mvr {pls} | R Documentation |
Functions to perform partial least squares regression (PLSR) or principal component regression (PCR), with a formula interface. Cross-validation can be used. Prediction, model extraction, plot, print and summary methods exist.
mvr(formula, ncomp, data, subset, na.action, method = pls.options()$mvralg, scale = FALSE, validation = c("none", "CV", "LOO"), model = TRUE, x = FALSE, y = FALSE, ...) plsr(..., method = pls.options()$plsralg) pcr(..., method = pls.options()$pcralg)
formula |
a model formula. Most of the lm formula
constructs are supported. See below. |
ncomp |
the number of components to include in the model (see below). |
data |
an optional data frame with the data to fit the model from. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data contain missing values. |
method |
the multivariate regression method to be used. If
"model.frame" , the model frame is returned. |
scale |
numeric vector, or logical. If numeric vector, X
is scaled by dividing each variable with the corresponding element
of scale . If scale is TRUE , X is scaled
by dividing each variable by its sample standard deviation. If
cross-validation is selected, scaling by the standard deviation is
done for every segment. |
validation |
character. What kind of (internal) validation to use. See below. |
model |
a logical. If TRUE , the model frame is returned. |
x |
a logical. If TRUE , the model matrix is returned. |
y |
a logical. If TRUE , the response is returned. |
... |
additional arguments, passed to the underlying fit
functions, and mvrCv . |
The functions fit PLSR or PCR models with 1, …,
ncomp
number of components. Multi-response models are fully
supported.
The type of model to fit is specified with the method
argument. Four PLSR algorithms are available: the kernel algorithm
("kernelpls"
), the wide kernel algorithm ("widekernelpls"
),
SIMPLS ("simpls"
) and the classical
orthogonal scores algorithm ("oscorespls"
). One PCR algorithm
is available: using the singular value decomposition ("svdpc"
).
If method
is "model.frame"
, the model frame is returned.
The functions pcr
and plsr
are wrappers for mvr
,
with different values for method
.
The formula
argument should be a symbolic formula of the form
response ~ terms
, where response
is the name of the
response vector or matrix (for multi-response models) and terms
is the name of one or more predictor matrices, usually separated by
+
, e.g., water ~ FTIR
or y ~ X + Z
. See
lm
for a detailed description. The named
variables should exist in the supplied data
data frame or in
the global environment. Note: Do not use mvr(mydata$y ~
mydata$X, ...)
, instead use mvr(y ~ X, data = mydata,
...)
. Otherwise, predict.mvr
will not work properly.
The chapter Statistical models in R of the manual An
Introduction to R distributed with R is a good reference on
formulas in R.
The number of components to fit is specified with the argument
ncomp
. It this is not supplied, the maximal number of
components is used (taking account of any cross-validation).
If validation = "CV"
, cross-validation is performed. The number and
type of cross-validation segments are specified with the arguments
segments
and segment.type
. See mvrCv
for
details. If validation = "LOO"
, leave-one-out cross-validation
is performed. It is an error to specify the segments when
validation = "LOO"
is specified.
Note that the cross-validation is optimised for speed, and some
generality has been sacrificed. Especially, the model matrix is
calculated only once for the complete cross-validation, so models like
y ~ msc(X)
will not be properly cross-validated. However,
scaling requested by scale = TRUE
is properly cross-validated.
For proper cross-validation of models where the model matrix must be
updated/regenerated for each segment, use the separate function
crossval
.
If method = "model.frame"
, the model frame is returned.
Otherwise, an object of class mvr
is returned.
The object contains all components returned by the underlying fit
function. In addition, it contains the following components:
validation |
if validation was requested, the results of the
cross-validation. See mvrCv for details. |
na.action |
if observations with missing values were removed,
na.action contains a vector with their indices. The
class of this vector is used by functions like fitted to
decide how to treat the observations. |
ncomp |
the number of components of the model. |
method |
the method used to fit the model. See the argument
method for possible values. |
scale |
if scaling was requested (with scale ), the
scaling used. |
call |
the function call. |
terms |
the model terms. |
model |
if model = TRUE , the model frame. |
x |
if x = TRUE , the model matrix. |
y |
if y = TRUE , the model response. |
Ron Wehrens and Bjørn-Helge Mevik
Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.
kernelpls.fit
,
widekernelpls.fit
,
simpls.fit
,
oscorespls.fit
,
svdpc.fit
,
mvrCv
,
crossval
,
loadings
,
scores
,
loading.weights
,
coef.mvr
,
predict.mvr
,
R2
,
MSEP
,
RMSEP
,
plot.mvr
data(yarn) ## Default methods: yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV") yarn.pls <- plsr(density ~ NIR, 6, data = yarn, validation = "CV") ## Alternative methods: yarn.oscorespls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV", method = "oscorespls") yarn.simpls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV", method = "simpls") data(oliveoil) sens.pcr <- pcr(sensory ~ chemical, ncomp = 4, scale = TRUE, data = oliveoil) sens.pls <- plsr(sensory ~ chemical, ncomp = 4, scale = TRUE, data = oliveoil)