mvr objects: Regression
coefficients, fitted values, residuals, the model frame, 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, \dots)
## S3 method for class 'mvr':
fitted(object, \dots)
## S3 method for class 'mvr':
residuals(object, \dots)
## S3 method for class 'mvr':
model.matrix(object, \dots)
## S3 method for class 'mvr':
model.frame(formula, \dots)
prednames(object, intercept = FALSE)
respnames(object)
compnames(object, comps, explvar = FALSE, ...)
explvar(object)mvr object. The fitted model.cumulative = FALSE. Defaults to
FALSE.model.frame.mvr and model.matrix.mvr.coef.mvr returns an array of regression coefficients. fitted.mvr returns an array with fitted values.
residuals.mvr returns an array with residuals.
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,
or NULL if not available.
coef.mvr, fitted.mvr and residuals.mvr are
usually called through their generic functions coef,
fitted and residuals, respectively.)
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
$i = 1, \ldots, length(comps)$. 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.
fitted.mvr and residuals.mvr return the fitted values
and residuals, respectively. If the model was fitted with
na.action = na.exclude (or after setting the default
na.action to "na.exclude" with options),
the fitted values (or residuals) corresponding to excluded
observations are returned as NA; otherwise, they are omitted.
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. compnames can also extract component names
from score and loading matrices. If explvar = TRUE in compnames, the
explained variance for each component (if available) 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. It can also handle
score and loading matrices returned by scores and loadings.
mvr, coef, fitted,
residuals, model.frame,
model.matrix, na.omitdata(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)Run the code above in your browser using DataCamp Workspace