Extract Information From a Fitted PLSR or PCR Model

Functions to extract information from 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.

multivariate, regression
"coef"(object, ncomp = object$ncomp, comps, intercept = FALSE, ...) "fitted"(object, ...) "residuals"(object, ...) "model.matrix"(object, ...) "model.frame"(formula, ...) prednames(object, intercept = FALSE) respnames(object) compnames(object, comps, explvar = FALSE, ...) explvar(object)
object, formula
an mvr object. The fitted model.
ncomp, comps
vector of positive integers. The components to include in the coefficients or to extract the names of. See below.
logical. Whether coefficients for the intercept should be included. Ignored if comps is specified. Defaults to FALSE.
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.

These functions are mostly used inside other functions. (Functions 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 $y = XB$ (for the $Q$ in $y = TQ$ where $T$ is the scores, see Yloadings). An array of dimension c(nxvar, nyvar, length(ncomp)) or c(nxvar, nyvar, length(comps)) is returned.

If comps is missing (or is NULL), coef()[,,ncomp[i]] are the coefficients for models with ncomp[i] components, for $i = 1, \ldots, length(ncomp)$. Also, if intercept = TRUE, the first dimension is $nxvar + 1$, with the intercept coefficients as the first row.

If comps is given, 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.


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.

See Also

mvr, coef, fitted, residuals, model.frame, model.matrix, na.omit

  • coef.mvr
  • fitted.mvr
  • residuals.mvr
  • model.frame.mvr
  • model.matrix.mvr
  • prednames
  • respnames
  • compnames
  • explvar
mod <- pcr(density ~ NIR, data = yarn[yarn$train,], ncomp = 5)
B <- coef(mod, ncomp = 3, intercept = TRUE)
## A manual predict method:
stopifnot(drop(B[1,,] + yarn$NIR[!yarn$train,] %*% B[-1,,]) ==
          drop(predict(mod, ncomp = 3, newdata = yarn[!yarn$train,])))

## Note the difference in formatting:
mod2 <- pcr(density ~ NIR, data = yarn[yarn$train,])
compnames(mod2, explvar = TRUE)[1:3]
compnames(mod2, comps = 1:3, explvar = TRUE)
Documentation reproduced from package pls, version 2.5-0, License: GPL-2

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