simsem (version 0.4-6)

setPopulation: Set the data generation population model underlying an object

Description

This function will set the data generation population model to be an appropriate one. If the appropriate data generation model is specified, the additional features can be seen in summary or summaryParam functions on the target object, such as bias in parameter estimates or percentage coverage.

Usage

setPopulation(target, population)

Arguments

target

The result object that you wish to set the data generation population model (linkS4class{SimResult}).

population

The population parameters specified in the linkS4class{SimSem} object

Value

• The target object that is changed the parameter.

See Also

• SimResultfor result object

Examples

# See each class for an example.
# Data generation model
loading <- matrix(0, 7, 3)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
loading[1:7, 3] <- NA
loadingVal <- matrix(0, 7, 3)
loadingVal[1:3, 1] <- "runif(1, 0.5, 0.7)"
loadingVal[4:6, 2] <- "runif(1, 0.5, 0.7)"
loadingVal[1:6, 3] <- "runif(1, 0.3, 0.5)"
loadingVal[7, 3] <- 1
loading.mis <- matrix("runif(1, -0.2, 0.2)", 7, 3)
loading.mis[is.na(loading)] <- 0
loading.mis[,3] <- 0
loading.mis[7,] <- 0
LY <- bind(loading, loadingVal, misspec=loading.mis)

RPS <- binds(diag(3))

path <- matrix(0, 3, 3)
path[2, 1] <- NA
BE <- bind(path, "runif(1, 0.3, 0.5)")

RTE <- binds(diag(7))

VY <- bind(c(rep(NA, 6), 0), c(rep(1, 6), ""))

datamodel <- model(LY=LY, RPS=RPS, BE=BE, RTE=RTE, VY=VY, modelType="SEM")

# Data analysis model
loading <- matrix(0, 7, 3)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
loading[7, 3] <- NA
path <- matrix(0, 3, 3)
path[2, 1] <- NA
path[1, 3] <- NA
path[2, 3] <- NA
errorCov <- diag(NA, 7)
errorCov[7, 7] <- 0
facCov <- diag(3)
analysis <- estmodel(LY=loading, BE=path, TE=errorCov, PS=facCov, modelType="SEM", indLab=paste("y", 1:7, sep=""))

# In reality, more than 10 replications are needed.
Output <- sim(10, n=200, analysis, generate=datamodel)

# Population 
loadingVal <- matrix(0, 7, 3)
loadingVal[1:3, 1] <- 0.6
loadingVal[4:6, 2] <- 0.6
loadingVal[7, 3] <- 1
LY <- bind(loading, loadingVal)
pathVal <- matrix(0, 3, 3)
pathVal[2, 1] <- 0.4
pathVal[1, 3] <- 0.4
pathVal[2, 3] <- 0.4
BE <- bind(path, pathVal)
PS <- binds(facCov)
errorCovVal <- diag(0.64, 7)
errorCovVal[7, 7] <- 0
TE <- binds(errorCov, errorCovVal)
population <- model(LY=LY, PS=PS, BE=BE, TE=TE, modelType="SEM")

# Set up the new population
Output <- setPopulation(Output, population) 

# This summary will contain the bias information
summary(Output)