# continuousCoverage: Find coverage rate of model parameters when simulations have... In simsem: SIMulated Structural Equation Modeling

## Description

A function to find the coverage rate of confidence intervals in a model when one or more of the simulations parameters vary randomly across replications.

## Usage

 ```1 2``` ```continuousCoverage(simResult, coverValue = NULL, contN = TRUE, contMCAR = FALSE, contMAR = FALSE, contParam = NULL, coverParam = NULL, pred = NULL) ```

## Arguments

 `simResult` `SimResult` that includes at least one randomly varying parameter (e.g. sample size, percent missing, model parameters) `coverValue` A target value used that users wish to find the coverage rate of that value (e.g., 0). If `NULL`, the parameter values will be used. `contN` Logical indicating if N varies over replications. `contMCAR` Logical indicating if the percentage of missing data that is MCAR varies over replications. `contMAR` Logical indicating if the percentage of missing data that is MAR varies over replications. `contParam` Vector of parameters names that vary over replications. `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"). If parameters are not specified, coverage rates for all parameters in the model will be returned. `pred` A list of varying parameter values that users wish to find statistical power from.

## Details

In this function, the coverage (which can be 0 or 1) is regressed on randomly varying simulation parameters (e.g., sample size, percentage of missing data, or model parameters) using logistic regression. For a set of independent variables values, the predicted probability from the logistic regression equation is the predicted coverage rate.

## Value

Data frame containing columns representing values of the randomly varying simulation parameters, and coverage rates for model parameters of interest.

## Author(s)

Sunthud Pornprasertmanit ([email protected]), Alexander M. Schoemann (East Carolina University; [email protected])

• `SimResult` to see how to create a simResult object with randomly varying parameters.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```## Not run: # Specify Sample Size by n loading <- matrix(0, 6, 1) loading[1:6, 1] <- NA LY <- bind(loading, 0.7) 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, CFA.Model, n=seq(100, 200, 20), pmMCAR=c(0, 0.1, 0.2)) summary(Output) # Find the coverage rates of all combinations of different sample size and percent MCAR missing Ccover <- continuousCoverage(Output, contN = TRUE, contMCAR = TRUE) Ccover # Find the coverage rates of parameter estimates when sample size is 200 # and percent MCAR missing is 0.3 Ccover2 <- continuousCoverage(Output, coverValue=0, contN = TRUE, contMCAR = TRUE, pred=list(N = 200, pmMCAR = 0.3)) Ccover2 ## End(Not run) ```