Participant 023 (1.12.15)
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rm(list = ls())

## Read in data
dat=read.csv('pre023.csv')

## Designate "999" as missing
dat[dat[,]==999] <- NA

## Load Psych package
library(psych)

## Run fa
fa(dat[,c(13:14,18:23,24:27)],nfactors=3,rotate='oblimin',fm='ml')

## Remove Irritable
fa(dat[,c(13:14,18:20,22,23,24:27)],nfactors=3,rotate='oblimin',fm='ml')

## Save as FA object
efa=fa(dat[,c(13:14,18:20,22,23,24:27)],nfactors=3,rotate='oblimin',fm='ml')

## Create a matrix of loadings and then factor score raw data, weighted by loadings
load=efa$loadings

## Remove factor loadings smaller than absolute value of .30
load[load[,]<.3 & load[,]>-.29]<-0

## Create pattern matrix for LY in LISREL
library(car)

pat=recode(load,'0=0;else=1')
write.table(pat,'pat.txt',row.names=F,col.names=F)

## Create subset
datlat=dat[,c(13:14,18:20,22,23,24:27)]

## Convert NAs to -999s
datlat[is.na(datlat[,])]<- -999

## Export file for CFA
write.table(datlat,'lat.txt',row.names=F,col.names=F)

## Preparing for Negative Outcomes and Avoiding Activities removed via CFA
datlat2=dat[,c(13:14,18:20,22,23,26,27)]

## Read in CFA loadings
cfaload=read.table('loads.txt')

## Convert loadings to matrix
cfamat=as.matrix(cfaload[,2:4])

## Generate Factor Scores
fs=factor.scores(datlat2,cfamat,method='components')
ts=as.data.frame(fs$scores)

## Create empty data frame "lag"
lag=data.frame(lagf1=numeric(length(ts[,1])),lagf2=numeric(length(ts[,1])),lagf3=numeric(length(ts[,1])))

## Lag factors
lagpad <- function(x, k) {
     c(rep(NA, k), x)[1 : length(x)] 
 }

lag[,1]=lagpad(ts[,1])
lag[,2]=lagpad(ts[,2])
lag[,3]=lagpad(ts[,3])
lag[,4]=ts[,1]
lag[,5]=ts[,2]
lag[,6]=ts[,3]

colnames(lag) <- c("lagf1","lagf2","lagf3","f1","f2","f3")

lag[is.na(lag[,])]<- -999

## Export file for DFA
write.table(lag,'lag.dat',row.names=F,col.names=F)