MSEP {pls} | R Documentation |
Functions to estimate the mean squared error of prediction (MSEP), root mean squared error of prediction (RMSEP) and R^2 for fitted PCR and PLSR models. Test-set, cross-validation and calibration-set estimates are implemented.
MSEP(object, estimate, newdata, comps = 1:object$ncomp, cumulative = TRUE, intercept = cumulative, se = FALSE, ...) RMSEP(...) R2(object, estimate, newdata, comps = 1:object$ncomp, cumulative = TRUE, intercept = cumulative, se = FALSE, ...)
object |
a mvr object |
estimate |
a character vector. Which estimators to use.
Should be a subset of c("all", "train", "CV", "adjCV",
"test") . "adjCV" is only available for (R)MSEP. See
below for how the estimators are chosen. |
newdata |
a data frame with test set data. |
comps |
a vector of positive integers. The components or number of components to use. See below. |
cumulative |
logical. See below. |
intercept |
logical. Whether estimates for a model with zero components should be returned as well. |
se |
logical. Whether estimated standard errors of the estimates should be calculated. Not implemented yet. |
... |
further arguments sent to underlying functions or (for
RMSEP ) to MSEP |
RMSEP
simply calls MSEP
and takes the square root of the
estimates. It therefore accepts the same arguments as MSEP
.
Several estimators can be used. "train" is the training
or calibration data estimate, also called (R)MSEC. For R2
,
this is the unadjusted R^2. It is
overoptimistic and should not be used for assessing models.
"CV" is the cross-validation estimate, and "adjCV" (for
RMSEP
and MSEP
) is
the bias-corrected cross-validation estimate. They can only be
calculated if the model has been cross-validated.
Finally, "test" is the test set estimate, using newdata
as test set.
Which estimators to use is decided as follows. If
estimate
is not specified, the test set estimate is returned if
newdata
is specified, otherwise the CV and adjusted CV (for
RMSEP
and MSEP
)
estimates if the model has been cross-validated, otherwise the
training data estimate. If estimate
is "all", all
possible estimates are calculated. Otherwise, the specified estimates
are calculated.
Several model sizes can also be specified. If cumulative
is
TRUE
(default), length(comps)
models are used, with
comps[1]
components, ..., comps[length(comps)]
components. Otherwise, a single model with the components
comps[1]
, ..., comps[length(comps)]
is used.
If intercept
is TRUE
, a model with zero components is
also used (in addition to the above). For R2
, this is simply
defined as 0.
An object of class "mvrVal"
, with components
val |
three-dimensional array of estimates. The first dimension is the different estimators, the second is the response variables and the third is the models. |
type |
"MSEP" , "RMSEP" or "R2" . |
comps |
the components specified, with 0 prepended if
intercept is TRUE . |
call |
the function call |
Ron Wehrens and Bjørn-Helge Mevik
Mevik, B.-H., Cederkvist, H. R. (2004) Mean Squared Error of Prediction (MSEP) Estimates for Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR). Journal of Chemometrics, 18(9), 422–429.
mvr
, crossval
, mvrCv
,
validationplot
, plot.mvrVal
data(sensory) mod <- plsr(Panel ~ Quality, ncomp = 4, data = sensory, CV = TRUE, length.seg = 1) RMSEP(mod) ## Not run: plot(R2(mod))