# open data
data(Example.Data)
# Make covariates used in mixed model
Example.Data$Time2 <- Example.Data$Time**2
Example.Data$Time3 <- Example.Data$Time**3
Example.Data$Time3_log <- (Example.Data$Time**3) * (log(Example.Data$Time))
# model 1: random intercept model
Model1 <- WS.Corr.Mixed(
Fixed.Part=Outcome ~ Time2 + Time3 + Time3_log + as.factor(Cycle)
+ as.factor(Condition), Random.Part = ~ 1|Id,
Dataset=Example.Data, Model=1, Id="Id", Number.Bootstrap = 50,
Seed = 12345)
# plot the results
plot(Model1)
time-consuming code parts
# model 2: random intercept + Gaussian serial corr
Model2 <- WS.Corr.Mixed(
Fixed.Part=Outcome ~ Time2 + Time3 + Time3_log + as.factor(Cycle)
+ as.factor(Condition), Random.Part = ~ 1|Id,
Correlation=corGaus(form= ~ Time, nugget = TRUE),
Dataset=Example.Data, Model=2, Id="Id", Seed = 12345)
# plot the results
# estimated corrs as a function of time lag (default plot)
plot(Model2)
# estimated corrs for all pairs of time points
plot(Model2, All.Individual = T)
# model 3
Model3 <- WS.Corr.Mixed(
Fixed.Part=Outcome ~ Time2 + Time3 + Time3_log + as.factor(Cycle)
+ as.factor(Condition), Random.Part = ~ 1 + Time|Id,
Correlation=corGaus(form= ~ Time, nugget = TRUE),
Dataset=Example.Data, Model=3, Id="Id", Seed = 12345)
# plot the results
# estimated corrs for all pairs of time points
plot(Model3)
# estimated corrs as a function of time lag
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