datacold {cold} | R Documentation |
This example is an artificial data.
data(datacold)
A data frame with 390 observations on the following 4 variables.
Subject
identifies de number of the individual profile. This vector contains observations of 30 individual profiles.
Treatment
a factor with levels 0
and 1
.
Time
a numeric vector that identifies the number of the time points observed.
z
a numeric vector representing the response variable.
data(datacold) mod0<- cold(z~Time*Treatment, data=datacold, time="Time", id="Subject", aggregate=Treatment, dependence="ind") summary (mod0) modI<- cold(z~Time*Treatment, data=datacold, time="Time", id="Subject", aggregate=Treatment, dependence="AR1") summary (modI) anova(mod0,modI) plot(modI,which=1,xlab="Time (weeks)",ylab="Count",main="Model AR1") ### independent with random intercept mod0R<- cold(z~Time*Treatment, random=~1,data=datacold, time="Time", id="Subject", aggregate=Treatment, dependence="indR") summary(mod0R) ### independent with random intercept (dependence="indR") ### using cubature (integration = "cubature") mod0R.C<- cold(z~Time*Treatment, random=~1,data=datacold, time="Time", id="Subject", aggregate=Treatment, dependence="indR", integration = "cubature") summary(mod0R.C) randeff(mod0R.C) ### dependence="indR2" ## It takes a long time to run ## Using Monte Carlo method (integration="MC") mod0R2MC<-cold(z~Time*Treatment, random = ~ 1 + Time, data=datacold, time="Time", id="Subject", dependence="indR2", integration="MC", M=8000, trace=TRUE) summary (mod0R2MC) randeff(mod0R2MC) ## Using cubature (integration="cubature") mod0R2C<-cold(z~Time*Treatment, random = ~ 1 + Time, data=datacold, time="Time", id="Subject", dependence="indR2", integration="cubature", trace=TRUE) summary (mod0R2C)