Description Usage Arguments Details Value Note Author(s) See Also Examples
Functions to extract R2 and RMSEP from autopls
objects, for
significance testing based on jackknife variance estimates for regression
1 2 3 4 5 6 7 8  ## S3 method for class 'autopls'
R2(object, estimate, nc = 'inherit', ic = FALSE, ...)
## S3 method for class 'autopls'
RMSEP(object, estimate, nc = 'inherit', ic = FALSE, ...)
jack.test.autopls (object, nc = 'inherit')
metaval (object, method, estimate, ic)
repeatedCV (object, k = 100, segments = 4)
clusterCV (object, valist)

object 
object of class 
method 
character. Should be or 'R2' or 'RMSEP' 
estimate 
character vector. Which estimators to use. In 
nc 

ic 
logical. Specifies whether estimates for a model with zero components should be returned 
k 
number of crossvalidations used in 
segments 
number of crossvalidation segments used in 
valist 
list of segments. The elements are vectors of plots assigned to a cluster of samples 
... 
Arguments to be passed to methods 
Some of these functions are just convenience wrappers for mvrVal
functions and for the jack.test
function in package pls.
More details are given here: mvrVal
,
jack.test
. Other functions are specific
autopls
functions. metaval
is used for a summary of validation
results during backselection. repeatedCV
is a meta crossvalidation
(repeated tenfold crossvalidation). clusterCV
is a leaveonesiteout
crossvalidation to avoid effects of spatial or other autocorrelation. The
elements of the list should be integer vectors specifying the indices of the
segments.
see mvrVal
and jack.test
.
The main difference is a reduced selection of functions (see above) and the
possibility to inherit a number of latent vectors from the autopls
object.
The metaval
function provides a matrix overview of model results for
all iterations and numbers of latent vectors in an autopls
object.
repeatedCV
provides results and basic statistics for repeated
crossvalidation runs.
If you want to make full use of the mvrVal
functions in the pls
package assign class mvr
to the model object.
Sebastian Schmidtlein, linking to code from package pls by Ron Wehrens and BjørnHelge Mevik.
mvrVal
, jack.test
,
autopls
, repCV
,
mvr_dcv
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## load predictor and response data to the current environment
data(murnau.X)
data(murnau.Y)
## call autopls with the standard options
model<autopls (murnau.Y ~ murnau.X)
## Validation
R2 (model)
R2 (model, nc = 'all')
RMSEP (model)
metaval (model, 'R2', 'CV', ic = FALSE)
## Jackknife test
jack.test.autopls (model)
## Meta crossvalidation
repeatedCV (model)

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