# set the working directory
#note that forward slashes are used in file paths in R
wd <- "C:/Users/Nisha/Dropbox/aMNLFA/Camtasia"
setwd(wd)

# installing and loading aMNLFA
#install.packages("aMNLFA")
library("aMNLFA")

#read data file in to an object called "df"
#make sure that you have formatted data according to the instructions in the appendix
#if data are located in a different directory than wd, specify path
df <- read.table("mr.dat",header=TRUE)
#look at the first 5 rows of data to make sure it read in properly
head(df)

# creating aMNLFA object (ob)
ob <-aMNLFA.object(path          = wd,
                            mrdata        = df,
                            indicators    = c("AU2","AU3","AU4","AU5","AU6",
                                             "AU7","AU8","AC1","AC2","AC3","AC4","AC5"),
                            catindicators = c("AU2","AU3","AU4","AU5","AU6",
                                              "AU7","AU8","AC1","AC2","AC3","AC4","AC5"),
                            time        = "CNGRADE",
                            meanimpact    = c("CNGRADE","BLAEFF","MALEFF","S14","S15","S16","S18","S19","LOWED","HIGHED",
                                              "COHORT","GRA_2","BLA_GRA","MAL_GRA",
                                              "LOW_GRA","HIG_GRA") ,
                            varimpact     = c("MALEFF","S14","S15","S16","S18","S19"),
                            measinvar     = c("CNGRADE","BLAEFF","MALEFF","S14","S15","S16","S18","S19","LOWED","HIGHED",
                                               "COHORT","GRA_2","BLA_GRA","MAL_GRA",
                                               "LOW_GRA","HIG_GRA") ,
                            factors       = c("COHORT","PARED","RACEETH","GENDER","SCHOOL"),
                            ID            = "ID",
                            thresholds    = FALSE)

#look at ob
ob

# make item plots
aMNLFA.itemplots(ob)

# draw calibration sample
aMNLFA.sample(ob)
#srdata should appear in your wd

#make Mplus input files for initial models
aMNLFA.initial(ob)
#run models manually or by running this line:
runModels(replaceOutfile = "modifiedDate")

#read in output from individual models and prepare simultaneous input file
aMNLFA.simultaneous(ob)
#run model manually or by running this line:
runModels(replaceOutfile = "modifiedDate")

# read data from the simultaneous model and make the final model with multiple testing corrections
aMNLFA.final(ob)
#run the model manually or by running this line:
runModels(replaceOutfile = "modifiedDate")

#read in final model results and create Mplus input file for generating scores in the full sample
aMNLFA.scores(ob)
#run the scoring model manually or by running this line:
runModels(replaceOutfile = "modifiedDate")

#generate descriptive plots for MNLFA scores
aMNLFA.scoreplots(ob)