enspls.fit {enpls} | R Documentation |
Ensemble sparse partial least squares regression.
enspls.fit(x, y, maxcomp = 5L, cvfolds = 5L, alpha = seq(0.2, 0.8,
0.2), reptimes = 500L, method = c("mc", "boot"), ratio = 0.8,
parallel = 1L)
x |
Predictor matrix. |
y |
Response vector. |
maxcomp |
Maximum number of components included within each model.
If not specified, will use |
cvfolds |
Number of cross-validation folds used in each model
for automatic parameter selection, default is |
alpha |
Parameter (grid) controlling sparsity of the model.
If not specified, default is |
reptimes |
Number of models to build with Monte-Carlo resampling or bootstrapping. |
method |
Resampling method. |
ratio |
Sampling ratio used when |
parallel |
Integer. Number of CPU cores to use.
Default is |
A list containing all sparse partial least squares model objects.
Nan Xiao <https://nanx.me>
See enspls.fs
for measuring feature importance
with ensemble sparse partial least squares regressions.
See enspls.od
for outlier detection with ensemble
sparse partial least squares regressions.
data("logd1k")
x <- logd1k$x
y <- logd1k$y
set.seed(42)
fit <- enspls.fit(
x, y,
reptimes = 5, maxcomp = 3,
alpha = c(0.3, 0.6, 0.9)
)
print(fit)
predict(fit, newx = x)