example/matrixpls.plsc-example.R

In mronkko/matrixpls: Matrix-Based Partial Least Squares Estimation

# Run the education example from the book

# Sanchez, G. (2013) PLS Path Modeling with R
# Trowchez Editions. Berkeley, 2013. 
# http://www.gastonsanchez.com/PLS Path Modeling with R.pdf

education <- read.csv("http://www.gastonsanchez.com/education.csv")

Support <- c(0, 0, 0, 0, 0, 0)
Advising <- c(0, 0, 0, 0, 0, 0)
Tutoring <- c(0, 0, 0, 0, 0, 0)
Value <- c(1, 1, 1, 0, 0, 0)
# Omit two paths (compared to the model in the book) to achieve 
# identification of the 2SLS analysis
Satisfaction <- c(0, 0, 1, 1, 0, 0)
Loyalty <- c(0, 0, 0, 0, 1, 0)

inner <- rbind(Support, Advising, Tutoring, Value, Satisfaction, Loyalty)


reflective <- diag(6)[c(rep(1,4),
                        rep(2,4),
                        rep(3,4),
                        rep(4,4),
                        rep(5,3),
                        rep(6,4)),]
formative <- matrix(0, 6, 23)

colnames(inner) <- colnames(reflective) <- rownames(formative) <- rownames(inner)
rownames(reflective) <- colnames(formative) <- colnames(education)[2:24]

education.model <- list(inner = inner,
              reflective = reflective,
              formative = formative)

# Reverse code two variables
education[,c("sup.under","loy.asha")] <- - education[,c("sup.under","loy.asha")]

S <- cor(education[,2:24])

# PLSc with OLS regression

education.out <- matrixpls(S,education.model,
                      disattenuate = TRUE,
                      parametersReflective = estimator.plscLoadings)

# PLSc with 2SLS regresssion

education.out2 <- matrixpls(S,education.model,
                      disattenuate = TRUE,
                      parametersReflective = estimator.plscLoadings,
                      parametersInner = estimator.tsls)


# Disattenuated regression with unit-weighted scales and exploratory factor analysis
# reliability estimates (with unconstrained MINRES estimator)

education.out3 <- matrixpls(S,education.model,
                       disattenuate = TRUE,
                       weightFun = weightFun.fixed,
                       parametersReflective = estimator.efaLoadings)

# Disattenuated GSCA with 2SLS regression after disattenuated based on 
# confirmatory factor analysis reliability estimates


education.out4 <- matrixpls(S,education.model,
                       disattenuate = TRUE,
                       innerEstim = innerEstim.gsca,
                       outerEstim = outerEstim.gsca,
                       parametersInner = estimator.tsls,
                       parametersReflective = estimator.cfaLoadings)


# Compare the results

cbind(PLSc = education.out, PLSc_2sls = education.out2, 
      DR = education.out3, GSCAc = education.out4)

# Compare the reliability estimates

cbind(PLSc = attr(education.out,"Q"), PLSc_2sls = attr(education.out2,"Q"), 
      DR = attr(education.out3,"Q"), GSCAc = attr(education.out4,"Q"))