | 0.978 | NA |
| fit_efag | 20516.73 | 0.101 | 538.689 | 0.865 | 3.212108e-64 |
| fit_efa | 20516.73 | 0.101 | 538.689 | 0.865 | 3.212108e-64 |
| fit_hclust | 20579.19 | 0.109 | 556.159 | 0.857 | 8.756386e-78 |
| fit_hclustg | 20587.13 | 0.108 | 596.235 | 0.847 | 1.656411e-79 |
| fit_g | 20813.21 | 0.128 | 854.452 | 0.773 | 1.340335e-128 |
rez_hclust <- make_hclust(data7a)
rez_ega <- make_ega(data7a, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Defecate"]], "C3" = colors[["Itch"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Bruise"]], "C7" = colors[["Pain"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data7a, n = 4, cols = c("F1" = colors[["Pain"]], "F2" = colors[["Sneeze"]],
"F3" = colors[["Burp"]], "F4" = colors[["Urinate"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Breathing | 0.493 | 0 | 7.190 × 10−4 | 0.083 | 0 | 0.146 |
| Thirsty | 0.349 | 0.119 | 0.053 | 0 | 0.018 | 0.061 |
| Hungry | 0.201 | 0.035 | 0.105 | −0.009 | 0.098 | 0.131 |
| Heart | 0.193 | 0 | 0 | 0 | 0.047 | 0.145 |
| Urinate | 0.095 | 0.572 | 0.119 | 0 | 0.078 | 0.049 |
| Defecate | 0.085 | 0.572 | 0.066 | 0.097 | 0.067 | 0.098 |
| Taste | 0.057 | 0.04 | 0.468 | 0.095 | 0.095 | 0.082 |
| Temp | 0.053 | 0.07 | 0.468 | 0.07 | 0.026 | 0.231 |
| Sneeze | −0.007 | 0 | 0 | 0.639 | 0.002 | 0.089 |
| Cough | −0.005 | 0 | 0.247 | 0.597 | 0.152 | 0.052 |
| Vomit | 0.111 | 0.11 | 0.068 | 0.223 | 0.18 | 0.021 |
| Wind | 0.043 | 0.037 | 0.048 | 0.074 | 0.656 | 0.056 |
| Burp | 0.012 | 0.003 | 0.077 | 0.169 | 0.463 | 0.158 |
| Sex_arousal | 0.134 | 0.104 | 0.076 | 0.048 | 0.193 | 0.126 |
| Muscles | 0.113 | 0.061 | 0.094 | 0.008 | 0.051 | 0.411 |
| Pain | 0.229 | 0.021 | 0.093 | 0.032 | 0.028 | 0.385 |
| Itch | 0.092 | 0.07 | 0.288 | 0.085 | 0.033 | 0.384 |
| Bruise | 0.054 | 0.006 | 0 | 0 | 0.13 | 0.352 |
| Affective_touch | 0.054 | 0.002 | 0.137 | 0.003 | 0.085 | 0.346 |
| Blood_Sugar | 0.115 | 0.011 | 0 | 0.039 | 0.072 | 0.232 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data7af)
rez_ega <- make_ega(data7af, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Pain"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Bruise"]], "C7" = colors[["Burp"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data7af, n = 4, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Pain"]],
"F3" = colors[["Sneeze"]], "F4" = colors[["Urinate"]]))
rez_cf7a <- make_cfa(data7af)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Breathing | 0.546 | 0.183 | 0 | 0.006 | 0.012 | 0.154 |
| Heart | 0.302 | 0.035 | 0 | 0 | 0.057 | 0.158 |
| Thirsty | 0.243 | 0.155 | 0.135 | 0.013 | 5.718 × 10−4 | 0.045 |
| Pain | 0.179 | 0.487 | 0.07 | 0.063 | 0 | 0.168 |
| Muscles | 0.065 | 0.441 | 0.079 | 0.023 | 0.041 | 0.321 |
| Hungry | 0.196 | 0.315 | 0.069 | 0 | 0 | 0 |
| Urinate | 0.087 | 0.082 | 0.585 | 0.015 | 0.068 | 0 |
| Defecate | 0.094 | 0.166 | 0.585 | 0.029 | 0.03 | 0.063 |
| Sneeze | 0 | 0.113 | 0.019 | 0.636 | 0.022 | 0 |
| Cough | 0.032 | 0.009 | 0.036 | 0.636 | 0.176 | 0.099 |
| Wind | 0.071 | 0.049 | 0.071 | 0.038 | 0.594 | 0.119 |
| Burp | 0.027 | 0 | 0.032 | 0.128 | 0.594 | 0.325 |
| Bruise | 0.085 | 0.204 | 0.014 | 0 | 0.134 | 0.441 |
| Blood_Sugar | 0.146 | 0.064 | 0.017 | 0.038 | 0.071 | 0.441 |
plot(rez_efa$n_fac)
rez_cf7a$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 17647.14 | 0.038 | 98.571 | 0.973 | NA |
| fit_egag | 17657.35 | 0.059 | 196.386 | 0.920 | 6.073383e-03 |
| fit_efa | 17782.60 | 0.084 | 346.672 | 0.827 | 3.842713e-30 |
| fit_efag | 17782.60 | 0.084 | 346.672 | 0.827 | 3.842713e-30 |
| fit_hclust | 17812.14 | 0.087 | 332.401 | 0.831 | 1.484630e-36 |
| fit_hclustg | 17819.37 | 0.089 | 370.921 | 0.810 | 3.993394e-38 |
| fit_g | 17990.36 | 0.111 | 573.200 | 0.685 | 2.959712e-75 |
rez_hclust <- make_hclust(data7b)
rez_ega <- make_ega(data7b, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Pain"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data7b, n = 4, cols = c("F1" = colors[["Wind"]], "F2" = colors[["Urinate"]],
"F3" = colors[["Heart"]], "F4" = colors[["Sneeze"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.56 | 0 | 0.026 | 0.019 | 0.052 | 0.03 | 0.208 |
| Breathing | 0.56 | 0.194 | 0.02 | 0.041 | 0.127 | 0.01 | 0.008 |
| Hungry | 0.047 | 0.477 | 0.07 | 0.007 | 0.089 | 0 | 0.091 |
| Thirsty | 0.064 | 0.376 | 0.124 | 0.013 | 0.013 | 0.023 | 0.046 |
| Sex_arousal | 0.044 | 0.203 | 0.09 | 0.092 | 0.174 | 0.128 | 0 |
| Defecate | 0.009 | 0.122 | 0.601 | 0.11 | 0.067 | 0 | 0 |
| Urinate | 0.013 | 0.184 | 0.537 | 5.716 × 10−4 | 0.133 | 0 | 0 |
| Taste | 0.028 | 0.077 | 0.201 | 0.038 | 0.199 | 0.071 | 0.14 |
| Sneeze | 0 | 0.01 | 0.037 | 0.579 | 0.143 | 0.08 | 0 |
| Cough | 0.033 | 0.054 | 0.045 | 0.474 | 0.122 | 0.163 | 0 |
| Vomit | 0.033 | 0.089 | 0.067 | 0.296 | 0.045 | 0.078 | 0.007 |
| Itch | 0.033 | 0.016 | 0.113 | 0.123 | 0.505 | 0.003 | 0 |
| Pain | 0.054 | 0.102 | 0.077 | 0.045 | 0.399 | 0.035 | 0.194 |
| Muscles | 0.064 | 0.075 | 0.059 | 0.035 | 0.32 | 0.105 | 0.146 |
| Temp | 0.025 | 0.102 | 0.118 | 0.11 | 0.318 | 0.036 | 0 |
| Affective_touch | 0.031 | 0.107 | 0.064 | 0.018 | 0.244 | 0.05 | 0.106 |
| Wind | 0.011 | 0.097 | 0.027 | 0.081 | 0.116 | 0.548 | 0.203 |
| Burp | 0.026 | 0.081 | 0.036 | 0.199 | 0.069 | 0.548 | 0.167 |
| Bruise | 0.011 | 0.062 | 0.008 | 0 | 0.188 | 0.172 | 0.449 |
| Blood_Sugar | 0.111 | 0.036 | 0.066 | 0.004 | 0.029 | 0.049 | 0.449 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data7bf)
rez_ega <- make_ega(data7bf, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data7bf, n = 3, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Urinate"]],
"F3" = colors[["Breathing"]]))
rez_cfa7b <- make_cfa(data7bf)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.616 | 0.002 | 0.012 | 0.018 | 0.04 | 0.043 |
| Breathing | 0.44 | 0.168 | 0.032 | 0.039 | 0.018 | 0.156 |
| Blood_Sugar | 0.187 | 0.054 | 0.011 | 0 | 0.071 | 0.153 |
| Hungry | 0.097 | 0.495 | 0.059 | 0 | 0.021 | 0.162 |
| Thirsty | 0.069 | 0.495 | 0.107 | 0.02 | 0.033 | 0.041 |
| Urinate | 0.023 | 0.179 | 0.604 | 0.027 | 0.017 | 0.108 |
| Defecate | 0.045 | 0.098 | 0.604 | 0.102 | 0.029 | 0.073 |
| Sneeze | 0.01 | 0 | 0.059 | 0.578 | 0.112 | 0.128 |
| Cough | 0.054 | 0.03 | 0.056 | 0.578 | 0.185 | 0.093 |
| Wind | 0.081 | 0.018 | 0.031 | 0.076 | 0.557 | 0.189 |
| Burp | 0.048 | 0.056 | 0.006 | 0.195 | 0.557 | 0.206 |
| Pain | 0.089 | 0.147 | 0.068 | 0.11 | 0.058 | 0.437 |
| Muscles | 0.088 | 0.036 | 0.064 | 0.074 | 0.138 | 0.387 |
| Bruise | 0.149 | 0.072 | 0.003 | 7.260 × 10−4 | 0.17 | 0.347 |
plot(rez_efa$n_fac)
rez_cfa7b$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 68203.78 | 0.033 | 178.485 | 0.982 | NA |
| fit_egag | 68259.45 | 0.044 | 340.521 | 0.959 | 8.144885e-13 |
| fit_efa | 68668.80 | 0.069 | 780.259 | 0.893 | 1.051831e-101 |
| fit_efag | 68668.80 | 0.069 | 780.259 | 0.893 | 1.051831e-101 |
| fit_hclustg | 68700.47 | 0.071 | 796.735 | 0.891 | 1.395370e-108 |
| fit_hclust | 68709.50 | 0.072 | 767.779 | 0.894 | 1.526592e-110 |
| fit_g | 69302.69 | 0.094 | 1,436.940 | 0.795 | 2.369973e-239 |
rez_hclust <- make_hclust(data7c)
rez_ega <- make_ega(data7c, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Pain"]],
"C5" = colors[["Cough"]], "C6" = colors[["Sex arousal"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data7c, n = 3, cols = c("F1" = colors[["Cough"]], "F2" = colors[["Urinate"]],
"F3" = colors[["Bruise"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| Heart | 0.521 | 0 | 0.045 | 0.039 | 0.009 | 0 | 0.053 | 0.117 |
| Breathing | 0.521 | 0.159 | 0.059 | 0.201 | 0.036 | 0.007 | 0 | 0.025 |
| Hungry | 0.074 | 0.54 | 0.09 | 0.09 | 0 | 0.124 | 0 | 0 |
| Thirsty | 0.101 | 0.54 | 0.082 | 0.04 | 0 | 0.056 | 0.046 | 0.022 |
| Urinate | 0.111 | 0.125 | 0.555 | 0.076 | 0.015 | 0.065 | 0.026 | 0 |
| Defecate | 0.011 | 0.061 | 0.555 | 0.127 | 0.087 | 0.122 | 0.067 | 0 |
| Pain | 0.068 | 0.078 | 0.065 | 0.513 | 0 | 0.144 | 0.015 | 0.095 |
| Temp | 0 | 0.034 | 0.061 | 0.353 | 0.09 | 0.138 | 0.046 | 0.029 |
| Taste | 0.101 | 0.032 | 0.083 | 0.281 | 0.063 | 0.074 | 0 | 0 |
| Muscles | 0.125 | 0 | 0 | 0.241 | 0.001 | 0.102 | 0.055 | 0.146 |
| Sneeze | 0.045 | 0 | 0.017 | 0.011 | 0.648 | 0.034 | 0.121 | 0 |
| Cough | 0.014 | 0 | 0 | 0.15 | 0.484 | 0 | 0.136 | 0.138 |
| Vomit | 0.005 | 0 | 0.107 | 0.021 | 0.318 | 0.007 | 0.04 | 0.009 |
| Sex_arousal | 0 | 0.116 | 0.138 | 0.142 | 0.004 | 0.494 | 0.097 | 0 |
| Affective_touch | 0.006 | 0.027 | 0 | 0.186 | 0.02 | 0.494 | 0 | 0.124 |
| Wind | 0.069 | 0.029 | 0.069 | 0.062 | 0.147 | 0.052 | 0.577 | 0.001 |
| Burp | 0 | 0.026 | 0.034 | 0.062 | 0.123 | 0.093 | 0.577 | 0.138 |
| Bruise | 0.048 | 0.022 | 0 | 0.211 | 0.031 | 0 | 0.069 | 0.455 |
| Blood_Sugar | 0.1 | 0 | 0 | 0.031 | 0.005 | 0 | 0 | 0.374 |
| Itch | 0.011 | 0 | 0 | 0.003 | 0.076 | 0.157 | 0.049 | 0.351 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data7cf)
rez_ega <- make_ega(data7cf, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data7cf, n = 4, cols = c("F1" = colors[["Cough"]], "F2" = colors[["Hungry"]],
"F3" = colors[["Bruise"]], "F4" = colors[["Breathing"]]))
rez_cfa7c <- make_cfa(data7cf)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.522 | 0 | 0.047 | 0.01 | 0.054 | 0.032 | 0.15 |
| Breathing | 0.522 | 0.164 | 0.066 | 0.047 | 0 | 0.233 | 0.038 |
| Hungry | 0.081 | 0.544 | 0.114 | 0 | 0.013 | 0.116 | 0 |
| Thirsty | 0.102 | 0.544 | 0.085 | 0 | 0.058 | 0.037 | 0.041 |
| Urinate | 0.124 | 0.142 | 0.568 | 0.011 | 0.038 | 0.021 | 0 |
| Defecate | 0.015 | 0.08 | 0.568 | 0.056 | 0.088 | 0.146 | 0 |
| Sneeze | 0.054 | 0 | 0.053 | 0.603 | 0.137 | 0.004 | 0 |
| Cough | 0.03 | 0 | 0.024 | 0.603 | 0.16 | 0.048 | 0.098 |
| Wind | 0.071 | 0.039 | 0.077 | 0.155 | 0.581 | 0.073 | 0 |
| Burp | 0 | 0.045 | 0.058 | 0.115 | 0.581 | 0.097 | 0.127 |
| Muscles | 0.133 | 0 | 0.007 | 0 | 0.076 | 0.489 | 0.206 |
| Pain | 0.091 | 0.116 | 0.107 | 0.03 | 0.033 | 0.489 | 0.148 |
| Bruise | 0.05 | 0.028 | 0 | 0.056 | 0.08 | 0.298 | 0.487 |
| Blood_Sugar | 0.107 | 0.003 | 0 | 0 | 0 | 0.051 | 0.487 |
plot(rez_efa$n_fac)
rez_cfa7c$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 23336.33 | 0.036 | 102.630 | 0.976 | NA |
| fit_egag | 23351.38 | 0.055 | 208.177 | 0.928 | 5.413702e-04 |
| fit_hclustg | 23444.76 | 0.072 | 314.491 | 0.873 | 2.853466e-24 |
| fit_hclust | 23462.50 | 0.074 | 299.902 | 0.878 | 4.020609e-28 |
| fit_efa | 23478.41 | 0.078 | 361.067 | 0.850 | 1.409458e-31 |
| fit_efag | 23478.41 | 0.078 | 361.067 | 0.850 | 1.409458e-31 |
| fit_g | 23705.76 | 0.104 | 607.808 | 0.723 | 6.042063e-81 |
rez_hclust <- make_hclust(data8a)
rez_ega <- make_ega(data8a, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Bruise"]], "C7" = colors[["Itch"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data8a, n = 5, cols = c("F1" = colors[["Cough"]], "F2" = colors[["Thirsty"]],
"F3" = colors[["Bruise"]], "F4" = colors[["Heart"]], "F5" = colors[["Urinate"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Breathing | 0.472 | 0.137 | 0.019 | 0.066 | 0.037 | 0.064 |
| Heart | 0.467 | 0.061 | 0 | 0.054 | 0 | 0.082 |
| Sex_arousal | 0.238 | 0.134 | 0.064 | 0.044 | 0.082 | 0.097 |
| Hungry | 0.155 | 0.454 | 0.095 | 0.027 | 0.049 | 0.016 |
| Thirsty | 0.01 | 0.405 | 0.118 | 0.041 | 0 | 0.102 |
| Temp | 0.12 | 0.337 | 0.028 | 0.067 | 0.008 | 0.137 |
| Taste | 0.072 | 0.158 | 0.025 | 0.113 | 0.09 | 0.137 |
| Urinate | 0.076 | 0.115 | 0.544 | 0.063 | 0 | 0.103 |
| Defecate | 0.008 | 0.136 | 0.544 | 0.13 | 0.08 | 0.066 |
| Sneeze | 0.03 | 0.084 | 0.068 | 0.545 | 0.208 | 0.07 |
| Cough | 0.053 | 0.147 | 0.052 | 0.4 | 0.285 | 0.013 |
| Vomit | 0.098 | 0.019 | 0.088 | 0.316 | 0.057 | 0.094 |
| Wind | 0.059 | 0.094 | 0.011 | 0.184 | 0.503 | 0.08 |
| Burp | 0.039 | 0.02 | 0.054 | 0.233 | 0.503 | 0.148 |
| Bruise | 0.075 | 0.004 | 0.01 | 0.029 | 0.041 | 0.479 |
| Itch | 0.107 | 0.081 | 0.007 | 0.05 | 0.046 | 0.396 |
| Affective_touch | 0.018 | 0.037 | 0 | 0.036 | 0.109 | 0.373 |
| Muscles | 0.031 | 0.109 | 0.009 | 0.007 | 0.095 | 0.319 |
| Blood_Sugar | 0.049 | 0.009 | 0.083 | 0.091 | 0.049 | 0.304 |
| Pain | 0.051 | 0.256 | 0.117 | 0.009 | 0.035 | 0.278 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data8af)
rez_ega <- make_ega(data8af, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Bruise"]], "C7" = colors[["Pain"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data8af, n = 4, cols = c("F1" = colors[["Cough"]], "F2" = colors[["Thirsty"]],
"F3" = colors[["Bruise"]], "F4" = colors[["Heart"]]))
rez_cfa8a <- make_cfa(data8af)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.531 | 0.072 | 0 | 0.045 | 0 | 0.128 |
| Breathing | 0.531 | 0.086 | 0.041 | 0.095 | 0.06 | 0.051 |
| Hungry | 0.146 | 0.533 | 0.107 | 0.052 | 0.059 | 0.044 |
| Thirsty | 0.014 | 0.533 | 0.122 | 0.063 | 0 | 0.138 |
| Urinate | 0.031 | 0.09 | 0.546 | 0.065 | 0.003 | 0.152 |
| Defecate | 0.013 | 0.154 | 0.546 | 0.102 | 0.089 | 0.09 |
| Sneeze | 0.063 | 0.032 | 0.096 | 0.533 | 0.238 | 0.099 |
| Cough | 0.078 | 0.082 | 0.06 | 0.533 | 0.301 | 0.02 |
| Wind | 0.054 | 0.053 | 0.018 | 0.199 | 0.509 | 0.095 |
| Burp | 0 | 0 | 0.059 | 0.28 | 0.509 | 0.162 |
| Bruise | 0.031 | 0 | 0.036 | 0 | 0.094 | 0.464 |
| Muscles | 0.043 | 0.013 | 0.015 | 0.026 | 0.121 | 0.401 |
| Blood_Sugar | 0.062 | 0 | 0.092 | 0.087 | 0.066 | 0.316 |
| Pain | 0.082 | 0.207 | 0.129 | 0.031 | 0.067 | 0.28 |
plot(rez_efa$n_fac)
rez_cfa8a$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 40816.35 | 0.033 | 128.727 | 0.976 | NA |
| fit_hclust | 40867.57 | 0.049 | 257.618 | 0.936 | 7.558221e-12 |
| fit_egag | 40871.15 | 0.051 | 282.380 | 0.929 | 1.263321e-12 |
| fit_hclustg | 40889.24 | 0.054 | 314.597 | 0.919 | 1.487098e-16 |
| fit_efag | 41018.42 | 0.067 | 457.898 | 0.872 | 1.322953e-44 |
| fit_efa | 41018.42 | 0.067 | 457.898 | 0.872 | 1.322953e-44 |
| fit_g | 41281.20 | 0.086 | 741.862 | 0.778 | 1.147181e-101 |
rez_hclust <- make_hclust(data8b)
rez_ega <- make_ega(data8b, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Cough"]],
"C5" = colors[["Pain"]], "C6" = colors[["Bruise"]], "C7" = colors[["Itch"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data8b, n = 5, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Thirsty"]],
"F3" = colors[["Breathing"]], "F4" = colors[["Bruise"]], "F5" = colors[["Urinate"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Breathing | 0.575 | 0.032 | 0 | 0.077 | 0 | 0.025 |
| Heart | 0.422 | 0.037 | 0 | 0.056 | 0.061 | 0.024 |
| Sex_arousal | 0.233 | 0.165 | 0.021 | 0.083 | 0 | 0.12 |
| Hungry | 0.124 | 0.469 | 0.039 | 0.099 | 0.058 | 0.02 |
| Thirsty | 0.013 | 0.392 | 0.124 | 0.023 | 0.15 | −0.066 |
| Temp | 0.08 | 0.377 | 2.195 × 10−4 | 0.05 | 0.093 | 0.033 |
| Taste | 0.038 | 0.22 | 0 | 0.113 | 0.188 | 0.083 |
| Urinate | 0.022 | 0.088 | 0.563 | 0.059 | 0.015 | 0.115 |
| Defecate | 0 | 0.07 | 0.563 | 0.197 | 0.125 | 0.009 |
| Cough | 0.062 | 0.124 | 0.06 | 0.465 | 0 | 0.03 |
| Burp | 0.035 | 0.052 | 0.092 | 0.444 | 0.018 | 0.124 |
| Sneeze | 0.07 | 0.081 | 0.09 | 0.404 | 0.052 | 0.091 |
| Wind | 0.051 | 0.02 | 0 | 0.38 | 0.155 | 0.018 |
| Vomit | 0.055 | 0.052 | 0.065 | 0.21 | 0.029 | 0.139 |
| Muscles | 0.025 | 0.091 | 0.017 | 0.06 | 0.468 | 0.192 |
| Pain | 0.015 | 0.199 | 0.07 | 0.07 | 0.468 | 0.07 |
| Affective_touch | 0 | 0.023 | 0.011 | 0.084 | 0.083 | 0.408 |
| Bruise | 0.025 | −0.006 | 0.027 | 0.041 | 0.17 | 0.4 |
| Blood_Sugar | 0.026 | −0.008 | 0.095 | 0.089 | 0.066 | 0.352 |
| Itch | 0.137 | 0.063 | 0 | 0.143 | 0.134 | 0.338 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data8bf)
rez_ega <- make_ega(data8bf, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Cough"]],
"C5" = colors[["Pain"]], "C6" = colors[["Bruise"]], "C7" = colors[["Itch"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data8bf, n = 4, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Heart"]],
"F3" = colors[["Thirsty"]], "F4" = colors[["Bruise"]]))
rez_cfa8b <- make_cfa(data8bf)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.551 | 0.054 | 0 | 0.053 | 0.078 | 0.051 |
| Breathing | 0.551 | 0.046 | 0 | 0.09 | 0.012 | 0 |
| Hungry | 0.075 | 0.538 | 0.045 | 0.115 | 0.101 | 0.058 |
| Thirsty | 0.019 | 0.538 | 0.13 | 0.035 | 0.161 | −0.107 |
| Urinate | 0 | 0.111 | 0.566 | 0.051 | 0.019 | 0.189 |
| Defecate | 0 | 0.088 | 0.566 | 0.173 | 0.127 | 0.022 |
| Burp | 0 | 0.036 | 0.094 | 0.499 | 0.05 | 0.116 |
| Cough | 0.083 | 0.097 | 0.067 | 0.473 | 0 | 0.051 |
| Wind | 0 | 0.014 | 0 | 0.409 | 0.18 | 0 |
| Sneeze | 0.102 | 0.06 | 0.11 | 0.359 | 0.088 | 0.017 |
| Muscles | 0.042 | 0.013 | 0.024 | 0.083 | 0.48 | 0.243 |
| Pain | 0.021 | 0.182 | 0.071 | 0.088 | 0.48 | 0.061 |
| Bruise | 0 | −0.034 | 0.044 | 0.031 | 0.241 | 0.499 |
| Blood_Sugar | 0.039 | −0.006 | 0.108 | 0.079 | 0.095 | 0.499 |
plot(rez_efa$n_fac)
rez_cfa8b$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_egag | 18012.76 | 0.048 | 151.514 | 0.932 | 1.159343e+04 |
| fit_ega | 18031.48 | 0.031 | 83.225 | 0.977 | NA |
| fit_hclustg | 18052.96 | 0.061 | 204.139 | 0.890 | 2.166613e-05 |
| fit_hclust | 18058.18 | 0.058 | 178.287 | 0.907 | 1.592270e-06 |
| fit_efa | 18123.87 | 0.076 | 287.481 | 0.822 | 8.654529e-21 |
| fit_efag | 18123.87 | 0.076 | 287.481 | 0.822 | 8.654529e-21 |
| fit_g | 18223.18 | 0.092 | 405.430 | 0.726 | 2.362503e-42 |
rez_hclust <- make_hclust(data9)
rez_ega <- make_ega(data9, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Cough"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data9, n = 3, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Thirsty"]],
"F3" = colors[["Bruise"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.556 | 0.036 | 0.007 | 0.051 | 0.037 | 0.007 | 0.102 |
| Breathing | 0.556 | 0.237 | 0.041 | 0.161 | 0.02 | 0 | 0 |
| Hungry | 0.15 | 0.536 | 0.068 | 0.097 | 0 | 0 | 0.036 |
| Thirsty | 0.098 | 0.536 | 0.128 | 0.102 | 0 | 0 | 0 |
| Urinate | 0.015 | 0.223 | 0.607 | 0.114 | 0.018 | 0.011 | 0 |
| Defecate | 0.045 | 0.047 | 0.607 | 0.129 | 0.098 | 0.04 | 0 |
| Pain | 0.026 | 0.087 | 0.037 | 0.426 | 0.032 | 0 | 0.164 |
| Temp | 0.012 | 0.073 | 0.078 | 0.405 | 0.13 | 0.018 | 0 |
| Muscles | 0.09 | 0.03 | 0.018 | 0.384 | 0.042 | 0.089 | 0.123 |
| Sex_arousal | 0.054 | 0.047 | 0.048 | 0.316 | 0.065 | 0.064 | 0 |
| Affective_touch | 0.018 | 0.015 | 0.01 | 0.285 | 0.034 | 0 | 0.2 |
| Taste | 0.06 | 0.016 | 0.047 | 0.219 | 0.112 | 0.016 | 0.059 |
| Sneeze | 0.03 | 0 | 0.028 | 0.081 | 0.643 | 0.063 | 0.04 |
| Cough | 0.024 | 0 | 7.041 × 10−4 | 0.14 | 0.466 | 0.133 | 0.062 |
| Vomit | 0.014 | 0 | 0.081 | 0.182 | 0.316 | 0.032 | 0.017 |
| Wind | 2.528 × 10−4 | 0 | 0.052 | 0.097 | 0.084 | 0.609 | 0.036 |
| Burp | 0.008 | 0 | 0 | 0.096 | 0.159 | 0.609 | 0.076 |
| Bruise | 0.01 | 0 | 0 | 0.183 | 0.023 | 0.05 | 0.444 |
| Blood_Sugar | 0.055 | 0.037 | 0 | 0.077 | 0.007 | 0.006 | 0.391 |
| Itch | 0.03 | 0 | 0 | 0.156 | 0.064 | 0.026 | 0.342 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data9f)
rez_ega <- make_ega(data9f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data9f, n = 3, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Urinate"]],
"F3" = colors[["Pain"]]))
rez_cfa9 <- make_cfa(data9f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.559 | 0.039 | 0.012 | 0.041 | 0.015 | 0.092 |
| Breathing | 0.559 | 0.244 | 0.051 | 0.033 | 0 | 0.104 |
| Hungry | 0.158 | 0.539 | 0.078 | 0.003 | 0 | 0.087 |
| Thirsty | 0.1 | 0.539 | 0.133 | 0.007 | 0 | 0.075 |
| Urinate | 0.02 | 0.232 | 0.613 | 0.03 | 0.02 | 0.06 |
| Defecate | 0.059 | 0.063 | 0.613 | 0.067 | 0.052 | 0.061 |
| Sneeze | 0.049 | 0.004 | 0.07 | 0.598 | 0.085 | 0.059 |
| Cough | 0.038 | 0.009 | 0.02 | 0.598 | 0.145 | 0.118 |
| Wind | 0.005 | 0 | 0.07 | 0.098 | 0.612 | 0.063 |
| Burp | 0.014 | 0 | 0.002 | 0.146 | 0.612 | 0.135 |
| Bruise | 0.017 | 0 | 0 | 0.036 | 0.059 | 0.477 |
| Pain | 0.039 | 0.115 | 0.07 | 0.048 | 0 | 0.461 |
| Muscles | 0.107 | 0.044 | 0.04 | 0.07 | 0.107 | 0.393 |
| Blood_Sugar | 0.063 | 0.046 | 0 | 0.019 | 0.014 | 0.256 |
plot(rez_efa$n_fac)
rez_cfa9$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 720615.2 | 0.041 | 2,068.422 | 0.980 | NA |
| fit_egag | 724475.5 | 0.063 | 6,068.587 | 0.941 | 0 |
| fit_hclust | 730031.8 | 0.089 | 11,594.850 | 0.886 | 0 |
| fit_hclustg | 731202.9 | 0.090 | 12,815.915 | 0.874 | 0 |
| fit_efag | 731887.4 | 0.091 | 13,520.449 | 0.867 | 0 |
| fit_efa | 731887.4 | 0.091 | 13,520.449 | 0.867 | 0 |
| fit_g | 743671.0 | 0.123 | 25,333.964 | 0.750 | 0 |
rez_hclust <- make_hclust(data10)
rez_ega <- make_ega(data10, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Bruise"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data10, n = 4, cols = c("F1" = colors[["Hungry"]], "F2" = colors[["Defecate"]],
"F3" = colors[["Itch"]], "F4" = colors[["Breathing"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||
| Item | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Breathing | 0.525 | 0.096 | 0.014 | 0.167 | 0.095 |
| Heart | 0.459 | 0.199 | 0.01 | 0.102 | 0.06 |
| Muscles | 0.357 | 0.185 | 0.043 | 0.108 | 0.088 |
| Hungry | 0.148 | 0.49 | 0.018 | 0.02 | 0.025 |
| Sex_arousal | 0.031 | 0.418 | 0.141 | 0 | 0.022 |
| Pain | 0.085 | 0.351 | 0.144 | 0 | 0 |
| Thirsty | 0.084 | 0.349 | 0.18 | 0.02 | 0.03 |
| Temp | 0.113 | 0.329 | 0.047 | 0.16 | 0.105 |
| Affective_touch | 0.099 | 0.234 | 0.014 | 0.031 | 0.154 |
| Defecate | 0.032 | 0.085 | 0.457 | 0.356 | 0.046 |
| Urinate | 0.006 | 0.258 | 0.42 | 0.093 | 0.032 |
| Vomit | 0.024 | 0.084 | 0.37 | 0.251 | 0.125 |
| Sneeze | 0.136 | 0.039 | 0.209 | 0.462 | 0.039 |
| Burp | 0.076 | 0.074 | 0.222 | 0.462 | 0.119 |
| Itch | 0.091 | 0.058 | 0.052 | 0.087 | 0.474 |
| Bruise | 0.039 | 0.055 | 0.072 | 0.012 | 0.363 |
| Wind | 0.052 | 0.103 | 0.043 | 0.111 | 0.23 |
plot(rez_efa$n_fac)
Note: No CFA (missing items)
rez_hclust <- make_hclust(data10f)
rez_ega <- make_ega(data10f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Wind"]],
"C5" = colors[["Cough"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data10f, n = 3, cols = c("F1" = colors[["Breathing"]], "F2" = colors[["Defecate"]],
"F3" = colors[["Hungry"]]))
# rez_cfa10 <- make_cfa(data10f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||
| Item | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| Breathing | 0.524 | 0.081 | 0.108 | 0.243 |
| Heart | 0.47 | 0.172 | 0.083 | 0.147 |
| Muscles | 0.372 | 0.175 | 0.108 | 0.096 |
| Hungry | 0.168 | 0.616 | 0.056 | 0.019 |
| Thirsty | 0.105 | 0.377 | 0.182 | 0.155 |
| Pain | 0.124 | 0.297 | 0.138 | 0.073 |
| Defecate | 0.049 | 0.089 | 0.528 | 0.099 |
| Sneeze | 0.149 | 0 | 0.391 | 0.153 |
| Burp | 0.103 | 0.06 | 0.364 | 0.184 |
| Urinate | 0.015 | 0.278 | 0.337 | 0.118 |
| Wind | 0.079 | 0.042 | 0.123 | 0.378 |
| Bruise | 0.079 | 0.045 | 0.049 | 0.378 |
plot(rez_efa$n_fac)
# rez_cfa10$testrez_hclust <- make_hclust(data11)
rez_ega <- make_ega(data11, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Bruise"]],
"C5" = colors[["Cough"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data11, n = 4, cols = c("F1" = colors[["Hungry"]], "F2" = colors[["Wind"]],
"F3" = colors[["Bruise"]], "F4" = colors[["Breathing"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.491 | 0.061 | 0.08 | 0.104 | 0.107 | 0.117 |
| Breathing | 0.491 | 0.101 | 0.021 | 0.178 | 0.067 | 0.194 |
| Hungry | 0 | 0.492 | 0.122 | 0.141 | 0.012 | 0.21 |
| Thirsty | 0.162 | 0.492 | 0.14 | 0.094 | 0.071 | 0.198 |
| Defecate | 0.036 | 0 | 0.477 | 0.044 | 0.113 | 0.066 |
| Wind | 0 | 0.012 | 0.404 | 0.095 | 0.257 | 0.072 |
| Urinate | 0.009 | 0.212 | 0.285 | 0.075 | 0.033 | 0.098 |
| Sex_arousal | 0.046 | 0.171 | 0.274 | 0.098 | 0.02 | 0.197 |
| Vomit | 0.02 | 0.037 | 0.274 | 0.093 | 0.08 | 0.084 |
| Burp | 0.055 | 0 | 0.265 | 0.188 | 0.248 | 0.017 |
| Bruise | 0.074 | 0.052 | 0.101 | 0.389 | 0.052 | 6.507 × 10−4 |
| Itch | 0.034 | 0.044 | 0.101 | 0.308 | 0.226 | 0.037 |
| Blood_Sugar | 0.104 | 0.072 | 0.003 | 0.26 | 0.016 | 0.019 |
| Affective_touch | 0.069 | 0.156 | 0.166 | 0.252 | 0.026 | 0.079 |
| Taste | 0.107 | 0 | 0.122 | 0.223 | 0.186 | 0.191 |
| Sneeze | 0.081 | 0.062 | 0.198 | 0.138 | 0.441 | 0.006 |
| Cough | 0.051 | 0 | 0.145 | 0.14 | 0.441 | 0.172 |
| Pain | 0.07 | 0.218 | 0.099 | 0.098 | 0.051 | 0.404 |
| Muscles | 0.163 | 0.02 | 0.143 | 0.114 | 0.207 | 0.355 |
| Temp | 0.136 | 0.245 | 0.141 | 0.068 | 0.022 | 0.346 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data11f)
rez_ega <- make_ega(data11f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Bruise"]],
"C5" = colors[["Cough"]], "C6" = colors[["Wind"]], "C7" = colors[["Pain"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data11f, n = 3, cols = c("F1" = colors[["Wind"]], "F2" = colors[["Hungry"]],
"F3" = colors[["Breathing"]]))
rez_cfa11 <- make_cfa(data11f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||
| Item | 1 | 2 | 3 | 4 |
|---|---|---|---|---|
| Breathing | 0.453 | 0.147 | 0.07 | 0.169 |
| Heart | 0.44 | 0.077 | 0.119 | 0.082 |
| Muscles | 0.257 | 0.173 | 0.251 | 0.047 |
| Hungry | 0.007 | 0.494 | 0.049 | 0.14 |
| Thirsty | 0.221 | 0.485 | 0.058 | 0.081 |
| Pain | 0.257 | 0.318 | 0.078 | 0.02 |
| Urinate | 0.032 | 0.312 | 0.211 | 0.015 |
| Wind | 0.084 | 0.087 | 0.476 | 0.086 |
| Burp | 0.109 | 0.021 | 0.386 | 0.106 |
| Sneeze | 0.099 | 0.063 | 0.373 | 0.044 |
| Cough | 0.187 | 0.065 | 0.32 | 0.068 |
| Defecate | 0.114 | 0.174 | 0.299 | 0 |
| Bruise | 0.077 | 0.077 | 0.14 | 0.463 |
| Blood_Sugar | 0.127 | 0.057 | 0.016 | 0.463 |
plot(rez_efa$n_fac)
rez_cfa11$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_hclustg | 29002.67 | 0.057 | 268.750 | 0.919 | 3.113615e+04 |
| fit_egag | 29008.58 | 0.057 | 261.203 | 0.922 | 1.621090e+03 |
| fit_hclust | 29010.84 | 0.056 | 243.273 | 0.928 | 5.246595e+02 |
| fit_ega | 29023.36 | 0.052 | 181.784 | 0.948 | NA |
| fit_efa | 29055.99 | 0.065 | 335.520 | 0.893 | 8.250859e-08 |
| fit_efag | 29055.99 | 0.065 | 335.520 | 0.893 | 8.250859e-08 |
| fit_g | 29173.20 | 0.078 | 472.916 | 0.838 | 2.914263e-33 |
rez_hclust <- make_hclust(data12)
rez_ega <- make_ega(data12, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Bruise"]],
"C5" = colors[["Cough"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data12, n = 2, cols = c("F1" = colors[["Urinate"]], "F2" = colors[["Bruise"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Breathing | 0.416 | 0.161 | 0.053 | 0.152 | 0.003 | 0.122 |
| Muscles | 0.362 | 0.05 | 0.155 | 0 | 0.088 | 0.115 |
| Heart | 0.264 | 0.016 | 0.089 | 0.02 | 0.046 | 0.052 |
| Affective_touch | 0.148 | 0.091 | 0.092 | 0.057 | 0.043 | 0 |
| Hungry | 0.025 | 0.367 | 0.177 | 0.015 | 0.014 | 0.197 |
| Burp | 0.09 | 0.347 | 0.066 | 0 | 0.144 | 0 |
| Temp | 0.205 | 0.314 | 0.156 | 0.021 | 0.108 | 0.119 |
| Blood_Sugar | 0.01 | 0.212 | 0 | 0.029 | 0.038 | 0 |
| Urinate | 0.118 | 0.161 | 0.552 | 0.01 | 0.188 | 0.015 |
| Wind | 0.126 | 0.089 | 0.262 | 0 | 0.117 | 0 |
| Defecate | 0.018 | 0.065 | 0.246 | 0.093 | 0.137 | 0.154 |
| Thirsty | 0.159 | 0.1 | 0.236 | 0.077 | 0.027 | 0.099 |
| Itch | 0.066 | 0 | 0.01 | 0.535 | 0.1 | 0.035 |
| Taste | 0.142 | 0.021 | 0.117 | 0.352 | 0.025 | 0.15 |
| Bruise | 0.03 | 0.044 | 0.047 | 0.286 | 0.044 | 0.125 |
| Sneeze | 0.09 | 0.138 | 0.295 | 0 | 0.554 | 0.019 |
| Cough | 0.083 | 0.095 | 0.016 | 0.134 | 0.348 | 0.03 |
| Vomit | 0 | 0.049 | 0.109 | 0.023 | 0.206 | 0.082 |
| Sex_arousal | 0.217 | 0 | 0.17 | 0.116 | 0.09 | 0.516 |
| Pain | 0.051 | 0.281 | 0.06 | 0.16 | 0.035 | 0.516 |
plot(rez_efa$n_fac)
Note: CFA errors.
rez_hclust <- make_hclust(data12f)
rez_ega <- make_ega(data8af, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Bruise"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data12f, n = 5, cols = c("F1" = colors[["Urinate"]], "F2" = colors[["Burp"]],
"F3" = colors[["Heart"]], "F4" = colors[["Pain"]], "F5" = colors[["Sneeze"]]))
# rez_cfa12 <- make_cfa(data12f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.531 | 0.072 | 0 | 0.045 | 0 | 0.128 |
| Breathing | 0.531 | 0.086 | 0.041 | 0.095 | 0.06 | 0.051 |
| Hungry | 0.146 | 0.533 | 0.107 | 0.052 | 0.059 | 0.044 |
| Thirsty | 0.014 | 0.533 | 0.122 | 0.063 | 0 | 0.138 |
| Urinate | 0.031 | 0.09 | 0.546 | 0.065 | 0.003 | 0.152 |
| Defecate | 0.013 | 0.154 | 0.546 | 0.102 | 0.089 | 0.09 |
| Sneeze | 0.063 | 0.032 | 0.096 | 0.533 | 0.238 | 0.099 |
| Cough | 0.078 | 0.082 | 0.06 | 0.533 | 0.301 | 0.02 |
| Wind | 0.054 | 0.053 | 0.018 | 0.199 | 0.509 | 0.095 |
| Burp | 0 | 0 | 0.059 | 0.28 | 0.509 | 0.162 |
| Bruise | 0.031 | 0 | 0.036 | 0 | 0.094 | 0.464 |
| Muscles | 0.043 | 0.013 | 0.015 | 0.026 | 0.121 | 0.401 |
| Blood_Sugar | 0.062 | 0 | 0.092 | 0.087 | 0.066 | 0.316 |
| Pain | 0.082 | 0.207 | 0.129 | 0.031 | 0.067 | 0.28 |
plot(rez_efa$n_fac)
# rez_cfa12$testrez_hclust <- make_hclust(data13)
rez_ega <- make_ega(data13, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Sex arousal"]], "C6" = colors[["Sneeze"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data13, n = 4, cols = c("F1" = colors[["Thirsty"]], "F2" = colors[["Heart"]],
"F3" = colors[["Burp"]], "F4" = colors[["Bruise"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.621 | 0.033 | 0 | 0.154 | 0.009 | 0.029 | 0.084 |
| Breathing | 0.621 | 0 | 0.038 | 0.177 | 0.015 | 0.039 | 0.044 |
| Hungry | 0 | 0.514 | 0.151 | 0.089 | 0.123 | 0.072 | 0.046 |
| Thirsty | 0.02 | 0.514 | 0.145 | 0.228 | 0.083 | 0.015 | 0.023 |
| Urinate | 0 | 0.342 | 0.544 | 0.108 | 0.053 | 0.069 | 0 |
| Defecate | 0.027 | 0 | 0.544 | 0.1 | 0.285 | 0.129 | 0 |
| Taste | 0.061 | 0.009 | 0.07 | 0.391 | 0.11 | 0.084 | 0.092 |
| Pain | 0.05 | 0.15 | 0.003 | 0.358 | 0.209 | 0.029 | 0 |
| Temp | 0.07 | 0.147 | 0.136 | 0.335 | 0.145 | 0.033 | 0.001 |
| Muscles | 0.069 | 0.085 | 0.014 | 0.274 | 0.146 | 0.064 | 0.05 |
| Affective_touch | 0.014 | 0.085 | 0.027 | 0.186 | 0.41 | 0.001 | 0.042 |
| Sex_arousal | 0 | 0.118 | 0.164 | 0.219 | 0.397 | 0.035 | 0 |
| Vomit | 0 | 0 | 0.097 | 0.083 | 0.24 | 0.14 | 0.038 |
| Burp | 0.016 | 0.069 | 0.017 | 0.033 | 0.102 | 0.608 | 0.015 |
| Sneeze | 0.031 | 0 | 0.101 | 0.115 | 0.082 | 0.461 | 0.011 |
| Wind | 0 | 0.05 | 0.143 | 0.037 | 0.036 | 0.363 | 0.012 |
| Cough | 0.019 | 0.018 | 0.007 | 0.081 | 0.059 | 0.321 | 0.119 |
| Bruise | 0 | 0.055 | 0 | 0.002 | 0.06 | 0.059 | 0.581 |
| Itch | 0.006 | 0 | 0 | 0.13 | 0 | 0.058 | 0.356 |
| Blood_Sugar | 0.085 | 0.024 | 0 | 0.002 | 0.034 | 0 | 0.253 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data13f)
rez_ega <- make_ega(data13f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Bruise"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data13f, n = 4, cols = c("F1" = colors[["Thirsty"]], "F2" = colors[["Heart"]],
"F3" = colors[["Burp"]], "F4" = colors[["Bruise"]]))
rez_cfa13 <- make_cfa(data13f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||
| Item | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Heart | 0.625 | 0.162 | 0.005 | 0.031 | 0.119 |
| Breathing | 0.625 | 0.089 | 0.057 | 0.06 | 0.068 |
| Thirsty | 0.034 | 0.536 | 0.154 | 0.024 | 0.051 |
| Hungry | 0 | 0.367 | 0.176 | 0.085 | 0.088 |
| Pain | 0.079 | 0.359 | 0.074 | 0.055 | 0 |
| Muscles | 0.091 | 0.235 | 0.068 | 0.106 | 0.017 |
| Urinate | 0.004 | 0.31 | 0.562 | 0.088 | 0 |
| Defecate | 0.043 | 0.13 | 0.562 | 0.166 | 0 |
| Burp | 0.025 | 0.064 | 0.043 | 0.622 | 0 |
| Sneeze | 0.036 | 0.09 | 0.115 | 0.472 | 0.042 |
| Wind | 0 | 0.096 | 0.138 | 0.361 | 0 |
| Cough | 0.026 | 0.068 | 0.025 | 0.336 | 0.138 |
| Bruise | 0.008 | 0.061 | 0 | 0.094 | 0.479 |
| Blood_Sugar | 0.086 | 0.035 | 0 | 0 | 0.479 |
plot(rez_efa$n_fac)
rez_cfa13$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_hclust | 25894.83 | 0.049 | 184.972 | 0.955 | 8.984228e+00 |
| fit_ega | 25899.23 | 0.038 | 116.734 | 0.977 | NA |
| fit_hclustg | 25903.37 | 0.054 | 226.518 | 0.942 | 1.260075e-01 |
| fit_egag | 25965.74 | 0.063 | 275.684 | 0.922 | 3.602767e-15 |
| fit_efag | 26137.94 | 0.086 | 474.294 | 0.849 | 1.458637e-52 |
| fit_efa | 26137.94 | 0.086 | 474.294 | 0.849 | 1.458637e-52 |
| fit_g | 26365.27 | 0.107 | 721.431 | 0.757 | 6.310507e-102 |
rez_hclust <- make_hclust(data14)
rez_ega <- make_ega(data14, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Cough"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data14, n = 7, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Thirsty"]],
"F3" = colors[["Bruise"]], "F4" = colors[["Temp"]],
"F5" = colors[["Pain"]], "F6" = colors[["Sneeze"]],
"F7" = colors[["Heart"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
| Heart | 0.557 | 0 | 0 | 0 | 0.142 | 0 | 0 | 0 | 0 |
| Breathing | 0.557 | 0.052 | 0.026 | 0.093 | 0 | 0.062 | 0.12 | 0 | 0.138 |
| Thirsty | 0 | 0.632 | 0.114 | 0.106 | 0 | 0.027 | 0.026 | 0.044 | 0.113 |
| Hungry | 0.034 | 0.351 | 0.011 | 0.031 | 0.08 | 0.016 | 0.009 | 0 | 0 |
| Taste | 0.019 | 0.28 | 0.043 | 0 | 0.208 | 0.041 | 0.089 | 0 | 0.172 |
| Urinate | 0 | 0.149 | 0.6 | 0.167 | 0 | 0.028 | 0 | 0 | 0 |
| Defecate | 0.032 | 0.052 | 0.6 | 0.001 | 0 | 0.072 | 0.152 | −0.043 | 0 |
| Sneeze | 0 | 0 | 0 | 0.565 | 0.298 | 0 | 0.046 | 0 | 0.215 |
| Vomit | 0.022 | 0.098 | 9.556 × 10−4 | 0.322 | 0.048 | 0 | 0 | 0 | 0.038 |
| Sex_arousal | 0.066 | 0.029 | 0.13 | 0.302 | 0.049 | 0.14 | 0 | 0 | 0 |
| Cough | 0.103 | 0 | 0 | 0.303 | 0.491 | 0.158 | 0.113 | 0.102 | 0.082 |
| Temp | 0 | 0.205 | 0 | 0 | 0.491 | 0 | 0 | 0 | 0.11 |
| Wind | 0 | 0.081 | 0.026 | 0.161 | 0.137 | 0.574 | 0.065 | 0 | 0.089 |
| Burp | 0.067 | 0.008 | 0.063 | 0 | 0.1 | 0.574 | 0 | 0.03 | 0 |
| Muscles | 0.123 | 0 | 0.037 | 0.053 | 0.168 | 0.03 | 0.573 | 0 | 0 |
| Pain | 0.006 | 0.131 | 0.097 | 0 | 0 | 0.034 | 0.573 | 0.054 | 0.069 |
| Bruise | 0 | 0.027 | −0.022 | 0 | 0.052 | 0 | 0.031 | 0.463 | 0.092 |
| Itch | 0 | 0 | 0 | 0 | 0.035 | 0.017 | 0 | 0.463 | 0 |
| Blood_Sugar | 0 | 0.15 | 0 | 0.025 | 0.078 | 0.052 | 0 | 0.094 | 0.465 |
| Affective_touch | 0.087 | 0.026 | 0 | 0.143 | 0.089 | 0 | 0.041 | 0 | 0.465 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data14f)
rez_ega <- make_ega(data14f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Wind"]], "C6" = colors[["Pain"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data14f, n = 5, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Defecate"]],
"F3" = colors[["Thirsty"]], "F4" = colors[["Heart"]],
"F5" = colors[["Muscles"]]))
rez_cfa14 <- make_cfa(data14f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Heart | 0.568 | 0 | 0 | 0.11 | 0 | 0 |
| Breathing | 0.568 | 0.055 | 0.045 | 0.017 | 0.078 | 0.132 |
| Thirsty | 0 | 0.57 | 0.132 | 0 | 0.05 | 0.063 |
| Hungry | 0.052 | 0.364 | 0.018 | 0.024 | 0.025 | 0.011 |
| Blood_Sugar | 0 | 0.257 | 0 | 0.094 | 0.07 | 0 |
| Bruise | 0 | 0.164 | −0.051 | 0.054 | 0 | 0.054 |
| Urinate | 0.015 | 0.192 | 0.613 | 0.013 | 0.052 | 0 |
| Defecate | 0.04 | −0.065 | 0.613 | −5.199 × 10−4 | 0.086 | 0.165 |
| Sneeze | 0 | 0.025 | 0.031 | 0.568 | 0.011 | 0.072 |
| Cough | 0.126 | 0.155 | −0.021 | 0.568 | 0.188 | 0.116 |
| Wind | 0.014 | 0.163 | 0.051 | 0.137 | 0.583 | 0.066 |
| Burp | 0.069 | 0 | 0.071 | 0.077 | 0.583 | 0 |
| Muscles | 0.124 | 0 | 0.039 | 0.192 | 0.025 | 0.58 |
| Pain | 0.016 | 0.143 | 0.104 | 0.007 | 0.04 | 0.58 |
plot(rez_efa$n_fac)
rez_cfa14$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_egag | 3941.802 | 0.061 | 97.490 | 0.929 | 1.928811e+07 |
| fit_hclustg | 3972.347 | 0.092 | 137.380 | 0.831 | 4.494018e+00 |
| fit_ega | 3975.352 | 0.040 | 65.620 | 0.975 | NA |
| fit_hclust | 3983.183 | 0.090 | 124.852 | 0.851 | 1.993355e-02 |
| fit_efa | 3985.451 | 0.104 | 159.830 | 0.779 | 6.413983e-03 |
| fit_efag | 3985.451 | 0.104 | 159.830 | 0.779 | 6.413983e-03 |
| fit_g | 4030.449 | 0.131 | 218.847 | 0.634 | 1.085985e-12 |
rez_hclust <- make_hclust(data15)
rez_ega <- make_ega(data15, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Sex arousal"]], "C4" = colors[["Sneeze"]],
"C5" = colors[["Muscles"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data15, n = 3, cols = c("F1" = colors[["Sneeze"]], "F2" = colors[["Hungry"]],
"F3" = colors[["Bruise"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Breathing | 0.604 | 0.184 | 0 | 0 | 0.029 | 0.037 | 0.106 |
| Hungry | 0.425 | 0.236 | 0.028 | 0 | 0.004 | 0 | 0.064 |
| Heart | 0.352 | 0.024 | 0.219 | 0.055 | 0 | 0 | 0.081 |
| Pain | 0.066 | 0.491 | 0.389 | 0 | 0.186 | 0 | 0.177 |
| Urinate | 0.105 | 0.425 | 0.031 | 0 | 0.156 | 0 | −0.06 |
| Defecate | 0.069 | 0.328 | 0.142 | 0.145 | 0 | 0 | −0.049 |
| Thirsty | 0.207 | 0.314 | 0 | −0.067 | 0.056 | 0.06 | 0.042 |
| Taste | 0.028 | 0.122 | 0.438 | 0.174 | 0 | 0 | 0.063 |
| Sex_arousal | 0.089 | 0.142 | 0.438 | 0.057 | 0.055 | 0.176 | 0.047 |
| Sneeze | 0 | 0.15 | 0.124 | 0.716 | 0.076 | 0 | 0 |
| Vomit | 0.019 | 0 | 0.196 | 0.313 | 0.121 | 0.126 | 0.055 |
| Cough | 0.038 | 0 | 0.13 | 0.3 | 0.09 | 0.211 | 0 |
| Itch | 0 | −0.069 | 0.059 | 0.288 | 0.014 | 0.21 | 0.102 |
| Temp | 0 | 0.196 | 0.096 | 0.272 | 0.543 | 0.052 | 0 |
| Muscles | 0.027 | 0.13 | 0 | −0.033 | 0.543 | 0.116 | 0.291 |
| Wind | 0 | 0.02 | 0.293 | 0.198 | 0.02 | 0.533 | 0 |
| Burp | 0.03 | 0.026 | 0 | 0.216 | 0.14 | 0.533 | 0.176 |
| Blood_Sugar | 0 | −0.081 | 0 | 0 | 0.139 | 0 | 0.479 |
| Bruise | 0.126 | 0.04 | 0 | 0.019 | 0.115 | 0.134 | 0.369 |
| Affective_touch | 0.06 | 0.122 | 0.172 | 0.094 | 0.008 | 0.032 | 0.242 |
plot(rez_efa$n_fac)
Note: CFA errors.
rez_hclust <- make_hclust(data15f)
rez_ega <- make_ega(data15f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Sneeze"]], "C4" = colors[["Wind"]],
"C5" = colors[["Bruise"]], "C6" = colors[["Wind"]], "C7" = colors[["Pain"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data15f, n = 3, cols = c("F1" = colors[["Hungry"]], "F2" = colors[["Burp"]],
"F3" = colors[["Blood Sugar"]]))
# rez_cfa15 <- make_cfa(data15f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||
| Item | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Breathing | 0.606 | 0.181 | 0 | 0.036 | 0.119 |
| Hungry | 0.428 | 0.231 | 0 | 0 | 0.058 |
| Heart | 0.368 | 0.067 | 0.075 | 0.042 | 0 |
| Pain | 0.104 | 0.579 | 0 | 0.079 | 0.173 |
| Urinate | 0.119 | 0.453 | 0.027 | 0 | 0.032 |
| Defecate | 0.078 | 0.345 | 0.223 | 0.009 | −0.043 |
| Thirsty | 0.203 | 0.292 | 0 | 0.035 | 0.074 |
| Sneeze | 0 | 0.22 | 0.571 | 0.127 | 0 |
| Cough | 0.069 | 0 | 0.571 | 0.234 | 0.061 |
| Wind | 0.037 | 0.072 | 0.104 | 0.557 | 0.035 |
| Burp | 0.031 | 0.03 | 0.236 | 0.557 | 0.233 |
| Blood_Sugar | 0.013 | −0.077 | 0 | 0 | 0.492 |
| Bruise | 0.125 | 0.055 | 0 | 0.135 | 0.423 |
| Muscles | 0.023 | 0.226 | 0.06 | 0.146 | 0.382 |
plot(rez_efa$n_fac)
# rez_cfa15$testrez_hclust <- make_hclust(data16)
rez_ega <- make_ega(data16, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Cough"]], "C6" = colors[["Bruise"]], "C7" = colors[["Wind"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data16, n = 4, cols = c("F1" = colors[["Thirsty"]], "F2" = colors[["Sneeze"]],
"F3" = colors[["Blood Sugar"]], "F4" = colors[["Burp"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Heart | 0.478 | 0.037 | 0.017 | 0.094 | 0.044 | 0.085 | 0.035 |
| Breathing | 0.478 | 0.189 | 0.068 | 0.136 | 0.021 | 0.059 | 0 |
| Thirsty | 0.088 | 0.571 | 0.142 | 0 | 0 | 0.09 | 0.044 |
| Hungry | 0.172 | 0.466 | 0.125 | 0.013 | 0 | 0.125 | 7.113 × 10−4 |
| Sex_arousal | 0.081 | 0.226 | 0.091 | 0 | 0.02 | 0.185 | 0.036 |
| Urinate | 0.038 | 0.28 | 0.555 | 0 | 0.02 | 0.066 | 0.04 |
| Defecate | 0.087 | 0.069 | 0.555 | 0.091 | 0.134 | 0.045 | 0.018 |
| Taste | 0.041 | 0 | 0.073 | 0.512 | 0.089 | 0.021 | 0.085 |
| Muscles | 0.173 | 0.012 | 0.028 | 0.395 | 0.077 | 0.212 | 0 |
| Itch | 0.105 | 0 | −0.015 | 0.277 | 0.074 | 0.164 | 0.011 |
| Sneeze | 0.003 | 0 | 0.02 | 0.133 | 0.585 | 0.13 | 0 |
| Cough | 0.099 | 0 | 0.039 | 0.113 | 0.47 | −0.011 | 0.145 |
| Vomit | 0.004 | 0.022 | 0.112 | 0.036 | 0.292 | 0.042 | 0.053 |
| Pain | 0.236 | 0.086 | 0 | 0.146 | 0.063 | 0.462 | 0 |
| Bruise | 0 | 0 | 0 | 0.097 | 0.103 | 0.421 | 0.069 |
| Blood_Sugar | 0 | 0.04 | 0 | 0.029 | −0.029 | 0.349 | 0 |
| Affective_touch | 0 | 0.223 | 0 | 0.079 | 0 | 0.311 | 0 |
| Temp | 0.015 | 0.113 | 0.131 | 0.147 | 0.036 | 0.232 | 0.086 |
| Wind | 0.03 | 0.098 | 0.046 | 0.102 | 0.053 | 0.106 | 0.604 |
| Burp | 0.034 | 8.632 × 10−4 | 0.026 | 0.024 | 0.168 | 0.056 | 0.604 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data16f)
rez_ega <- make_ega(data16f, cols = c(
"C1" = colors[["Heart"]], "C2" = colors[["Hungry"]], "C3" = colors[["Defecate"]], "C4" = colors[["Muscles"]],
"C5" = colors[["Cough"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data16f, n = 4, cols = c("F1" = colors[["Thirsty"]], "F2" = colors[["Cough"]],
"F3" = colors[["Bruise"]], "F4" = colors[["Wind"]]))
# rez_cfa16 <- make_cfa(data16f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | ||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| Breathing | 0.429 | 0.145 | 0.078 | 0.03 | 0 | 0 |
| Pain | 0.416 | 0.093 | 0.035 | 0.03 | 0.031 | 0.242 |
| Muscles | 0.378 | 0.037 | 0.075 | 0.134 | 0.013 | 0.011 |
| Heart | 0.342 | 0.038 | 0.021 | 0.048 | 0.046 | 0 |
| Hungry | 0.18 | 0.567 | 0.133 | 0 | 0.008 | 0.081 |
| Thirsty | 0.108 | 0.567 | 0.162 | 0 | 0.06 | 0 |
| Urinate | 0.04 | 0.265 | 0.568 | 0.025 | 0.057 | 0 |
| Defecate | 0.151 | 0.03 | 0.568 | 0.08 | 0.042 | 0 |
| Sneeze | 0.05 | 0 | 0.062 | 0.574 | 0.027 | 0.156 |
| Cough | 0.179 | 0 | 0.045 | 0.574 | 0.152 | −0.026 |
| Wind | 0.076 | 0.073 | 0.088 | 0.075 | 0.612 | 0.044 |
| Burp | 0.025 | 0.009 | 0.033 | 0.135 | 0.612 | 0.068 |
| Bruise | 0.193 | 0 | 0 | 0.126 | 0.077 | 0.529 |
| Blood_Sugar | 0 | 0.067 | 0 | −0.021 | 0 | 0.529 |
plot(rez_efa$n_fac)
# rez_cfa16$testrez_hclust <- make_hclust(data17)
rez_ega <- make_ega(data17, cols = c(
"C1" = colors[["Hungry"]], "C2" = colors[["Defecate"]], "C3" = colors[["Muscles"]], "C4" = colors[["Cough"]],
"C5" = colors[["Itch"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data17, n = 5, cols = c("F1" = colors[["Sneeze"]], "F2" = colors[["Hungry"]],
"F3" = colors[["Defecate"]], "F4" = colors[["Burp"]],
"F5" = colors[["Vomit"]]))
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||||
| Item | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| Hungry | 0.597 | 0.04 | 0.032 | 0 | 0 | 1.557 × 10−4 | 0 |
| Breathing | 0.407 | 0 | 0.088 | 0.046 | 0 | 0.02 | 0.096 |
| Thirsty | 0.337 | 0.125 | 0.065 | 0 | 0.043 | 0.09 | 0 |
| Blood_Sugar | 0.224 | 0 | 0.105 | 0.044 | 0 | 0.037 | 0 |
| Heart | 0.211 | 0.057 | 0.092 | 0.039 | 0 | 0.05 | 0.12 |
| Urinate | 0.14 | 0.557 | 0.144 | 0.026 | 0.058 | 0.012 | 0.003 |
| Defecate | 0.049 | 0.557 | 0.126 | 0.065 | 0.068 | 0.021 | 0 |
| Sex_arousal | 0.034 | 0.18 | 0.528 | 0 | 0.003 | 0.109 | 0 |
| Affective_touch | 0.025 | 0.043 | 0.306 | 0.111 | 0.067 | 0 | 0.114 |
| Muscles | 0.131 | 0 | 0.297 | 0.053 | 0.122 | 0.127 | 0.182 |
| Taste | 0.14 | 0.05 | 0.239 | 0.109 | 0.015 | 0.084 | 0 |
| Sneeze | 0.047 | 0.079 | 0.006 | 0.699 | 0.05 | 0.03 | 0.056 |
| Cough | 0.041 | 0 | 0.114 | 0.504 | 0.174 | 0.068 | 0.029 |
| Vomit | 0.038 | 0.026 | 0.191 | 0.195 | 0.034 | 0.017 | 0.151 |
| Temp | 0.03 | 0.05 | 0.112 | 0.122 | 0.514 | 0.052 | 0.163 |
| Itch | 0 | 0.052 | 0.054 | 0.059 | 0.514 | 0.044 | 0.208 |
| Wind | 0.089 | 0.033 | 0.215 | 0.01 | 0.118 | 0.557 | 0 |
| Burp | 0.08 | 0 | 0.101 | 0.09 | 0 | 0.557 | 0.282 |
| Bruise | 0.034 | 0 | 0 | 0.055 | 0.081 | 0.209 | 0.496 |
| Pain | 0.102 | 0.002 | 0.216 | 0.097 | 0.258 | 0 | 0.496 |
plot(rez_efa$n_fac)
rez_hclust <- make_hclust(data17f)
rez_ega <- make_ega(data17f, cols = c(
"C1" = colors[["Hungry"]], "C2" = colors[["Defecate"]], "C3" = colors[["Cough"]], "C4" = colors[["Burp"]],
"C5" = colors[["Muscles"]], "C6" = colors[["Wind"]], "C7" = colors[["Bruise"]], "C8" = colors[["Blood Sugar"]]))
rez_efa <- make_efa(data17f, n = 4, cols = c("F1" = colors[["Burp"]], "F2" = colors[["Hungry"]],
"F3" = colors[["Urinate"]], "F4" = colors[["Sneeze"]]))
rez_cfa17 <- make_cfa(data17f)
rez_hclust$p / rez_ega$p / rez_efa$p
plot(rez_hclust$pvclust, hang = -1, cex = 0.5)
pvclust::pvrect(rez_hclust$pvclust, alpha=0.95, max.only=FALSE)
stab <- EGAnet::itemStability(rez_ega$rez)
rez_ega$table| EGA Loadings | |||||
| Item | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Hungry | 0.599 | 0.045 | 0 | 0.002 | 0.028 |
| Breathing | 0.414 | 0 | 0.034 | 0.015 | 0.179 |
| Thirsty | 0.335 | 0.128 | 0.003 | 0.099 | 0.058 |
| Blood_Sugar | 0.242 | 0 | 0.031 | 0.055 | 0 |
| Heart | 0.213 | 0.069 | 0.039 | 0.058 | 0.132 |
| Urinate | 0.158 | 0.575 | 0.033 | 0.038 | 0.051 |
| Defecate | 0.063 | 0.575 | 0.059 | 0.061 | 0.014 |
| Sneeze | 0.05 | 0.113 | 0.621 | 0.04 | 0.093 |
| Cough | 0.071 | 0 | 0.621 | 0.096 | 0.147 |
| Wind | 0.104 | 0.094 | 0.029 | 0.564 | 0.132 |
| Burp | 0.095 | 0 | 0.078 | 0.564 | 0.304 |
| Pain | 0.108 | 0.046 | 0.098 | 0.012 | 0.613 |
| Bruise | 0.048 | 0 | 0 | 0.219 | 0.343 |
| Muscles | 0.172 | 0.013 | 0.081 | 0.185 | 0.28 |
plot(rez_efa$n_fac)
rez_cfa17$test| CFA Model Comparison | |||||
| Name | BIC | RMSEA | Chi2 | CFI | BF |
|---|---|---|---|---|---|
| fit_ega | 8493.471 | 0.057 | 102.289 | 0.947 | NA |
| fit_efag | 8516.023 | 0.089 | 224.513 | 0.828 | 1.267374e-05 |
| fit_efa | 8516.023 | 0.089 | 224.513 | 0.828 | 1.267374e-05 |
| fit_g | 8552.229 | 0.101 | 277.330 | 0.771 | 1.742022e-13 |
| fit_hclust | 8557.833 | 0.097 | 227.561 | 0.817 | 1.056922e-14 |
| fit_egag | NA | NA | NA | NA | 3.324503e+10 |
| fit_hclustg | NA | NA | NA | NA | 3.130020e-10 |
Note the usage of descriptive factor names relating directly to the items to avoid abstraction.
# Add empirical variables
add_empirical <- function(data, sample = "Sample 1a") {
x <- data.frame(
Original = rowMeans(data),
HungryThirsty = (data$Hungry + data$Thirsty) / 2,
MusclesPain = (data$Muscles + data$Pain) / 2,
WindBurp = (data$Wind + data$Burp) / 2,
UrinateDefecate = (data$Urinate + data$Defecate) / 2,
BreathingHeart = (data$Heart + data$Breathing) / 2)
if("Blood_Sugar" %in% names(data)) {
x$BruiseBlood <- (data$Blood_Sugar + data$Bruise) / 2
} else {
x$BruiseBlood <- data$Bruise
}
if("Cough" %in% names(data)) {
x$CoughSneeze <- (data$Cough + data$Sneeze) / 2
} else {
x$CoughSneeze <- data$Sneeze
}
x$Sample <- sample
x
}
scores <- list(
sample1a = add_empirical(data1a, "Sample 1a"),
sample1b = add_empirical(data1b, "Sample 1b"),
sample2 = add_empirical(data2, "Sample 2"),
sample3 = add_empirical(data3, "Sample 3"),
sample4 = add_empirical(data4, "Sample 4"),
sample5 = add_empirical(data5, "Sample 5"),
sample6 = add_empirical(data6, "Sample 6"),
sample7a = add_empirical(data7a, "Sample 7a"),
sample7b = add_empirical(data7b, "Sample 7b"),
sample7c = add_empirical(data7c, "Sample 7c"),
sample8a = add_empirical(data8a, "Sample 8a"),
sample8b = add_empirical(data8b, "Sample 8b"),
sample9 = add_empirical(data9, "Sample 9"),
sample10 = add_empirical(data10, "Sample 10"),
sample11 = add_empirical(data11 , "Sample 11"),
sample12 = add_empirical(data12 , "Sample 12"),
sample13 = add_empirical(data13, "Sample 13"),
sample14 = add_empirical(data14 , "Sample 14"),
sample15 = add_empirical(data15 , "Sample 15"),
sample16 = add_empirical(data16, "Sample 16"),
sample17 = add_empirical(data17, "Sample 17")
)
save(scores, file = "../data/scores.RData")