Explore.WS.Corr {CorrMixed}  R Documentation 
This function allows for exploring the withinsubject (testretest) correlation (R
) structure in the data, taking relevant covariates into account. Estimated correlations as a function of time lag (= absolute difference between measurement moments t_1
and t_2
) are provided as well as their confidence intervals (based on a nonparametric bootstrap).
Explore.WS.Corr(OLS.Model=" ", Dataset, Id, Time,
Alpha=0.05, Smoother.Span=.2, Number.Bootstrap=100,
Seed=1)
OLS.Model 

Dataset 
A 
Id 
The subject indicator. 
Time 
The time indicator. Should be coded as 
Alpha 
The 
Smoother.Span 
A smoothing (loess) technique is used to estimate 
Number.Bootstrap 
The number of nonparametric bootstrap samples to be used to estimate the Confidence Interval for 
Seed 
The seed to be used in the bootstrap. Default 
Est.Corr 
The estimated correlations 
All.Corrs 
A 
Bootstrapped.Corrs 
A 
Alpha 
The 
CI.Upper 
The upper bounds of the confidence intervals. 
CI.Lower 
The lower bounds of the confidence intervals. 
Wim Van der Elst, Geert Molenberghs, RalfDieter Hilgers, & Nicole Heussen
Van der Elst, W., Molenberghs, G., Hilgers, R., & Heussen, N. (2015). Estimating the reliability of repeatedly measured endpoints based on linear mixedeffects models. A tutorial. Submitted.
# Open data
data(Example.Data)
# Explore correlation structure
Expl_Corr < Explore.WS.Corr(OLS.Model="Outcome~as.factor(Time)+
as.factor(Cycle) + as.factor(Condition)", Dataset=Example.Data,
Id="Id", Time="Time", Alpha=.05, Number.Bootstrap=50, Seed=123)
# explore results
summary(Expl_Corr)
# plot with correlations for all time lags, and
# add smoothed (loess) correlation function
plot(Expl_Corr, Indiv.Corrs=TRUE)
# plot bootstrapped smoothed (loess) correlation function
plot(Expl_Corr)