plspm.fit | R Documentation |
Estimate path models with latent variables by partial
least squares approach without providing the full list of
results as plspm()
. This might be helpful when
doing simulations, intensive computations, or when you
don't want the whole enchilada.
plspm.fit(Data, path_matrix, blocks, modes = NULL, scaling = NULL, scheme = "centroid", scaled = TRUE, tol = 1e-06, maxiter = 100, plscomp = NULL)
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 betwenn latent variables). |
blocks |
list of vectors with column indices or
column names from |
scaling |
optional list of string vectors indicating
the type of measurement scale for each manifest variable
specified in |
modes |
character vector indicating the type of
measurement for each block. Possible values are:
|
scheme |
string indicating the type of inner
weighting scheme. Possible values are |
scaled |
whether manifest variables should be
standardized. Only used when |
tol |
decimal value indicating the tolerance
criterion for the iterations ( |
maxiter |
integer indicating the maximum number of
iterations ( |
plscomp |
optional vector indicating the number of
PLS components (for each block) to be used when handling
non-metric data (only used if |
plspm.fit
performs the basic PLS algorithm and
provides limited results (e.g. outer model, inner model,
scores, and path coefficients).
The argument path_matrix
is a matrix of zeros and
ones that indicates the structural relationships between
latent variables. path_matrix
must be a lower
triangular matrix; it contains 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 |
path_coefs |
Matrix of path coefficients (this
matrix has a similar form as |
Gaston Sanchez, Giorgio Russolillo
Tenenhaus M., Esposito Vinzi V., Chatelin Y.M., and Lauro C. (2005) PLS path modeling. Computational Statistics & Data Analysis, 48, pp. 159-205.
Lohmoller J.-B. (1989) Latent variables path modeling with partial least squares. Heidelberg: Physica-Verlag.
Wold H. (1985) Partial Least Squares. In: Kotz, S., Johnson, N.L. (Eds.), Encyclopedia of Statistical Sciences, Vol. 6. Wiley, New York, pp. 581-591.
Wold H. (1982) Soft modeling: the basic design and some extensions. In: K.G. Joreskog & H. Wold (Eds.), Systems under indirect observations: Causality, structure, prediction, Part 2, pp. 1-54. Amsterdam: Holland.
innerplot
, plot.plspm
,
## Not run: ## typical example of PLS-PM in customer satisfaction analysis ## model with six LVs and reflective indicators # 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_path = rbind(IMAG, EXPE, QUAL, VAL, SAT, LOY) # outer model list sat_blocks = list(1:5, 6:10, 11:15, 16:19, 20:23, 24:27) # vector of reflective modes sat_modes = rep("A", 6) # apply plspm.fit satpls = plspm.fit(satisfaction, sat_path, sat_blocks, sat_modes, scaled=FALSE) # summary of results summary(satpls) # default plot (inner model) plot(satpls) ## End(Not run)
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