getCoverage: Find coverage rate of model parameters

Description

A function to find the coverage rate of confidence intervals in a model when none, one, or more of the simulations parameters vary randomly across replications.

Usage

getCoverage(simResult, coverValue = NULL, contParam = NULL, coverParam = NULL, 
    nVal = NULL, pmMCARval = NULL, pmMARval = NULL, paramVal = NULL)

Arguments

simResult

'>SimResult that may include randomly varying parameters (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.

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.

nVal

The sample size values that users wish to find power from.

pmMCARval

The percent completely missing at random values that users wish to find power from.

pmMARval

The percent missing at random values that users wish to find power from.

paramVal

A list of varying parameter values that users wish to find power from.

Value

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

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.

Examples

Run this code

# NOT RUN {
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 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, model=CFA.Model, n=seq(100, 200, 20), pmMCAR=c(0, 0.1, 0.2))
summary(Output)

# Get the coverage rates of all possible combinations of n and pmMCAR
getCoverage(Output)

# Get the coverage rates of the combinations of n of 100 and 200 and pmMCAR of 0, 0.1, and 0.2
getCoverage(Output, coverValue = 0, nVal=c(100, 200), pmMCARval=c(0, 0.1, 0.2))
# }