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#load the mVAR library
library(rmarkdown)
library(knitr)
library("gridExtra")
library("devtools")## Loading required package: usethislibrary("networktools")## Registered S3 method overwritten by 'R.oo':
## method from
## throw.default R.methodsS3## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4library("mlVAR")## Registered S3 methods overwritten by 'huge':
## method from
## plot.sim BDgraph
## print.sim BDgraphlibrary("qgraph")
library(apaTables)## Warning: package 'apaTables' was built under R version 3.6.2library(tidyverse)## -- Attaching packages ------------------------------------------------------------------------------ tidyverse 1.3.0 --## v ggplot2 3.2.1 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## Warning: package 'ggplot2' was built under R version 3.6.2## Warning: package 'dplyr' was built under R version 3.6.2## -- Conflicts --------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::combine() masks gridExtra::combine()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()# check version - mlvar should be 0.4.1 and network tools is 1.2.1
packageVersion("mlVAR")## [1] '0.4.4'packageVersion("networktools")## [1] '1.2.2'#read in the csv file note this is for he WEEK
networkDataday<-read.csv("LSM_R.csv",header=TRUE)
networkDataweek<-read.csv("LSM_W1.csv",header=TRUE)This first series of analyses will be for the within-day effects. This uses data that was collected at 8 AM to 8 PM and every 4 hours in between. Variables across models are identical, so I set those up first.
#CREATE CORRELATION TABLES
dayc <- networkDataday %>% dplyr::select(3:10)
weekc <- networkDataweek %>% dplyr::select(3:10)
apa.cor.table(dayc, filename = "networkdaycor.doc", table.number = 1,
show.conf.interval=FALSE, landscape = TRUE)## The ability to suppress reporting of reporting confidence intervals has been deprecated in this version.
## The function argument show.conf.interval will be removed in a later version.##
##
## Table 1
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. STRESS 3.42 1.92
##
## 2. DEP 3.09 1.81 .74**
## [.71, .77]
##
## 3. ANGER 3.62 2.01 .70** .68**
## [.67, .73] [.65, .71]
##
## 4. HATE 2.99 1.92 .50** .53** .59**
## [.46, .54] [.49, .57] [.55, .63]
##
## 5. GUILT 2.45 1.49 .37** .35** .34** .31**
## [.31, .42] [.29, .40] [.29, .39] [.25, .36]
##
## 6. REGRET 2.36 1.44 .30** .26** .34** .31**
## [.25, .36] [.21, .32] [.29, .39] [.26, .37]
##
## 7. EMP 3.71 1.79 .30** .24** .32** .23**
## [.24, .35] [.18, .30] [.26, .37] [.17, .28]
##
## 8. PURP 3.83 1.84 .36** .30** .35** .26**
## [.30, .41] [.24, .35] [.29, .40] [.20, .32]
##
## 5 6 7
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .50**
## [.45, .54]
##
## .25** .22**
## [.19, .30] [.16, .27]
##
## .27** .23** .77**
## [.21, .32] [.18, .29] [.74, .79]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## apa.cor.table(weekc, filename = "networkweek.doc", table.number = 2,
show.conf.interval=FALSE, landscape = TRUE)## The ability to suppress reporting of reporting confidence intervals has been deprecated in this version.
## The function argument show.conf.interval will be removed in a later version.##
##
## Table 2
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. STRESS 3.84 1.82
##
## 2. DEP 3.33 1.79 .77**
## [.75, .80]
##
## 3. ANGER 3.99 1.90 .73** .74**
## [.69, .76] [.71, .76]
##
## 4. HATE 3.23 1.87 .50** .52** .61**
## [.45, .54] [.47, .57] [.57, .65]
##
## 5. GUILT 2.41 1.40 .27** .28** .28** .23**
## [.21, .32] [.22, .34] [.22, .34] [.17, .29]
##
## 6. REGRET 2.35 1.32 .28** .29** .27** .25**
## [.22, .34] [.23, .35] [.21, .33] [.19, .31]
##
## 7. EMP 4.07 1.74 .31** .22** .31** .16**
## [.25, .37] [.16, .28] [.25, .36] [.10, .22]
##
## 8. PURP 4.18 1.80 .36** .26** .31** .17**
## [.31, .42] [.20, .32] [.25, .37] [.11, .23]
##
## 5 6 7
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .59**
## [.55, .63]
##
## .23** .23**
## [.17, .29] [.17, .29]
##
## .24** .23** .81**
## [.18, .30] [.16, .29] [.78, .83]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
## #names of the variables
vars = c("STRESS", "DEP", "ANGER",
"HATE", "GUILT", "REGRET", "EMP", "PURP")
#name the id variable
#data should be in longform...
idvar = c("ID")
#name the beep variable
#pretty sure this is a differnet form of "day" variable
beepvar=c("TIME")##Day Model
#run the model - Model 10 neg emotions, 4 PTSD clusters (excludes PTSD item 11)
#NOTE THAT THE NAMES OF VARIABLES NEED TO MATCH NAMES IN TEH DATASET.
modelNewday<-mlVAR(networkDataday, vars=vars, idvar=idvar,lags=1, estimator="lmer", contemporaneous="orthogonal", temporal="orthogonal", verbose=TRUE, beepvar="TIME")## Warning in mlVAR(networkDataday, vars = vars, idvar = idvar, lags = 1,
## estimator = "lmer", : 275 subjects detected with < 20 measurements. This is
## not recommended, as within-person centering with too few observations per
## subject will lead to biased estimates (most notably: negative self-loops).## Estimating temporal and between-subjects effects##
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|=================================================================| 100%#save the model
save(modelNewday, file="modelNewday.Rdata")
#load the model
load("modelNewday.Rdata")##Week Model
#run the model - Model 10 neg emotions, 4 PTSD clusters (excludes PTSD item 11)
#NOTE THAT THE NAMES OF VARIABLES NEED TO MATCH NAMES IN TEH DATASET.
modelNewweek<-mlVAR(networkDataweek, vars=vars, idvar=idvar,lags=1, estimator="lmer", contemporaneous="orthogonal", temporal="orthogonal", verbose=TRUE, beepvar="TIME")## Warning in mlVAR(networkDataweek, vars = vars, idvar = idvar, lags = 1, :
## 230 subjects detected with < 20 measurements. This is not recommended, as
## within-person centering with too few observations per subject will lead to
## biased estimates (most notably: negative self-loops).## Estimating temporal and between-subjects effects##
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|=================================================================| 100%#save the model
save(modelNewweek, file="modelNewweek.Rdata")
#load the model
load("modelNewweek.Rdata")#names of the symptoms
symptoms <- c("stress", "depressed", "angry", "hate", "guilt",
"regret", "empowered", "purpose")
# group defined here
gr<- list(c(1:2),c(3:4), c(5,6), c(7:8)) # Compute the networks and layout:
between <- plot(modelNewday, "between", nonsig="hide", rule = "and")
contemporaneous<- plot(modelNewday, "contemporaneous", nonsig="hide", rule = "and")
temporal <- plot(modelNewday, "temporal", nonsig = "hide")
Layout <- averageLayout(contemporaneous, temporal)# Compute the networks and layout:
betweenw <- plot(modelNewweek, "between", nonsig="hide", rule = "and")
contemporaneousw <- plot(modelNewweek, "contemporaneous", nonsig="hide", rule = "and")
temporalw <- plot(modelNewweek, "temporal", nonsig = "hide")
Layoutw <- averageLayout(contemporaneousw, temporalw)###########plot contemporaneous in average layout - all connections here#################
#plot pdf colour
pdf("Between_day.pdf",width=13.5,height=8)
qgraph(between, vsize=7, title = "", theme = "colorblind", legend.cex=.82, border.width=1.5, border.color="black",
layout = Layout, cut = 0)
dev.off()## png
## 2pdf("Contemporaneous_DAY.pdf",width=13.5,height=8)
qgraph(contemporaneous, vsize=7, title = "", theme = "colorblind", legend.cex=.82, border.width=1.5, border.color="black", cut = 0, layout = Layout)
dev.off()## png
## 2pdf("Temporal_DAY.pdf",width=13.5,height=8)
qgraph(temporal, vsize=7, title = "", theme = "colorblind", legend.cex=.82, border.width=1.5, border.color="black",
layout = Layout, cut = 0)
dev.off()## png
## 2##########plot temporal in average layout - all connections here#############
#plot pdf colour
pdf("Between_week.pdf",width=13.5,height=8)
qgraph(betweenw, vsize=7, title = "", theme = "colorblind", legend.cex=.82, border.width=1.5, border.color="black",
layout = Layoutw, cut = 0)
dev.off()## png
## 2pdf("Contemporaneous_WEEK.pdf",width=13.5,height=8)
qgraph(contemporaneousw, vsize=7, title = "", theme = "colorblind", legend.cex=.82, border.width=1.5, border.color="black", cut = 0, layout = Layoutw)
dev.off()## png
## 2pdf("Temporal_week.pdf",width=13.5,height=8)
qgraph(temporalw, vsize=7, title = "", theme = "colorblind", legend.cex=.82, border.width=1.5, border.color="black",
layout = Layoutw, cut = 0)
dev.off()## png
## 2