library(Hmisc) library(sem) source("AdvancedFactorFunctionsV1.01.r") AthleticsData <- spss.get("AthleticsData.sav") attach(AthleticsData) AD.R <- cor(AthleticsData) AD.nobs <- dim(AthleticsData)[1] cfa1.model <- specifyModel("CFA1.r") options(digit=5) cfa1.fit <- sem(cfa1.model, AD.R, AD.nobs) summary(cfa1.fit) modIndices(cfa1.fit) cfa2.model <- specifyModel("CFA2.r") cfa2.model <- update(cfa1.model) add, Endurance -> MAXPUSHU, theta19, NA cfa2.fit <- sem(cfa2.model, AD.R, AD.nobs) summary(cfa2.fit) modIndices(cfa2.fit) cfa3.model <- specifyModel("CFA3.r") cfa3.fit <- sem(cfa3.model, AD.R, AD.nobs) summary(cfa3.fit) modIndices(cfa3.fit) cfa4.model <- specifyModel("CFA4.r") cfa4.fit <- sem(cfa4.model, AD.R, AD.nobs) summary(cfa4.fit) modIndices(cfa4.fit) factanal(AthleticsData, factors = 3, rotation="varimax") ######### library(Hmisc) library(sem) source("AdvancedFactorFunctionsV1.01.r") AthleticsData <- spss.get("AthleticsData.sav") attach(AthleticsData) AD.R <- cor(AthleticsData) AD.nobs <- dim(AthleticsData)[1] (x <- SetupCFAPattern(AD.R,3,c("Hand-Eye","Endurance","Strength"))) FAtoCFA(x,model.name = "Pure1") Pure1.model <-cfa(covs=NULL,reference.indicators=FALSE) Pure1.fit <- sem(Pure1.model, AD.R, AD.nobs) CFAfromSEM(Pure1.fit) print.FLS(CFAfromSEM(Pure1.fit)) FAtoSEM(x,model.name="Pure2") Pure2.model <- specifyModel() ## paste lines sem(Pure2.model,AD.R,AD.nobs) (x <- MLFA(AD.R,3,AD.nobs)$Varimax) FAtoSEM(x,"fa1",cutoff=0.30,make.start.values=TRUE) fa1.model <- specifyModel() #paste in lines fa1.out <- sem(fa1.model,AD.R,AD.nobs) summary(fa1.out) FAtoREF(x,AD.R,"ref1") ref1.model <- specifyModel() # paste in lines from ref1.r ref1.out <- sem(ref1.model,AD.R,AD.nobs) summary(ref1.out)