# Options

# Surpress scientific notations, digits and lines

options(scipen=999, digits=10, max.print=99999999)

# PLS pat modeling

# Increase Number of lines in output

options(max.print=99999999)

options(digits=9)


library(plspm)


# Inner Model
PEU = c(0,0,0,0)
PU =  c(0,0,0,0)
ATT = c(1,1,0,0)
INT = c(0,0,1,0)

inner = rbind(PEU, PU, ATT, INT)

# Plot Inner Model
innerplot(inner)

# Blocks Outer Model
outer = list(1:3, 4:6, 7:9, 10:12)

# Modes (reflective indicators)
mode = rep("A", 4)

PLS.1 = plspm(Data.01.PPT, inner, outer, modes = mode, scaled = TRUE, boot.val=TRUE, br=1000)
summary(PLS.1)


# Plot Inner Model
innerplot(PLS.1)

# Plot Outer Loadings
outerplot(PLS.1, what = "loadings")

# Plot Outer Weights
outerplot(PLS.1, what = "weights")




# Hierarchical Model

# Inner Model
G =   c(0,0,0,0,0)
PEU = c(1,0,0,0,0)
PU =  c(1,0,0,0,0)
ATT = c(1,0,0,0,0)
INT = c(1,0,0,0,0)

inner.HM<-rbind(G,PEU,PU,ATT,INT)


# Plot Inner Model
innerplot(inner.HM)

# Blocks Outer Model
outer.HM = list(1:12, 1:3, 4:6, 7:9, 10:12)

# Modes (reflective indicators)
mode.HM = rep("A", 5)

PLS.2 = plspm(Data.01.PPT, inner.HM, outer.HM, modes = mode.HM, scheme="path", scaled = TRUE)
summary(PLS.2)

# Plots

innerplot(PLS.2)
outerplot(PLS.2, what="loadings")

# Consistent PLS
# matrixpls

library(matrixpls)

TAM.Model<-'
PEU =~ q1 + q2 + q3
PU =~ q4 + q5 + q6
ATT =~ q7 + q8 + q9
BI =~ q10 + q11 + q12
ATT ~ PEU + PU
BI ~ ATT
'
R.TAM<-cor(Data.01.PPT[,1:12])

# Consistent PLS (PLSc)

TAM.output.01<-matrixpls(R.TAM, TAM.Model,
                            disattenuate = TRUE,
                            parametersReflective = estimator.plscLoadings)

summary(TAM.output.01)

# PLS

TAM.output.02<-matrixpls(R.TAM, TAM.Model)

summary(TAM.output.02)

cbind(TAM.output.01, TAM.output.02)


# Consistent PLS (PLSc) bootstrapping

TAM.boot.01<-matrixpls.boot(Data.01.PPT[,1:12], model = TAM.Model, disattenuate = TRUE, parametersReflective = estimator.plscLoadings, R = 1000)
print(TAM.boot.01)
summary(TAM.boot.01)

# PLS bootsrapping

TAM.boot.02<-matrixpls.boot(Data.01.PPT[,1:12], model = TAM.Model, R = 1000)
print(TAM.boot.02)
summary(TAM.boot.02)