Estimate score and path coefficient applying a partial least squares approach
pls(
x,
inner,
outer,
modes,
scheme = "path",
scaled = TRUE,
tol = 1e-05,
iter = 100,
...
)a list with score, path coefs and R2
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"
The length of modes must be equal to the length of blocks
string indicating the type of inner weighting
scheme. Possible values are "centroid", "factorial", or
"path"
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
############################################################################################
Internal function