Make a plot of confidence interval coverage rates
Make a plot of confidence interval coverage rates given varying parameters (e.g., sample size, percent missing completely at random, or random parameters in the model)
plotCoverage(object, coverParam, coverValue = NULL, contParam = NULL, contN = TRUE,
contMCAR = TRUE, contMAR = TRUE, useContour = TRUE)object |
|
coverParam |
Vector of parameters names that the user wishes to find coverage rate for. This can be a vector of names (e.g., "f1=~y2", "f1~~f2"). |
coverValue |
A target value used that users wish to find the coverage rate of that value (e.g., 0). If |
contParam |
Vector of parameters names that vary over replications that users wish to use in the plot. |
contN |
Include the varying sample size in the coverage rate plot if available |
contMCAR |
Include the varying MCAR (missing completely at random percentage) in the coverage rate plot if available |
contMAR |
Include the varying MAR (missing at random percentage) in the coverage rate plot if available |
useContour |
This argument is used when users specify to plot two varying parameters. If |
Predicting whether the confidence interval of each replication covers target value (or parameter) or not by varying parameters using logistic regression (without interaction). Then, plot the logistic curves predicting the probability of significance against the target varying parameters.
Not return any value. This function will plot a graph only.
Sunthud Pornprasertmanit (psunthud@gmail.com), Alexander M. Schoemann (East Carolina University; schoemanna@ecu.edu)
SimResult to see how to create a simResult object with randomly varying parameters.
getCoverage to obtain a coverage rate given varying parameters values.
## Not run: loading <- matrix(0, 6, 1) loading[1:6, 1] <- NA LY <- bind(loading, 0.4) RPS <- binds(diag(1)) RTE <- binds(diag(6)) CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType="CFA") # Specify both continuous sample size and percent missing completely at random. # Note that more fine-grained values of n and pmMCAR is needed, e.g., n=seq(50, 500, 1) # and pmMCAR=seq(0, 0.2, 0.01) Output <- sim(NULL, n=seq(100, 200, 20), pmMCAR=c(0, 0.1, 0.2), model=CFA.Model) # Plot the power of the first factor loading along the sample size value plotCoverage(Output, "f1=~y1", contMCAR=FALSE) plotCoverage(Output, "f1=~y1", coverValue = 0, contMCAR=FALSE) # Plot the power of the correlation along the sample size and percent missing completely at random plotCoverage(Output, "f1=~y1") ## End(Not run)
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