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### extract.R: Extraction functions
## coef.mvr: Extract the base variable regression coefficients from
## an mvr object.
coef.mvr <- function(object, ncomp = object$ncomp, comps, intercept = FALSE,
...)
{
if (missing(comps) || is.null(comps)) {
## Cumulative coefficients:
B <- object$coefficients[,,ncomp, drop=FALSE]
if (isTRUE(intercept)) { # Intercept only has meaning for
# cumulative coefficients
dB <- dim(B)
dB[1] <- dB[1] + 1
dnB <- dimnames(B)
dnB[[1]] <- c("(Intercept)", dnB[[1]])
BInt <- array(dim = dB, dimnames = dnB)
BInt[-1,,] <- B
for (i in seq(along = ncomp))
BInt[1,,i] <- object$Ymeans - object$Xmeans %*% B[,,i]
B <- BInt
}
} else {
## Individual coefficients:
B <- object$coefficients[,,comps, drop=FALSE]
g1 <- which(comps > 1)
## Indiv. coef. must be calculated since object$coefficients is
## cumulative coefs.
B[,,g1] <- B[,,g1, drop=FALSE] -
object$coefficients[,,comps[g1] - 1, drop=FALSE]
dimnames(B)[[3]] <- paste("Comp", comps)
}
return(B)
}
## fitted.mvr: Extract the fitted values. It is needed because the case
## na.action == "na.exclude" must be treated differently from what is done
## in fitted.default.
fitted.mvr <- function(object, ...) {
if (inherits(object$na.action, "exclude")) {
naExcludeMvr(object$na.action, object$fitted.values)
} else {
object$fitted.values
}
}
## residuals.mvr: Extract the residuals. It is needed because the case
## na.action == "na.exclude" must be treated differently from what is done
## in residuals.default.
residuals.mvr <- function(object, ...) {
if (inherits(object$na.action, "exclude")) {
naExcludeMvr(object$na.action, object$residuals)
} else {
object$residuals
}
}
## naExcludeMvr: Perform the equivalent of naresid.exclude and
## napredict.exclude on three-dimensional arrays where the first dimension
## corresponds to the observations.
## Almost everything here is lifted verbatim from naresid.exclude (R 2.2.0)
naExcludeMvr <- function(omit, x, ...) {
if (length(omit) == 0 || !is.numeric(omit))
stop("invalid argument 'omit'")
if (length(x) == 0)
return(x)
n <- nrow(x)
keep <- rep.int(NA, n + length(omit))
keep[-omit] <- 1:n
x <- x[keep,,, drop = FALSE] # This is where the real difference is!
temp <- rownames(x)
if (length(temp)) {
temp[omit] <- names(omit)
rownames(x) <- temp
}
return(x)
}
## loadings is in stats, but doesn't work for prcomp objects, and is not
## generic, so we build our own:
loadings <- function(object, ...) UseMethod("loadings")
loadings.default <- function(object, ...) {
L <- if (inherits(object, "prcomp")) object$rotation else object$loadings
if (!(inherits(L, "loadings") || inherits(L, "list")))
class(L) <- "loadings"
attr(L, "explvar") <- explvar(object)
L
}
## scores: Return the scores (also works for prcomp/princomp objects):
scores <- function(object, ...) UseMethod("scores")
scores.default <- function(object, ...) {
S <- if (inherits(object, "prcomp")) object$x else object$scores
if (!(inherits(S, "scores") || inherits(S, "list")))
class(S) <- "scores"
attr(S, "explvar") <- explvar(object)
S
}
## Yscores: Return the Yscores
Yscores <- function(object) object$Yscores
## loading.weights: Return the loading weights:
loading.weights <- function(object) object$loading.weights
## Yloadings: Return the Yloadings
Yloadings <- function(object) object$Yloadings
## model.frame.mvr: Extract or generate the model frame from a `mvr' object.
## It is simply a slightly modified `model.frame.lm'.
model.frame.mvr <- function(formula, ...) {
dots <- list(...)
nargs <- dots[match(c("data", "na.action", "subset"), names(dots), 0)]
if (length(nargs) || is.null(formula$model)) {
fcall <- formula$call
fcall$method <- "model.frame"
fcall[[1]] <- as.name("mvr")
fcall[names(nargs)] <- nargs
env <- environment(formula$terms)
if (is.null(env)) env <- parent.frame()
eval(fcall, env, parent.frame())
}
else formula$model
}
## model.matrix.mvr: Extract the model matrix from an `mvr' object.
## It is a modified version of model.matrix.lm.
model.matrix.mvr <- function(object, ...) {
if (n_match <- match("x", names(object), 0))
object[[n_match]]
else {
data <- model.frame(object, ...)
mm <- NextMethod("model.matrix", data = data)
mm <- delete.intercept(mm) # Deletes any intercept coloumn
## model.matrix.default prepends the term name to the colnames of
## matrices. If there is only one predictor term, and the
## corresponding matrix has colnames, remove the prepended term name:
mt <- terms(object)
if (length(attr(mt, "term.labels")) == 1 &&
!is.null(colnames(data[[attr(mt, "term.labels")]])))
colnames(mm) <- sub(attr(mt, "term.labels"), "", colnames(mm))
return(mm)
}
}
## delete.intercept: utilitiy function that deletes the response coloumn from
## a model matrix, and adjusts the "assign" attribute:
delete.intercept <- function(mm) {
## Save the attributes prior to removing the intercept coloumn:
saveattr <- attributes(mm)
## Find the intercept coloumn:
intercept <- which(saveattr$assign == 0)
## Return if there was no intercept coloumn:
if (!length(intercept)) return(mm)
## Remove the intercept coloumn:
mm <- mm[,-intercept, drop=FALSE]
## Update the attributes with the new dimensions:
saveattr$dim <- dim(mm)
saveattr$dimnames <- dimnames(mm)
## Remove the assignment of the intercept from the attributes:
saveattr$assign <- saveattr$assign[-intercept]
## Restore the (modified) attributes:
attributes(mm) <- saveattr
## Return the model matrix:
mm
}
## The following "extraction" functions are mostly used in plot and summary
## functions.
## The names of the response variables:
respnames <- function(object)
dimnames(fitted(object))[[2]]
## The names of the prediction variables:
prednames <- function(object, intercept = FALSE) {
if (isTRUE(intercept))
c("(Intercept)", rownames(object$loadings))
else
rownames(object$loadings)
}
## The names of the components:
## Note: The components must be selected prior to the format statement
compnames <- function(object, comps, explvar = FALSE, ...) {
M <- if (is.matrix(object)) object else scores(object)
labs <- colnames(M)
if (missing(comps))
comps <- seq(along = labs)
else
labs <- labs[comps]
if (isTRUE(explvar) && !is.null(evar <- explvar(M)[comps]))
labs <- paste(labs, " (", format(evar, digits = 2, trim = TRUE),
" %)", sep = "")
return(labs)
}
## The explained X variance:
explvar <- function(object)
switch(class(object)[1],
mvr = 100 * object$Xvar / object$Xtotvar,
princomp =,
prcomp = 100 * object$sdev^2 / sum(object$sdev^2),
scores =,
loadings = attr(object, "explvar")
)
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