README.md

In gastonstat/plspm2: Tools for Partial Least Squares Path Modeling

plspm

plspm is an R package dedicated to Partial Least Squares Path Modeling (PLS-PM) analysis for both metric and non-metric data. Versions later than 4.0 include a whole new set of features to handle non-metric variables.

Installation

Step 1) In order to install plspm2 you need to use the R package devtools. Run in your R console:

# install "devtools"
install.packages("devtools") 
library(devtools)

Step 2) Then you need to install the packages tester and turner. Run in your R console:

# install "tester" and "turner"
install_github('tester', username='gastonstat')
install_github('turner', username='gastonstat')

Step 3) And finally, you install plspm2. Run in your R console:

# install "plspm2""
install_github('plspm2', username='gastonstat')

# load plspm2
library(plspm2)

PLS-PM with Metric Data

Typical example with a Customer Satisfaction Model

# load dataset satisfaction
data(satisfaction)

# define path matrix (inner model)
IMAG = c(0,0,0,0,0,0)
EXPE = c(1,0,0,0,0,0)
QUAL = c(0,1,0,0,0,0)
VAL = c(0,1,1,0,0,0)
SAT = c(1,1,1,1,0,0) 
LOY = c(1,0,0,0,1,0)
sat_path = rbind(IMAG, EXPE, QUAL, VAL, SAT, LOY)

# define list of blocks (outer model)
sat_blocks = list(1:5, 6:10, 11:15, 16:19, 20:23, 24:27)

# vector of modes (reflective indicators)
sat_mod = rep("A", 6) 

# apply plspm with bootstrap validation
satpls = plspm(satisfaction, sat_path, sat_blocks, sat_mod, scaled=FALSE, boot.val=TRUE)

# default print
satpls

# summary of results
summary(satpls)

# plot inner model results
plot(satpls, what = "inner")

# plot outer model loadings
plot(satpls, what = "loadings")

# plot outer model weights
plot(satpls, what = "weights")

PLS-PM with Non-Metric Data

Example with the classic Russett data (original data set)

# load dataset russett A
# (variable 'demo' as numeric)
data(russa)

# load dataset russett B
# (variable 'demo' as factor)
data(russb)

# russett all numeric
rus_path = rbind(c(0, 0, 0), c(0, 0, 0), c(1, 1, 0))
rownames(rus_path) = c("AGRI", "IND", "POLINS")
colnames(rus_path) = c("AGRI", "IND", "POLINS")
rus_blocks = list(1:3, 4:5, 6:9)
rus_scaling = list(c("NUM", "NUM", "NUM"),
                   c("NUM", "NUM"),
                   c("NUM", "NUM", "NUM", "NUM"))
rus_modes = c("A", "A", "A")

Example 1

PLS-PM using data set russa and scaling all 'NUM'

# PLS-PM using data set 'russa'
rus_pls1 = plspm(russa, rus_path, rus_blocks, rus_scaling, rus_modes, 
                 scheme = "centroid", plscomp=c(1,1,1), tol = 0.0000001)

rus_pls1

# outer model
rus_pls1$outer_model

# inner model
rus_pls1$inner_model

# scores
head(rus_pls1$scores)

# plot inner model
plot(rus_pls1)

Example 2

PLS-PM using data set russa, and different scaling

# new scaling
rus_scaling2 = list(c("NUM", "NUM", "NUM"),
                    c("ORD", "ORD"),
                    c("NUM", "NUM", "NUM", "NOM"))

# PLS-PM using data set 'russa'
rus_pls2 = plspm(russa, rus_path, rus_blocks, rus_scaling2, rus_modes, 
                 scheme = "centroid", plscomp=c(1,1,1), tol = 0.0000001)

# outer model
rus_pls2$outer_model

Example 3

Now let's use data set russb (it contains a factor!)

# take a peek
head(russb)

# PLS-PM using data set 'russb'
rus_pls3 = plspm(russb, rus_path, rus_blocks, rus_scaling2, rus_modes, 
                 scheme = "centroid", plscomp=c(1,1,1), tol = 0.0000001)

# outer model
rus_pls3$outer_model

Example 4

Now let's change modes

# modes new A
rus_modes2 = c("newA", "newA", "newA")

# PLS-PM using data set 'russa'
rus_pls4 = plspm(russa, rus_path, rus_blocks, rus_scaling2, rus_modes2, 
                 scheme = "centroid", plscomp=c(1,1,1), tol = 0.0000001)

# outer model
rus_pls4$outer_model

Example 5

Let's make things more interesting, flexible and versatile. How? What if you could have more freedom specifying the arguments? Now you can! Note that you can specify blocks using variables' names, the scaling types are NOT case senstive, neither are modes nor scheme. Isn't that cool?

# blocks
rus_blocchi = list(
   c("gini", "farm", "rent"),
   c("gnpr", "labo"),
   c("inst", "ecks", "death", "demo"))

# scaling
rus_scaling3 = list(c("numeric", "numeric", "numeric"),
                    c("ordinal", "ORDINAL"),
                    c("NuM", "numer", "NUM", "nominal"))

# modes new A
rus_modes3 = c("newa", "NEWA", "NewA")

# PLS-PM using data set 'russb'
rus_pls5 = plspm(russb, rus_path, rus_blocchi, rus_scaling3, rus_modes3, 
                 scheme = "CENTROID", plscomp=c(1,1,1), tol = 0.0000001)

# outer model
rus_pls5$outer_model

Authors Contact

Gaston Sanchez (gaston.stat at gmail.com)

Laura Trinchera (ltr at rouenbs.fr)

Giorgio Russolillo (giorgio.russolillo at cnam.fr)

gastonstat/plspm2 documentation built on May 16, 2019, 5:47 p.m.