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plspm (version 0.4.9)

Tools for Partial Least Squares Path Modeling (PLS-PM)

Description

Partial Least Squares Path Modeling (PLS-PM) analysis for both metric and non-metric data, as well as REBUS analysis.

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Version

Install

install.packages('plspm')

Monthly Downloads

3,223

Version

0.4.9

License

GPL-3

Issues

17

Pull Requests

3

Stars

71

Forks

26

Maintainer

Gaston Sanchez

Last Published

April 23rd, 2017

Functions in plspm (0.4.9)

cereals

Cereals datset
check_args

Check arguments for
check_data

Check Data
check_maxiter

Check maximum number of iterations
college

College datasets
dummy.G

Dummy by Giorgio
alpha

Cronbach's alpha
arizona

Arizona vegetation dataset
check_specs

Check specifications of PLS-PM algorithm
check_tol

Check tolerance threshold
get_boots

Performs bootstrap validation in
get_data_scaled

Scaling data outside
get_PLSR

Internal PLS regression (full data)
get_PLSR_NA

Internal PLS regression with missing values
get_locals_test

Local groups comparison test
get_ord_scale

Non-Metric Ordinal Scale
get_path_scheme

Calculate inner weighting path scheme
get_weights_nonmetric

Outer Weights Non-Metric Data
innerplot

Plot inner model
check_blocks

Check well defined blocks
check_boot

Check bootstrap options
futbol

Futbol dataset from Spain-England-Italy
check_model

Check congruence between inner and outer models
check_modes

Check modes
check_scaling

Check types of measurement scales and metric
check_scheme

Check scheme
get_ave

Calculate AVE (Average Variance Extracted)
get_GQI

Group Quality Index
get_effects

Path coefficient effects for
get_generals

Get general parameters
get_rank

Rank of a non-metric variable
plspm.fit

Basic results for Partial Least Squares Path Modeling
plspm.groups

Two Groups Comparison in PLS-PM
russa

Russett A
russb

Russett B
get_PLSRdoubleQ

get_PLSRdoubleQ
get_alpha

Calculate Cronbach's alpha
get_gof

Goodness-of-fit for
get_inner_summary

Inner summary assessment
is_missing

Presence of missing values
it.reb

Iterative steps of Response-Based Unit Segmentation (REBUS)
quantiplot

Quantification Plot
rebus.pls

Response Based Unit Segmentation (REBUS)
get_rho

Calculate Dillon-Goldstein's rho
local.models

PLS-PM for global and local models
mobile

ECSI Mobile Phone Provider dataset
get_boot_stats

Get data frame with bootstrap statistics
get_num_scale

Non-Metric Numerical Scale
get_numerics

Transform factors in MV into numeric
get_paths

Calculate path coefficients for
get_plsr1

PLS regression for
normalize

Normalize a vector
offense

Offense dataset
russett

Russett dataset
check_path

Check path matrix
check_plscomp

Check vector of PLS components (for non-metric plspm)
get_dummies

Dummy matrices for categorical manifest variables
get_dummy

Non-Metric Dummy
satisfaction

Satisfaction dataset
wines

Wines dataset
rescale

Rescale Latent Variable Scores
rho

Dillon-Goldstein's rho
technology

Technology data set
test_dataset

Test Data Set Availibility
test_null_weights

Test outer weights convergence within specified maxiter
unidim

Unidimensionality of blocks
get_metric

Type of metric based on scaling measurement
get_nom_scale

Non-Metric Nominal Scale
get_scores

Calculate Latent Variable Scores
get_treated_data

Apply corresponding treatment to MV
plot.plspm

Plots for PLS Path Models
plspm

PLS-PM: Partial Least Squares Path Modeling
get_manifests

Building data matrix with manifest variables
get_unidim

Unidimensionality of reflective blocks
get_weights

Outer Weights
orange

Orange Juice dataset
outerplot

Plot outer model
rebus.test

Permutation Test for REBUS Multi-Group Comparison
res.clus

Clustering on communality and structural residuals
test_factors

Test presence of factors
test_manifest_scaling

Test scaling of selected manifest variables
simdata

Simulated data for REBUS with two groups
spainfoot

Spanish football dataset