Estimate path models with latent variables by partial least squares approach (for both metric and non-metric data)
plspm(
Data,
path_matrix,
blocks,
modes = NULL,
scaling = NULL,
scheme = "centroid",
scaled = TRUE,
tol = 1e-06,
maxiter = 100,
plscomp = NULL,
boot.val = FALSE,
br = NULL,
dataset = TRUE
)matrix or data frame containing the manifest variables.
A square (lower triangular) boolean matrix representing the inner model (i.e. the path relationships between latent variables).
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).
character vector indicating the type of measurement for each
block. Possible values are: "A", "B", "newA", "PLScore", "PLScow".
The length of modes must be equal to the length of blocks.
optional argument for runing the non-metric approach;
it is a list of string vectors indicating the type of
measurement scale for each manifest variable specified in blocks.
scaling must be specified when working with non-metric variables.
Possible values: "num" (linear transformation,
suitable for numerical variables), "raw" (no transformation),
"nom" (non-monotonic transformation, suitable for nominal variables),
and "ord" (monotonic transformation, suitable for ordinal variables).
string indicating the type of inner weighting
scheme. Possible values are "centroid", "factorial", or
"path".
whether manifest variables should be standardized.
Only used when scaling = NULL. When (TRUE, data is
scaled to standardized values (mean=0 and variance=1).
The variance is calculated dividing by N instead of N-1).
decimal value indicating the tolerance criterion for the
iterations (tol=0.000001). Can be specified between 0 and 0.001.
integer indicating the maximum number of iterations
(maxiter=100 by default). The minimum value of maxiter is 100.
optional vector indicating the number of PLS components
(for each block) to be used when handling non-metric data
(only used if scaling is provided)
whether bootstrap validation should be performed.
(FALSE by default).
number bootstrap resamples. Used only
when boot.val=TRUE. When boot.val=TRUE, the default number of
re-samples is 100.
whether the data matrix used in the computations should be
retrieved (TRUE by default).
Internal function