sPLSda {sgPLS} | R Documentation |
Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)
Description
Function to perform sparse Partial Least Squares to classify samples (supervised analysis) and select variables.
Usage
sPLSda(X, Y, ncomp = 2, keepX = rep(ncol(X), ncomp),
max.iter = 500, tol = 1e-06)
Arguments
X |
numeric matrix of predictors. |
Y |
a factor or a class vector for the discrete outcome. |
ncomp |
the number of components to include in the model (see Details). |
keepX |
numeric vector of length |
max.iter |
integer, the maximum number of iterations. |
tol |
a positive real, the tolerance used in the iterative algorithm. |
Details
sPLSda
function fit sPLS models with 1, \ldots ,
ncomp
components
to the factor or class vector Y
. The appropriate indicator (dummy)
matrix is created.
Value
sPLSda
returns an object of class "sPLSda"
, a list
that contains the following components:
X |
the centered and standardized original predictor matrix. |
Y |
the centered and standardized indicator response vector or matrix. |
ind.mat |
the indicator matrix. |
ncomp |
the number of components included in the model. |
keepX |
number of |
mat.c |
matrix of coefficients to be used internally by |
variates |
list containing the variates. |
loadings |
list containing the estimated loadings for the |
names |
list containing the names to be used for individuals and variables. |
tol |
the tolerance used in the iterative algorithm, used for subsequent S3 methods |
max.iter |
the maximum number of iterations, used for subsequent S3 methods |
iter |
Number of iterations of the algorthm for each component |
Author(s)
Benoit Liquet and Pierre Lafaye de Micheaux.
References
On sPLS-DA: Le Cao, K.-A., Boitard, S. and Besse, P. (2011). Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 12:253.
See Also
sPLS
, summary
,
plotIndiv
, plotVar
,
cim
, network
, predict
, perf
and http://www.mixOmics.org for more details.
Examples
### Examples from mixOmics packages
data(liver.toxicity)
X <- as.matrix(liver.toxicity$gene)
# Y will be transformed as a factor in the function,
# but we set it as a factor to set up the colors.
Y <- as.factor(liver.toxicity$treatment[, 4])
model <- sPLSda(X, Y, ncomp = 2, keepX = c(20, 20))