coef.mvr {pls} | R Documentation |
Functions to extract the regression coefficients or the model matrix
from mvr
objects.
## S3 method for class 'mvr': coef(object, comps = object$ncomp, intercept = FALSE, cumulative = TRUE, ...) ## S3 method for class 'mvr': model.matrix(object, ...)
object |
an mvr object. The fitted model. |
comps |
vector of positive integers. The components to include in the coefficients. Defaults to the number of components used for fitting the model. |
intercept |
logical. Whether coefficients for the intercept should
be included. Ignored if cumulative = FALSE . Defaults to
FALSE . |
cumulative |
logical. Whether cumulative (the default) or individual coefficients for each component should be returned. See below. |
... |
other arguments sent to underlying functions. Currently
only used for model.matrix.mvr . |
coef.mvr
is used to extract the regression coefficients of a
model, i.e. the B in y = XB. An array of dimension
c(nxvar, nyvar, length(comps))
is returned.
If cumulative = TRUE
, coef()[,,comps[i]]
are
the coefficients for models with comps[i]
components, for
i = 1, ..., length(comps). Also, if intercept = TRUE
,
the first dimension is nxvar + 1, with the intercept
coefficients as the first row.
If cumulative = FALSE
, however, coef()[,,comps[i]]
are
the coefficients for a model with only the component comps[i]
,
i.e. the contribution of the component comps[i]
on the
regression coefficients.
model.matrix.mvr
returns the (possibly coded) matrix used as
X in the fitting.
coef.mvr
returns an array of regression coefficients.
model.matrix.mvr
returns the X matrix.
Ron Wehrens and Bjørn-Helge Mevik
data(NIR) mod <- pcr(y ~ X, data = NIR[NIR$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,])))