View source: R/WS.Corr.Mixed.R
WS.Corr.Mixed | 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. Non-parametric bootstrap-based confidence intervals can be requested.
WS.Corr.Mixed(Dataset, Fixed.Part=" ", Random.Part=" ", Correlation=" ", Id, Time=Time, Model=1, Number.Bootstrap=100, Alpha=.05, Seed=1)
Dataset |
A |
Fixed.Part |
The outcome and fixed-effect part of the mixed-effects model to be fitted. The model should be specified in agreement with the |
Random.Part |
The random-effect part of the mixed-effects model to be fitted (specified in line with the |
Correlation |
An optional object describing the within-group correlation structure (specified in line with the |
Id |
The subject indicator. |
Time |
The time indicator. Default |
Model |
The type of model that should be fitted. |
Number.Bootstrap |
The number of bootstrap samples to be used to estimate the Confidence Intervals around R. Default |
Alpha |
The α-level to be used in the bootstrap-based Confidence Interval for R. Default Alpha=0.05 |
Seed |
The seed to be used in the bootstrap. Default Seed=1. |
Warning 1
To avoid problems with the lme
function, do not specify powers directly in the function call. For example, rather than specifying Fixed.Part=ZSV ~ Time + Time**2
in the function call, first add Time**2
to the dataset
(Dataset$TimeSq <- Dataset$Time ** 2
) and then use the new variable name in the call:
Fixed.Part=ZSV ~ Time + TimeSq
Warning 2
To avoid problems with the lme
function, specify the Random.Part and Correlation arguments like e.g.,
Random.Part = ~ 1| Subject
and
Correlation=corGaus(form= ~ Time, nugget = TRUE)
not like e.g.,
Random.Part = ~ 1| Subject
and
Correlation=corGaus(form= ~ Time| Subject, nugget = TRUE)
(i.e., do not use Time| Subject
)
Model |
The type of model that was fitted (model 1, 2, or 3.) |
D |
The D matrix of the fitted model. |
Tau2 |
The τ^2 component of the fitted model. This component is only obtained when serial correlation is requested (Model 2 or 3), \varepsilon_{2} \sim N(0, τ^2 H_{i})). |
Rho |
The ρ component of the fitted model which determines the matrix H_{i}, ρ(|t_{ij}-t_{ik}|). This component is only obtained when serial correlation is considered (Model 2 or 3). |
Sigma2 |
The residual variance. |
AIC |
The AIC value of the fitted model. |
LogLik |
The log likelihood value of the fitted model. |
R |
The estimated reliabilities. |
CI.Upper |
The upper bounds of the bootstrapped confidence intervals. |
CI.Lower |
The lower bounds of the bootstrapped confidence intervals. |
Alpha |
The α level used in the estimation of the confidence interval. |
Coef.Fixed |
The estimated fixed-effect parameters. |
Std.Error.Fixed |
The standard errors of the fixed-effect parameters. |
Time |
The time values in the dataset. |
Fitted.Model |
A fitted model of class |
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.
Explore.WS.Corr, WS.Corr.Mixed.SAS
# 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) # summary of the results summary(Model1) # plot the results plot(Model1) ## Not run: 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) # summary of the results summary(Model2) # 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) # summary of the results summary(Model3) # plot the results # estimated corrs for all pairs of time points plot(Model3) # estimated corrs as a function of time lag ## End(Not run)
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