WS.Corr.Mixed.SAS {CorrMixed} | R Documentation |
This function allows for the estimation of the within-subject correlations using a general and flexible modeling approach that allows at the same time to capture hierarchies in the data, the presence of covariates, and the derivation of correlation estimates. The output of proc MIXED (SAS) is used as the input for this function. Confidence intervals for the correlations based on the Delta method are provided.
WS.Corr.Mixed.SAS(Model, D, Sigma2, Asycov, Rho, Tau2, Alpha=0.05, Time)
Model |
The type of model that should be fitted. |
D |
The |
Sigma2 |
The residual variance. |
Asycov |
The asymptotic correlation matrix of covariance parameter estimates. |
Rho |
The |
Tau2 |
The |
Alpha |
The |
Time |
The time points available in the dataset on which the analysis was conducted. |
Model |
The type of model that was fitted. |
R |
The estimated within-subject correlations. |
Alpha |
The |
CI.Upper |
The upper bounds of the confidence intervals (Delta-method based). |
CI.Lower |
The lower bounds of the confidence intervals (Delta-method based). |
Time |
The time values in the dataset. |
Wim Van der Elst, Geert Molenberghs, Ralf-Dieter Hilgers, & Nicole Heussen
Van der Elst, W., Molenberghs, G., Hilgers, R., & Heussen, N. (2015). Estimating the reliability of repeatedly measured endpoints based on linear mixed-effects models. A tutorial. Submitted.
# Open data
data(Example.Data)
# Estimate R and Delta method-based CI
# based on SAS output of fitted Model 2
# First specify asycov matrix
Asy_mat <- matrix(c(129170, -10248, -12.0814, -74.8605,
-10248, 25894, 21.0976, -50.1059,
-12.0814, 21.0976, 0.07791, 1.2120,
-74.8605, -50.1059, 1.212, 370.65), nrow = 4)
Model2_SAS <- WS.Corr.Mixed.SAS(Model="Model 2",
D=500.98, Tau2=892.97, Rho=3.6302, Sigma2=190.09,
Asycov = Asy_mat, Time=c(1:45))
summary(Model2_SAS)
plot(Model2_SAS)