#his script contains example of basic lavaan modeling

#install packages
install.packages("lavaan")
install.packages("semPlot")

#load packages
library(lavaan)
library(semPlot)



## PoliticalDemocracy example
PoliticalDemocracy.model <- "
  # measurement model
    ind60 =~ x1 + x2 + x3
    dem60 =~ y1 + y2 + y3 + y4
    dem65 =~ y5 + y6 + y7 + y8

  # structural model
    dem60 ~ ind60
    dem65 ~ ind60 + dem60

  # covariances
    y1 ~~ y5
    y2 ~~ y4 + y6
    y3 ~~ y7
    y4 ~~ y8
    y6 ~~ y8
"
#fit the model
PoliticalDemocracy.fit <- sem(PoliticalDemocracy.model, data=PoliticalDemocracy)

#look at model parameters
summary(PoliticalDemocracy.fit, standardized=TRUE, fit.measures=TRUE)

fitMeasures(PoliticalDemocracy.fit, fit.measures = c("chisq", "df", "pvalue","cfi","tli","rmsea","srmr","aic", "bic"  ))

semPaths(object = PoliticalDemocracy.fit,
         layout = "tree",
         rotation = 1,
         whatLabels = "std",
         edge.label.cex = 1,
         what = "std",
         edge.color = "navy")












#load data
data = read.delim(file.choose(),stringsAsFactors = F, header = T,sep = "\t", quote = "\"", dec = ".")

#look at the data
str(data)

# define the model
npas_NEO.model="
            #measurement model
            NPAS =~ Q1+Q2+Q3+Q4+Q5+Q6+Q7+Q8+Q9+Q10+Q11+Q12+Q13+Q14+Q15+Q16+Q17+Q18+Q19+Q20+Q21+Q22+Q23+Q24+Q25+Q26
            Extraversion  =~ TIPI1 + TIPI6
            Neuroticism   =~ TIPI4 + TIPI9 
            Openess       =~ TIPI5 + TIPI10

            #structural model
            NPAS ~ Extraversion + Neuroticism   +  Openess"

#fit the model
npas_NEO.fit = sem(model = npas_NEO.model, data = data,orthogonal = F)

# inspect model parameters
summary(npas_NEO.fit, standardized=TRUE)

# inspect fit indices
fitMeasures(npas_NEO.fit, fit.measures = c("chisq", "df", "pvalue","cfi","tli","rmsea","srmr","aic", "bic"  ))

# plot model
semPaths(object = npas_NEO.fit,
         layout = "tree",
         rotation = 1,
         whatLabels = "std",
         edge.label.cex = 1,
         what = "std",
         edge.color = "navy")

#look at suggested modifications
modindices(npas_NEO.fit, sort=T)




# redefine the model
npas_NEO_cov.model="
            #measurement model
            NPAS =~ Q1+Q2+Q3+Q4+Q5+Q6+Q7+Q8+Q9+Q10+Q11+Q12+Q13+Q14+Q15+Q16+Q17+Q18+Q19+Q20+Q21+Q22+Q23+Q24+Q25+Q26
            Extraversion  =~ TIPI1 + TIPI6
            Neuroticism   =~ TIPI4 + TIPI9 
            Openess       =~ TIPI5 + TIPI10

            #structural model
            NPAS ~ Extraversion  + Neuroticism +  Openess

            #covariances
Q11 ~~    Q19
Q1  ~~    Q4
Q7  ~~    Q12 + Q25
Q16 ~~    Q17 + Q8
Q12 ~~    Q25
Q2  ~~    Q15 + Q7  +  Q25 + Q12 + Q8
Q3  ~~    Q16 + Q17 +  Q8
Q17 ~~    Q8  + Q9  +  Q13
Q26 ~~    Q9  + Q5  +  Q24
Q8  ~~    Q15
Q5  ~~    Q24
Q9  ~~    Q5  + Q24
Q23 ~~    Q21 + Q22 + Q6
"

#fit the model
npas_NEO_cov.fit = lavaan::sem(model = npas_NEO_cov.model, data = data,orthogonal = F,estimator = "ML")

# inspect model parameters
summary(npas_NEO_cov.fit, standardized=TRUE)

# inspect fit indices
fitMeasures(npas_NEO_cov.fit, fit.measures = c("chisq", "df", "pvalue","cfi","tli","rmsea","srmr","aic", "bic"  ))

# plot model
semPaths(object = npas_NEO_cov.fit,
         layout = "tree",
         rotation = 1,
         whatLabels = "std",
         edge.label.cex = 1,
         what = "std",
         edge.color = "navy")