setwd(file.path("D:", "Muhammad Yaseen", "Consultancy at UAF", "Iram Jahanzaib", "2012-10-12"))

# source('http://openmx.psyc.virginia.edu/getOpenMx.R')
library(OpenMx)

# library(foreign)
# Data <- read.spss(file = "ICT_data1.sav", to.data.frame = TRUE)
# save.image(file = "Data.RData")
# rm(list=ls(all=TRUE))
load(file = "Data.RData") 

str(Data)
head(Data)
names(Data)

# The data for this model contain twelve variables.All twelve variables are ordered categorical variables. 
# use raw data for this model.

oneFactorRaw <- Data[c( "z1", "z2", "z3", "z4")]
z1 <- factor(x = oneFactorRaw$z1, order = TRUE)
z2 <- factor(x = oneFactorRaw$z2, order = TRUE)
z3 <- factor(x = oneFactorRaw$z3, order = TRUE)
z4 <- factor(x = oneFactorRaw$z4, order = TRUE)

oneFactorOrd <- data.frame( z1, z2, z3, z4)

oneFactorOrd$z1 <- mxFactor(oneFactorOrd$z1, levels = c(1, 2, 3 ,4, 5))
oneFactorOrd$z2 <- mxFactor(oneFactorOrd$z2, levels = c(1, 2, 3 ,4, 5))
oneFactorOrd$z3 <- mxFactor(oneFactorOrd$z3, levels = c(1, 2, 3 ,4, 5))
oneFactorOrd$z4 <- mxFactor(oneFactorOrd$z4, levels = c(1, 2, 3 ,4, 5))

str(oneFactorOrd)

# Model specification
# Creating a path centric factor model in which three exogeous factors and one endogeous factor is defined
oneFactorModel <- mxModel("Common Factor Model Path Specification",
        type="RAM"
        ,mxData(
                observed=oneFactorOrd
                ,type="raw"
        )
       , manifestVars=c("z1","z2","z3","z4")
        ,latentVars="F3"
        # residual variances
        ,mxPath(
                from=c("z1","z2","z3","z4")
                ,arrows=2
                ,free=FALSE
                ,values=c(1,1,1,1)
                ,labels=c("e1","e2","e3")
        )
        # latent variance
        ,mxPath(
                from="F3"
                ,arrows=2
                ,free=TRUE
                ,values=1
                ,labels ="varF3"
        )
        # factor loadings
        ,mxPath(
                from="F3"
                ,to=c("z1","z2","z3","z4")
                ,arrows=1
                ,free=c(TRUE,TRUE,TRUE, FALSE)
                ,values=c(1,1,1,1)
                ,labels=c("RA","Com","Eas","obs")
        )     
        # means
        ,mxPath(
                 from="one",
                ,to=c("z1","z2","z3","z4","F3")
                ,arrows=1
                ,free=FALSE
                ,values=0
                ,labels=c("meanz1","meanz2","meanz3","meanz4","meanF3")
        )
        ,mxMatrix(
                type="Full"
                ,nrow=4
                ,ncol=4
                ,free=c(TRUE,  TRUE,  TRUE,  TRUE,
                        TRUE,  TRUE,  TRUE,  TRUE,
                        TRUE,  TRUE,  TRUE,  TRUE,
                        TRUE,  TRUE,  TRUE,  TRUE)
                ,values=c(-4, -3, -2, -1, 0,
                                 1, 2, 3, 4),
                ,dimnames=list(c(), c('z1', 'z2', 'z3' , 'z4'))
                ,byrow=TRUE
                ,name="thresh"
        )
        ,mxRAMObjective(A="A", S="S", F="F", M="M", thresholds="thresh")
) # close model
This model may then be optimized using the mxRun command.
oneFactorResults <- mxRun(oneFactorModel)
   summery(oneFactorFit)