mvrVal  R Documentation 
Functions to estimate the mean squared error of prediction (MSEP), root mean squared error of prediction (RMSEP) and R^2 (A.K.A. coefficient of multiple determination) for fitted PCR and PLSR models. Testset, crossvalidation and calibrationset estimates are implemented.
mvrValstats( object, estimate, newdata, ncomp = 1:object$ncomp, comps, intercept = cumulative, se = FALSE, ... ) R2(object, ...) ## S3 method for class 'mvr' R2( object, estimate, newdata, ncomp = 1:object$ncomp, comps, intercept = cumulative, se = FALSE, ... ) MSEP(object, ...) ## S3 method for class 'mvr' MSEP( object, estimate, newdata, ncomp = 1:object$ncomp, comps, intercept = cumulative, se = FALSE, ... ) RMSEP(object, ...) ## S3 method for class 'mvr' RMSEP(object, ...)
object 
an 
estimate 
a character vector. Which estimators to use. Should be a
subset of 
newdata 
a data frame with test set data. 
ncomp, comps 
a vector of positive integers. The components or number of components to use. 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
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 crossvalidation estimate, and
"adjCV"
(for RMSEP
and MSEP
) is the biascorrected
crossvalidation estimate. They can only be calculated if the model has
been crossvalidated. Finally, "test"
is the test set estimate,
using newdata
as test set.
Which estimators to use is decided as follows (see below for
mvrValstats
). 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 crossvalidated, 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 comps
is missing (or
is NULL
), length(ncomp)
models are used, with ncomp[1]
components, ..., ncomp[length(ncomp)]
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).
The R^2 values returned by "R2"
are calculated as 1 
SSE/SST, where SST is the (corrected) total sum of squares of the
response, and SSE is the sum of squared errors for either the fitted
values (i.e., the residual sum of squares), test set predictions or
crossvalidated predictions (i.e., the PRESS). For estimate =
"train"
, this is equivalent to the squared correlation between the fitted
values and the response. For estimate = "train"
, the estimate is
often called the prediction R^2.
mvrValstats
is a utility function that calculates the statistics
needed by MSEP
and R2
. It is not intended to be used
interactively. It accepts the same arguments as MSEP
and R2
.
However, the estimate
argument must be specified explicitly: no
partial matching and no automatic choice is made. The function simply
calculates the types of estimates it knows, and leaves the other untouched.
mvrValstats
returns a list with components
threedimensional array of SSE values. The first dimension is the different estimators, the second is the response variables and the third is the models.
matrix of SST values. The first dimension is the different estimators and the second is the response variables.
a numeric vector giving the number of objects used for each estimator.
the components specified, with 0
prepended
if intercept
is TRUE
.
TRUE
if
comps
was NULL
or not specified.
The other functions return an object of class "mvrVal"
, with
components
threedimensional array of estimates. The first dimension is the different estimators, the second is the response variables and the third is the models.
"MSEP"
,
"RMSEP"
or "R2"
.
the components specified, with
0
prepended if intercept
is TRUE
.
TRUE
if comps
was NULL
or not
specified.
the function call
Ron Wehrens and BjørnHelge 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(oliveoil) mod < plsr(sensory ~ chemical, ncomp = 4, data = oliveoil, validation = "LOO") RMSEP(mod) ## Not run: plot(R2(mod))
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