Healthy U Daily Life Analyses
Nikki Puccetti
7/20/2021
Setup
1. Libraries
library(psych)
library(psycho)
library(lme4)
library(lmerTest)
library(nlme)
library(sjstats)
library(dplyr)
library(foreign)
library(sjPlot)
library(dplyr)
library(tidyr)
library(plyr)
library(ggplot2)
library(gridExtra)
library(EMAeval)
library(knitr)
library(arsenal)
library(corrplot)
library(magrittr)
library(kableExtra)
library(ggcorrplot)2. Dataframes
#Measures of Sx and Cog risk etc.
trait <- read.csv("/Users/nikki/Desktop/Research/HealthyU_Scanning_Local/Emily_HUresources/WRG Survey/HealthyU_Questionnaires_Masterdata_Wide.csv", header=T)
trait <- trait[-c(1)]
#trait <- trait[c(2,9:11,13:32)]
#EMA and GPS variables
ema <- read.csv("/Users/nikki/Desktop/Research/Masters_reanalysis/Data/HealthyU_EMA_andGPS_onlyEMAdays.csv")
ema_wi <- read.csv("/Users/nikki/Desktop/Research/EMA/HealthyU_EMA_andGPS_onlyEMAdays_AllData.csv")
#ema_wi <- ema_wi[-c(1)]
ema_bw <- read.csv("/Users/nikki/Desktop/Research/EMA/HealthyU_EMA_andGPS_onlyEMAdays_BWsubjectsData.csv")
#ema_bw <- ema_bw[-c(1)]
allema <- merge(ema_wi, ema_bw, by.x = "subid", by.y = "Subject")3. EMA data preparation (Skip this step now)
colnames(ema)
ema$allstress <- (ema$LivingStress + ema$FamilyStress + ema$AcademicStress + ema$PeerStress + ema$JobStress + ema$OtherStress)
ema_items <- ema
## careless responders
flagged <- SD_cutoff(ema_items, item.colnames = c("anxious","attentive","content","excited","happy","irritable", "nervous","upset","well_sleep","relaxed"), ID.colname = "subid")
flagged <- flagged$Index_of_Flagged_Assessment
ema_items$careless <- 0
ema_items$careless[flagged] <- 1
ema_inclCareless <- ema_items
ema_items <- subset(ema_items,ema_items$careless == 0)
#ema_items <- ema[c(2,41,42,47,35:40,9:20,33,34)]
sublist <- split(ema , f = ema$subid )
#sublist <-lapply(sublist, function(sublist) cbind(sublist, "StartDay"= sublist[1,3]))
nobs <- lapply(sublist, function(x) nrow(x))
nobs <- do.call("rbind", nobs)
nobs <- as.data.frame(nobs)
nobs$subid <- rownames(nobs)
colnames(nobs) <- c("numResp", "subid")
##Affect
grpmeans<-aggregate(ema_items$NA_Mean_new,list(ema_items$subid),mean,na.rm=TRUE)
print(grpmeans) #examine group means
names(grpmeans)<-c("Subject","ema_avg_na") #rename variables
grpmeans_pa<-aggregate(ema_items$PA_Mean_new,list(ema_items$subid),mean,na.rm=TRUE)
print(grpmeans_pa) #examine group means
names(grpmeans_pa)<-c("Subject","ema_avg_pa") #rename variables
ema_means <-merge(grpmeans,grpmeans_pa,by="Subject") #merge group means into original data
## Stress reactivity
func <- function(ema_items)
{
return(data.frame(ema_stressReact = cor(ema_items$NA_Mean_new, ema_items$allstress, use = "pairwise.complete.obs")))
}
nastress <- ddply(ema_items, .(subid), func)
ema_means <- merge(ema_means,nastress,by.x = "Subject", by.y = "subid", all.x = T)
##Stress
grpmeans_str<-aggregate(ema_items$allstress,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans_str)<-c("Subject","ema_all_str") #rename variables
ema_means <-merge(ema_means,grpmeans_str,by="Subject") #merge group means into original data
grpmeans_str1<-aggregate(ema_items$LivingStress,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans_str1)<-c("Subject","ema_living_str") #rename variables
ema_means <-merge(ema_means,grpmeans_str1,by="Subject") #merge group means into original data
grpmeans_str2<-aggregate(ema_items$FamilyStress,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans_str2)<-c("Subject","ema_family_str") #rename variables
ema_means <-merge(ema_means,grpmeans_str2,by="Subject") #merge group means into original data
grpmeans_str3<-aggregate(ema_items$AcademicStress,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans_str3)<-c("Subject","ema_acad_str") #rename variables
ema_means <-merge(ema_means,grpmeans_str3,by="Subject") #merge group means into original data
grpmeans_str4<-aggregate(ema_items$PeerStress,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans_str4)<-c("Subject","ema_peer_str") #rename variables
ema_means <-merge(ema_means,grpmeans_str4,by="Subject") #merge group means into original data
grpmeans_str5<-aggregate(ema_items$JobStress,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans_str5)<-c("Subject","ema_job_str") #rename variables
ema_means <-merge(ema_means,grpmeans_str5,by="Subject") #merge group means into original data
grpmeans_str6<-aggregate(ema_items$OtherStress,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans_str6)<-c("Subject","ema_other_str") #rename variables
ema_means <-merge(ema_means,grpmeans_str6,by="Subject") #merge group means into original data
##Social
grpmeans<-aggregate(ema_items$SocConnect,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans)<-c("Subject","ema_social_connect") #rename variables
ema_means <-merge(ema_means,grpmeans,by="Subject") #merge group means into original data
grpmeans<-aggregate(ema_items$DesireSocEngagement,list(ema_items$subid),mean,na.rm=TRUE)
names(grpmeans)<-c("Subject","ema_social_desire") #rename variables
ema_means <-merge(ema_means,grpmeans,by="Subject") #merge group means into original data
## With Who
grpsum<-aggregate(ema_items$Acquaintances,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_withAcquant") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$Strangers,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_withStranger") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$CloseFriends,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_withCFriend") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$RomanticPart,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_withPartner") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$Family,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_withFam") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$Coworkers,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_withCowork") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$Alone,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_withSelf") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
ema_means$with_Sum <- (ema_means$ema_withAcquant + ema_means$ema_withStranger + ema_means$ema_withCFriend+ ema_means$ema_withPartner+ ema_means$ema_withFam+ ema_means$ema_withCowork+ ema_means$ema_withSelf)
ema_means <- merge(ema_means, nobs, by.x = "Subject", by.y = "subid")
## Place/Location
ema_items$Location <- factor(ema_items$Location,
levels=c(1,2,3,4,5,6,7),
labels= c("rest_store","home","work","school","others_home","gym_exerc","other"))
ema_items$Location <- as.character(ema_items$Location)
ema_items$LocationSPECIFY <- as.character(ema_items$LocationSPECIFY)
ema_items$Location2 <- ifelse(ema_items$Location == "other", ema_items$LocationSPECIFY, ema_items$Location)
#spread locations wide
ema_items_loc <- ema_items[c("subid","Date","Location")]
ema_items_loc$ema_rest_store <- ifelse(ema_items_loc$Location=="rest_store",1,0)
ema_items_loc$ema_home <- ifelse(ema_items_loc$Location=="home",1,0)
ema_items_loc$ema_work <- ifelse(ema_items_loc$Location=="work",1,0)
ema_items_loc$ema_school <- ifelse(ema_items_loc$Location=="school",1,0)
ema_items_loc$ema_other_home <- ifelse(ema_items_loc$Location=="others_home",1,0)
ema_items_loc$ema_gym_exerc <- ifelse(ema_items_loc$Location=="gym_exerc",1,0)
ema_items_loc$ema_other <- ifelse(ema_items_loc$Location=="other",1,0)
#move into big dataframe
ema_items <- cbind(ema_items, ema_items_loc[c(4:10)])
##Summing it
grpsum<-aggregate(ema_items$ema_rest_store,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_loc_restStore") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$ema_home,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_loc_home") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$ema_work,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_loc_work") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$ema_school,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_loc_school") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$ema_other_home,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_loc_otherHome") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$ema_gym_exerc,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_loc_exerc") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$ema_other,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_loc_other") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
ema_means$loc_Sum <- (ema_means$ema_loc_restStore + ema_means$ema_loc_home + ema_means$ema_loc_work+ ema_means$ema_loc_school+ ema_means$ema_loc_otherHome+ ema_means$ema_loc_exerc+ ema_means$ema_loc_other)
## Convos
grpsum<-aggregate(ema_items$F2FConvo,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_liveConvo") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$DigitalConvo,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_digitalConvo") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data
grpsum<-aggregate(ema_items$NoConvo,list(ema_items$subid),sum,na.rm=TRUE)
names(grpsum)<-c("Subject","ema_noConvo") #rename variables
ema_means <-merge(ema_means,grpsum,by="Subject") #merge group means into original data4. Write out new dataframes (Skip this step now)
#write.csv(ema_items,"/Users/nikki/Desktop/Research/EMA/HealthyU_EMA_andGPS_onlyEMAdays_AllData.csv")
#write.csv(ema_means,"/Users/nikki/Desktop/Research/EMA/HealthyU_EMA_andGPS_onlyEMAdays_BWsubjectsData.csv")Basic descriptions and visuals
Density plots
Black lines in the background represent each subject’s distribution and the single colored distribution is across the subjects’ means
avg_na <- ggplot(data = allema) +
geom_density(aes(x=NA_Mean_new, group=subid), alpha=.1) +
geom_density(aes(x=ema_avg_na, group=1), fill = "turquoise", alpha = .5) +
labs(title="Individual and Group NA density", x = "Negative Affect", y = "Density")+
ylim(0,0.11) +
theme_minimal()
avg_pa <- ggplot(data = allema) +
geom_density(aes(x=PA_Mean_tri, group=subid), alpha=.1) +
geom_density(aes(x=ema_avg_pa, group=1), fill = "palegreen3", alpha = .5) +
labs(title="Individual and Group PA density", x = "Positive Affect", y = "Density")+
ylim(0,.11) +
theme_minimal()
avg_stress <- ggplot(data = allema) +
geom_density(aes(x=allstress, group=subid), alpha=.1) +
geom_density(aes(x=ema_all_str, group=1), fill = "palevioletred2", alpha = .5) +
labs(title="Individual and Group Stress density", x = "Total Stress Rating", y = "Density")+
ylim(0,.65) +
theme_minimal()
avg_social <- ggplot(data = allema) +
geom_density(aes(x=SocConnect, group=subid), alpha=.1) +
geom_density(aes(x=ema_social_connect, group=1), fill = "sienna1", alpha = .5) +
labs(title="Individual and Group Social density", x = "Social Connectedness", y = "Density")+
ylim(0,.21) +
theme_minimal()
avg_entropy <- ggplot(data = allema) +
geom_density(aes(x= entropy_sameday, group=subid), alpha=.1) +
geom_density(aes(x=entropy_sameday, group=1), fill = "mediumpurple3", alpha = .5) +
labs(title="Individual and Group Entropy density", x = "RE", y = "Density")+
ylim(0,1.85) +
theme_minimal()
#grid.arrange(avg_na, avg_pa, avg_stress,avg_social,avg_entropy,nrow = 5)
avg_na
avg_pa 
avg_stress
avg_social
avg_entropy
Categorical EMA item plots
Various types of stress, who subjects are with/their company, and different locations are examined:
cat_ema1 <- ema_bw[c(1,6:11)]
cat_ema2 <- ema_bw[c(1,14:20,22)]
cat_ema3 <- ema_bw[c(1,22,23:29)]
#1
#colnames(cat_ema1)
keycol <- "stresstype"
valuecol <- "sum"
gathercols <- c("ema_living_str","ema_family_str","ema_acad_str","ema_peer_str",
"ema_job_str","ema_other_str")
cat_ema1_long <- gather_(cat_ema1, keycol, valuecol, gathercols)
cat_ema1_long$stresstype <- as.factor(cat_ema1_long$stresstype)
ggplot(data = cat_ema1_long, aes(group = stresstype)) +
geom_boxplot(aes(x = stresstype, y = sum, fill = stresstype)) +
geom_violin(aes(x = stresstype, y = sum, fill = stresstype), alpha = .2) +
labs(title="Types of Stress Ratings", x = "Stress Domains", y = "Average rating per Subject")+
theme_minimal()
#2
#colnames(cat_ema2)
keycol <- "withtype"
valuecol <- "sum"
gathercols <- c("ema_withAcquant","ema_withStranger","ema_withCFriend","ema_withPartner",
"ema_withFam","ema_withCowork","ema_withSelf")
cat_ema2_long <- gather_(cat_ema2, keycol, valuecol, gathercols)
cat_ema2_long$withtype <- as.factor(cat_ema2_long$withtype)
ggplot(data = cat_ema2_long, aes(group = withtype)) +
geom_boxplot(aes(x = withtype, y = (sum/numResp), fill = withtype), outlier.shape = NA) +
geom_violin(aes(x = withtype, y = (sum/numResp), fill = withtype), alpha = .2) +
geom_point(aes(x = withtype, y = (sum/numResp), fill = withtype), size = 2, shape = 6, alpha = .45) +
labs(title="Proportion of Occasions per Company type", x = "Company/Who the're with", y = "Proportion of Occasions per Subject")+
theme_minimal()
#3
# colnames(cat_ema3)
keycol <- "Locationtype"
valuecol <- "sum"
gathercols <- c("ema_loc_restStore","ema_loc_home","ema_loc_work","ema_loc_school",
"ema_loc_otherHome","ema_loc_exerc","ema_loc_other")
cat_ema3_long <- gather_(cat_ema3, keycol, valuecol, gathercols)
cat_ema3_long$Locationtype <- as.factor(cat_ema3_long$Locationtype)
ggplot(data = cat_ema3_long, aes(group = Locationtype)) +
geom_boxplot(aes(x = Locationtype, y = (sum/numResp), fill = Locationtype),outlier.shape = NA) +
geom_violin(aes(x = Locationtype, y = (sum/numResp), fill = Locationtype), alpha = .2) +
geom_point(aes(x = Locationtype, y = (sum/numResp), fill = Locationtype), size = 2, shape = 6, alpha = .45) +
labs(title="Proportion of Occasions per Location type", x = "Location", y = "Proportion of Occasions per Subject")+
theme_minimal()
Descriptive statistics
ema_bw_reduced <- ema_bw[c(22,2:5,6:20,33)]
means <- describe(ema_bw_reduced)
means <- round(means, digits=2)
#Knit a lil table
means %>%
kbl() %>%
kable_styling()| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| numResp | 1 | 141 | 24.91 | 8.76 | 25.00 | 25.22 | 5.93 | 1.00 | 47.00 | 46.00 | -0.31 | 0.72 | 0.74 |
| ema_avg_na | 2 | 141 | 34.99 | 14.41 | 36.04 | 35.00 | 14.03 | 3.25 | 74.81 | 71.56 | 0.02 | -0.25 | 1.21 |
| ema_avg_pa | 3 | 141 | 54.02 | 10.05 | 55.02 | 54.20 | 8.63 | 24.31 | 87.49 | 63.18 | -0.15 | 0.41 | 0.85 |
| ema_stressReact | 4 | 132 | 0.35 | 0.29 | 0.38 | 0.37 | 0.29 | -0.68 | 0.99 | 1.66 | -0.65 | 0.86 | 0.03 |
| ema_all_str | 5 | 137 | 14.66 | 5.41 | 13.52 | 14.21 | 5.72 | 6.33 | 32.22 | 25.89 | 0.73 | 0.01 | 0.46 |
| ema_living_str | 6 | 140 | 1.93 | 1.12 | 1.50 | 1.72 | 0.74 | 1.00 | 6.67 | 5.67 | 1.55 | 2.20 | 0.09 |
| ema_family_str | 7 | 141 | 2.05 | 1.08 | 1.64 | 1.90 | 0.90 | 1.00 | 5.89 | 4.89 | 1.07 | 0.49 | 0.09 |
| ema_acad_str | 8 | 141 | 4.15 | 1.27 | 4.14 | 4.15 | 1.28 | 1.00 | 7.00 | 6.00 | 0.02 | -0.50 | 0.11 |
| ema_peer_str | 9 | 141 | 2.13 | 1.12 | 1.86 | 1.97 | 1.04 | 1.00 | 5.67 | 4.67 | 1.14 | 0.67 | 0.09 |
| ema_job_str | 10 | 140 | 2.24 | 1.32 | 1.71 | 2.05 | 0.91 | 1.00 | 7.00 | 6.00 | 1.15 | 0.60 | 0.11 |
| ema_other_str | 11 | 140 | 2.29 | 1.21 | 1.94 | 2.13 | 1.18 | 1.00 | 5.61 | 4.61 | 0.97 | 0.08 | 0.10 |
| ema_social_connect | 12 | 141 | 50.66 | 14.76 | 50.58 | 51.20 | 14.31 | 6.85 | 92.21 | 85.36 | -0.36 | 0.49 | 1.24 |
| ema_social_desire | 13 | 141 | 47.49 | 15.54 | 47.55 | 47.88 | 13.44 | 4.78 | 98.22 | 93.44 | -0.08 | 0.84 | 1.31 |
| ema_withAcquant | 14 | 141 | 3.35 | 3.24 | 3.00 | 2.94 | 2.97 | 0.00 | 18.00 | 18.00 | 1.29 | 2.26 | 0.27 |
| ema_withStranger | 15 | 141 | 1.71 | 2.53 | 1.00 | 1.23 | 1.48 | 0.00 | 17.00 | 17.00 | 2.95 | 12.82 | 0.21 |
| ema_withCFriend | 16 | 141 | 6.70 | 4.98 | 6.00 | 6.31 | 5.93 | 0.00 | 23.00 | 23.00 | 0.66 | -0.04 | 0.42 |
| ema_withPartner | 17 | 141 | 0.67 | 1.98 | 0.00 | 0.22 | 0.00 | 0.00 | 19.00 | 19.00 | 6.18 | 50.08 | 0.17 |
| ema_withFam | 18 | 141 | 2.43 | 2.71 | 2.00 | 1.96 | 2.97 | 0.00 | 16.00 | 16.00 | 1.82 | 4.52 | 0.23 |
| ema_withCowork | 19 | 141 | 0.50 | 1.07 | 0.00 | 0.26 | 0.00 | 0.00 | 8.00 | 8.00 | 3.46 | 17.12 | 0.09 |
| ema_withSelf | 20 | 141 | 10.50 | 6.14 | 10.00 | 10.07 | 5.93 | 0.00 | 32.00 | 32.00 | 0.82 | 1.05 | 0.52 |
| ema_noConvo | 21 | 141 | 11.42 | 7.76 | 11.00 | 10.81 | 7.41 | 0.00 | 36.00 | 36.00 | 0.71 | 0.38 | 0.65 |
Reliability
print(paste("Stress Domains Alpha = ", round(a1$total$raw_alpha, digits=4), sep=""))## [1] "Stress Domains Alpha = 0.8634"print(paste("Company Domains Alpha = ", round(a2$total$raw_alpha, digits=4), sep=""))## [1] "Company Domains Alpha = 0.1922"print(paste("Location Domains Alpha = ", round(a3$total$raw_alpha, digits=4), sep=""))## [1] "Location Domains Alpha = 0.0103"Correlations and heat maps
ggcorrplot(r1, method = "square", ggtheme = ggplot2::theme_minimal, title = "Correlations between subject averages of EMA variables", show.legend = TRUE, legend.title = "Corr", show.diag = FALSE,
colors = c("blue1","gray90", "violetred3"), outline.color = "black",
hc.order = FALSE, hc.method = "pairwise", lab = T,
lab_col = "black", lab_size = 2, p.mat = p.mat1, sig.level = 0.05,
insig = c("pch"), pch = 4, pch.col = "black",
pch.cex = 5, tl.cex = 12, tl.col = "black", tl.srt = 45,
digits = 2)
ggcorrplot(r2, method = "square", ggtheme = ggplot2::theme_minimal, title = "Correlations between subject averages of Stress domains", show.legend = TRUE, legend.title = "Corr", show.diag = FALSE,
colors = c("blue1","gray90", "violetred3"), outline.color = "gray",
hc.order = FALSE, hc.method = "complete", lab = T,
lab_col = "black", lab_size = 2, p.mat = NULL, sig.level = 0.05,
insig = c("pch"), pch = 4, pch.col = "black",
pch.cex = 5, tl.cex = 12, tl.col = "black", tl.srt = 45,
digits = 2)
ggcorrplot(r3, method = "square", ggtheme = ggplot2::theme_minimal, title = "Correlations between subject averages of EMA variables", show.legend = TRUE, legend.title = "Corr", show.diag = FALSE,
colors = c("blue1","gray90", "violetred3"), outline.color = "black",
hc.order = FALSE, hc.method = "complete", lab = T,
lab_col = "black", lab_size = 2, p.mat = NULL, sig.level = 0.05,
insig = c("pch"), pch = 4, pch.col = "black",
pch.cex = 5, tl.cex = 12, tl.col = "black", tl.srt = 45,
digits = 2)