https://github.com/cran/caret
Tip revision: 282a9c980c81d17a8a23daa5e816fd04fe768a60 authored by Max Kuhn on 02 September 2011, 18:25:30 UTC
version 5.01-001
version 5.01-001
Tip revision: 282a9c9
knnreg.R
"knnreg" <- function(x, ...) UseMethod("knnreg")
knnreg.default <- function(x, ...)
{
if(!any(class(x) %in% "formula")) stop("knnreg only implemented for formula objects")
}
knnreg.formula <- function (formula, data, subset, na.action, k = 5, ...)
{
cl <- match.call()
if (missing(formula) ||
(length(formula) != 3) ||
(length(attr(terms(formula[-2], data = data), "term.labels")) < 1) ||
(length(attr(terms(formula[-3], data = data), "term.labels")) != 1))
stop("formula missing or incorrect")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m[[1]] <- as.name("model.frame")
m$... <- NULL
m$k <- NULL
m <- eval(m, parent.frame())
Terms <- attr(m, "terms")
y <- model.extract(m, "response")
x <- model.matrix(Terms, m)
xvars <- as.character(attr(Terms, "variables"))[-1]
if ((yvar <- attr(Terms, "response")) > 0)
xvars <- xvars[-yvar]
xlev <- if (length(xvars) > 0) {
xlev <- lapply(m[xvars], levels)
xlev[!sapply(xlev, is.null)]
}
xint <- match("(Intercept)", colnames(x), nomatch = 0)
if (xint > 0)
x <- x[, -xint, drop = FALSE]
RET <- list(learn = list(y = y, X = x))
RET$k <- k
RET$terms <- Terms
RET$call <- match.call(expand.dots = TRUE)
RET$contrasts <- attr(x, "contrasts")
RET$xlevels <- xlev
RET$theDots <- list(...)
attr(RET, "na.message") <- attr(m, "na.message")
if (!is.null(attr(m, "na.action")))
RET$na.action <- attr(m, "na.action")
class(RET) <- "knn3"
RET
}
knnreg.matrix <- function(x, y, k = 5, ...)
{
if(!is.matrix(x)) x <- as.matrix(x)
if(!is.numeric(y)) stop("y must be numeric")
RET <- list(learn = list(y = y, X = x))
RET$k <- k
RET$terms <- NULL
RET$call <- match.call(expand.dots = TRUE)
RET$contrasts <- NULL
RET$theDots <- list(...)
class(RET) <- "knnreg"
RET
}
knnreg.data.frame <- function(x, y, k = 5, ...)
{
x <- as.data.frame(x)
if(!is.numeric(y)) stop("y must be numeric")
RET <- list(learn = list(y = y, X = x))
RET$k <- k
RET$terms <- NULL
RET$call <- match.call(expand.dots = TRUE)
RET$contrasts <- NULL
RET$theDots <- list(...)
class(RET) <- "knnreg"
RET
}
print.knnreg <- function (x, ...)
{
cat(x$k, "-nearest neighbor regression model\n", sep = "")
cat("\nCall:\n", deparse(x$call), "\n\n", sep = "")
invisible(x)
}
predict.knnreg <- function (object, newdata, ...)
{
if (!inherits(object, "knnreg"))
stop("object not of class knnreg")
if (!is.null(Terms <- object$terms)) {
if (missing(newdata))
newdata <- model.frame(object)
else {
newdata <- model.frame(as.formula(delete.response(Terms)),
newdata)
}
x <- model.matrix(delete.response(Terms), newdata, contrasts = object$contrasts)
xint <- match("(Intercept)", colnames(x), nomatch = 0)
if (xint > 0)
x <- x[, -xint, drop = FALSE]
}
else {
x <- as.matrix(newdata)
}
argList <- list(train = object$learn$X,
test = x,
y = object$learn$y,
k = object$k)
RET <- do.call("knnregTrain", argList)
RET
}
knnregTrain <- function(train, test, y, k = 5, use.all=TRUE)
{
train <- as.matrix(train)
if(is.null(dim(test))) dim(test) <- c(1, length(test))
test <- as.matrix(test)
if(any(is.na(train)) || any(is.na(test)) || any(is.na(y)))
stop("no missing values are allowed")
p <- ncol(train)
ntr <- nrow(train)
if(length(y) != ntr) stop("'train' and 'class' have different lengths")
if(ntr < k) {
warning(gettextf("k = %d exceeds number %d of patterns", k, ntr),
domain = NA)
k <- ntr
}
if (k < 1)
stop(gettextf("k = %d must be at least 1", k), domain = NA)
nte <- nrow(test)
if(ncol(test) != p) stop("dims of 'test' and 'train differ")
Z <- .C("knn3reg",
as.integer(k),
as.integer(ntr),
as.integer(nte),
as.integer(p),
as.double(train),
as.double(y),
as.double(test),
double(nte),
as.integer(FALSE),
as.integer(use.all))
Z[[8]]
}