matrixpls.plspm: A plspm compatibility wrapper for matrixpls
In matrixpls: Matrix-Based Partial Least Squares Estimation

Description Usage Arguments Details Value References Examples

matrixpls.plspm mimics plspm function of the plspm package. The arguments and their default values and the output of the function are identical with plspm function, but internally the function uses matrixpls estimation.

matrixpls.plspm(
  Data,
  path_matrix,
  blocks,
  modes = NULL,
  scheme = "centroid",
  scaled = TRUE,
  tol = 1e-06,
  maxiter = 100,
  boot.val = FALSE,
  br = NULL,
  dataset = TRUE
)

`Data`	matrix or data frame containing the manifest variables.
`path_matrix`	A square (lower triangular) boolean matrix representing the inner model (i.e. the path relationships between latent variables).
`blocks`	list of vectors with column indices or column names from `Data` indicating the sets of manifest variables forming each block (i.e. which manifest variables correspond to each block).
`modes`	character vector indicating the type of measurement for each block. Possible values are: `"A", "B"`. The length of `modes` must be equal to the length of `blocks`.
`scheme`	string indicating the type of inner weighting scheme. Possible values are `"centroid"`, `"factorial"`, or `"path"`.
`scaled`	whether manifest variables should be standardized. When (`TRUE`, data is scaled to standardized values (mean=0 and variance=1). The variance is calculated dividing by `N` instead of `N-1`).
`tol`	decimal value indicating the tolerance criterion for the iterations (`tol=0.000001`). Can be specified between 0 and 0.001.
`maxiter`	integer indicating the maximum number of iterations (`maxiter=100` by default). The minimum value of `maxiter` is 100.
`boot.val`	whether bootstrap validation should be performed. (`FALSE` by default).
`br`	number bootstrap resamples. Used only when `boot.val=TRUE`. When `boot.val=TRUE`, the default number of re-samples is 100.
`dataset`	whether the data matrix used in the computations should be retrieved (`TRUE` by default).

The function matrixpls.plspm calculates indicator weights and estimates a model identically to the plspm function. In contrast to the matrixpls function that provides only weights and parameter estimates, this function also reports multiple post-estimation statistics. Because of this matrixpls.plspm is substantially less efficient than the matrixpls function.

The argument path_matrix is a matrix of zeros and ones that indicates the structural relationships between composites. This must be a lower triangular matrix. path_matrix will contain a 1 when column j affects row i, 0 otherwise.

An object of class "plspm".

`outer_model`	Results of the outer model. Includes: outer weights, standardized loadings, communalities, and redundancies
`inner_model`	Results of the inner (structural) model. Includes: path coeffs and R-squared for each endogenous latent variable
`scores`	Matrix of latent variables used to estimate the inner model. If `scaled=FALSE` then `scores` are latent variables calculated with the original data (non-standardized).
`path_coefs`	Matrix of path coefficients (this matrix has a similar form as `path_matrix`)
`crossloadings`	Correlations between the latent variables and the manifest variables (also called crossloadings)
`inner_summary`	Summarized results of the inner model. Includes: type of LV, type of measurement, number of indicators, R-squared, average communality, average redundancy, and average variance extracted
`effects`	Path effects of the structural relationships. Includes: direct, indirect, and total effects
`unidim`	Results for checking the unidimensionality of blocks (These results are only meaningful for reflective blocks)
`gof`	Goodness-of-Fit index
`data`	Data matrix containing the manifest variables used in the model. Only available when `dataset=TRUE`
`boot`	List of bootstrapping results; only available when argument `boot.val=TRUE`

Sanchez, G. (2013). PLS Path Modeling with R. Retrieved from http://www.gastonsanchez.com/PLS Path Modeling with R.pdf #'

cores <- getOption("mc.cores")
options(mc.cores=2)

# Run the example from plspm package

# load dataset satisfaction
data(satisfaction)
# inner model matrix
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_inner = rbind(IMAG, EXPE, QUAL, VAL, SAT, LOY)
# outer model list
sat_outer = list(1:5, 6:10, 11:15, 16:19, 20:23, 24:27)
# vector of modes (reflective indicators)
sat_mod = rep("A", 6)

# apply matrixpls
matrixpls.res <- matrixpls.plspm(satisfaction, sat_inner, sat_outer, sat_mod,
                                 scaled=FALSE, boot.val=FALSE)

print(summary(matrixpls.res))

options(mc.cores=cores)