enspls.od | R Documentation |
Outlier detection with ensemble sparse partial least squares.
enspls.od(
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 four components:
error.mean
- error mean for all samples (absolute value)
error.median
- error median for all samples
error.sd
- error sd for all samples
predict.error.matrix
- the original prediction error matrix
To maximize the probablity that each observation can
be selected in the test set (thus the prediction uncertainty
can be measured), please try setting a large reptimes
.
Nan Xiao <https://nanx.me>
See enspls.fs
for measuring feature importance
with ensemble sparse partial least squares regressions.
See enspls.fit
for fitting ensemble sparse
partial least squares regression models.
data("logd1k")
x <- logd1k$x
y <- logd1k$y
set.seed(42)
od <- enspls.od(
x, y,
reptimes = 5, maxcomp = 3,
alpha = c(0.3, 0.6, 0.9)
)
plot(od, prob = 0.1)
plot(od, criterion = "sd", sdtimes = 1)
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