findSa: findSa

In Omisc: Univariate Bootstrapping and Other Things

findSa

Description

This is an implementation of the YHY bootstrap covariance matrix.

Usage

findSa(S, fitted, p, a = 0.5, df, n, tau = NULL, tol = 1e-07)

Arguments

S	Sample covariance matrix
fitted	The fitted covariance matrix
p	the number of columns in the covariance matrix
a	the starting value for the a parameter
df	the degrees of freedom in the model
n	the number of participants in the model
tau	the population tau. If no tau is provided, the estimated tau from the model will be used
tol	the difference between ga and tau at which the function will converge

Value

a list of the "a" adjusted covariance matrix, Sa, the tau, ga, and the number of interations.

Examples

require(Omisc)
require(lavaan)
set.seed(2^7-1)
modelTest<-'
LV1=~ .7*x1+.8*x2+.75*x3+.6*x4
LV2=~ .7*y1+.8*y2+.75*y3+.6*y4
LV1~~.3*LV2
LV1~~1*LV1
LV2~~1*LV2
'
modelFit<-'
LV1=~ x1+x2+x3+x4
LV2=~ y1+y2+y3+y4
LV1~~start(.5)*LV2
LV1~~1*LV1
LV2~~1*LV2
'

testdata<-simulateData(modelTest, sample.nobs = 250)
fit<-cfa(modelFit, testdata)

fitted<-fitted(fit)$cov
fitted<-fitted[,1:ncol(fitted)]
S<-cov(testdata)
p<-8
a<-.5
n<-250
df<-21
findSa(S, fitted, p, .5, df, n)

Omisc documentation built on Aug. 9, 2022, 5:09 p.m.