coef.mvr {pls} | R Documentation |
Functions to extract information from mvr
objects: Regression
coefficients, the model frame, the the model matrix, names of the
variables and components, and the X variance explained by the
components.
## S3 method for class 'mvr': coef(object, comps = object$ncomp, intercept = FALSE, cumulative = TRUE, ...) ## S3 method for class 'mvr': model.matrix(object, ...) ## S3 method for class 'mvr': model.frame(formula, ...) prednames(object, intercept = FALSE) respnames(object) compnames(object, comps = 1:object$ncomp, explvar = FALSE) explvar(object)
object, formula |
an mvr object. The fitted model. |
comps |
vector of positive integers. The components to include in the coefficients or to extract the names of. |
intercept |
logical. Whether coefficients for the intercept should
be included. Ignored if cumulative = FALSE . Defaults to
FALSE . |
cumulative |
logical. Whether cumulative (the default) or individual coefficients for each component should be returned. See below. |
explvar |
logical. Whether the explained X variance should be appended to the component names. |
... |
other arguments sent to underlying functions. Currently
only used for model.frame.mvr and model.matrix.mvr . |
Except for coef.mvr
, these functions are mostly used inside
other functions.
coef.mvr
is used to extract the regression coefficients of a
model, i.e. the B in y = XB. An array of dimension
c(nxvar, nyvar, length(comps))
is returned.
If cumulative = TRUE
, coef()[,,comps[i]]
are
the coefficients for models with comps[i]
components, for
i = 1, ..., length(comps). Also, if intercept = TRUE
,
the first dimension is nxvar + 1, with the intercept
coefficients as the first row.
If cumulative = FALSE
, however, coef()[,,comps[i]]
are
the coefficients for a model with only the component comps[i]
,
i.e. the contribution of the component comps[i]
on the
regression coefficients.
model.frame.mvr
returns the model frame; i.e. a data frame with
all variables neccessary to generate the model matrix. See
model.frame
for details.
model.matrix.mvr
returns the (possibly coded) matrix used as
X in the fitting. See model.matrix
for
details.
prednames
, respnames
and compnames
extract the
names of the X variables, responses and components,
respectively. With intercept = TRUE
in prednames
,
the name of the intercept variable (i.e. "(Intercept)"
) is
returned as well. If explvar = TRUE
in compnames
, the
explained variance for each component is appended to the component
names. For optimal formatting of the explained variances when not all
components are to be used, one should specify the desired components
with the argument comps
.
explvar
extracts the amount of X variance (in per cent)
explained by for each component in the model.
coef.mvr
returns an array of regression coefficients.
model.frame.mvr
returns a data frame.
model.matrix.mvr
returns the X matrix.
prednames
, respnames
and compnames
return a
character vector with the corresponding names.
explvar
returns a numeric vector with the explained variances.
Ron Wehrens and Bjørn-Helge Mevik
data(NIR) mod <- pcr(y ~ X, data = NIR[NIR$train,], ncomp = 5) B <- coef(mod, comps = 3, intercept = TRUE) ## A manual predict method: stopifnot(drop(B[1,,] + NIR$X[!NIR$train,] %*% B[-1,,]) == drop(predict(mod, comps = 3, newdata = NIR[!NIR$train,]))) ## Note the difference in formatting: mod2 <- pcr(y ~ X, data = NIR[NIR$train,]) compnames(mod2, explvar = TRUE)[1:3] compnames(mod2, comps = 1:3, explvar = TRUE)