Nothing
#Computes intraclass correlations for each level of variation. Returns the ICC for total variation (ICC.mat) and for within variation (within.ICC.mat)
ICC <- function(y,timevar, csvar, df) {
if(is.factor(df[,y])){
stop("ICC only defined for continuous variables.")
}
dep.y<-df[,y]
time<-df[,timevar]
cs<-df[,csvar]
one.way.anova<-lme4::lmer(dep.y~1+(1|time)+(1|cs))
vcomp<-summary(one.way.anova)$varcor
cs.vcomp<-vcomp[["cs"]][1]
ts.vcomp<-vcomp[["time"]][[1]]
residual.vcomp<-attr(vcomp,"sc")^2
total.variance<-cs.vcomp+ts.vcomp+residual.vcomp
##ICC for time, between, and observations
ICC_between<-cs.vcomp/total.variance
ICC_time<-ts.vcomp/total.variance
ICC_idiosyncratic<-residual.vcomp/total.variance
#now to get idiosyncratic and common within ICC
within_y<-rewie.dat(df, y,csvar=csvar,model="REWE")
dv<-paste(y,"_within",sep="")
dv<-within_y[,dv]
within.anova<-lme4::lmer(dv~1+(1|time))
withinv<-summary(within.anova)$varcor
withinvcomp<-withinv[["time"]][[1]]
residual.within.vcomp<-attr(withinv,"sc")^2
within_variance<-withinvcomp+residual.within.vcomp
#####within ICCs
ICC_within_idio<-residual.within.vcomp/within_variance
ICC_within_common<-withinvcomp/within_variance
##package results
ICC.mat<-matrix(0,nrow=4,ncol=2)
colnames(ICC.mat)<-c("ICC","variance component")
rownames(ICC.mat)<-c("total","temporal","idiosyncratic","between")
ICC.mat[1,1]<-NA
ICC.mat[1,2]<-total.variance
ICC.mat[2,1]<-ICC_time
ICC.mat[2,2]<-ts.vcomp
ICC.mat[3,1]<-ICC_idiosyncratic
ICC.mat[3,2]<-residual.vcomp
ICC.mat[4,1]<-ICC_between
ICC.mat[4,2]<-cs.vcomp
#iwthin
ICC.within.mat<-matrix(0,nrow=3,ncol=2)
colnames(ICC.within.mat)<-c("ICC_within","within variance component")
rownames(ICC.within.mat)<-c("total","temporal","idiosyncratic")
ICC.within.mat[1,1]<-NA
ICC.within.mat[1,2]<-within_variance
ICC.within.mat[2,1]<-ICC_within_common
ICC.within.mat[2,2]<-withinvcomp
ICC.within.mat[3,1]<-ICC_within_idio
ICC.within.mat[3,2]<-residual.within.vcomp
ICC.list<-list(ICC.mat=ICC.mat,
within.ICC.mat=ICC.within.mat)
return(ICC.list)
}
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