#' @rdname FLXMCL
# @aliases FLXMCLmajority-class
#'
#' @family mixtures majority
#'
#' @import flexmix
#' @export
setClass("FLXMCLmajority", contains = "FLXMCL")
#' This is a model driver for \code{\link[flexmix]{flexmix}} from package \pkg{flexmix} implementing mixtures of majority classifiers.
#'
#' @title Mixtures of Majority Classifiers
#'
#' @param formula A formula which is interpreted relative to the formula specified in the call to \code{\link[flexmix]{flexmix}} using \code{\link[stats]{update.formula}}.
#' Only the left-hand side (response) of the formula is used. Default is to use the original \code{\link[flexmix]{flexmix}} model formula.
#' @param \dots Further arguments to and from other methods.
#'
#' @return Returns an object of class \code{FLXMCLmajority} inheriting from \code{FLXMCL}.
#'
#' @rdname FLXMCLmajority
# @aliases FLXMCLmajority
#'
#' @family mixtures majority
#'
#' @import flexmix
#' @export
#'
#' @examples
#' library(benchData)
#' data <- flashData(1000)
#' data$x <- scale(data$x)
#' grid <- expand.grid(x.1=seq(-6,6,0.2), x.2=seq(-4,4,0.2))
#'
#' cluster <- kmeans(data$x, center = 4)$cluster
#' model <- FLXMCLmajority()
#' fit <- flexmix(y ~ ., data = as.data.frame(data), concomitant = FLXPmultinom(~ x.1 + x.2), model = model, cluster = cluster)
#'
#' ## prediction with aggregation depending on membership in mixture components
#' pred.grid <- mypredict(fit, newdata = grid, aggregate = TRUE)
#' image(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]][,1], length(seq(-6,6,0.2))))
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]][,1], length(seq(-6,6,0.2))), add = TRUE)
#' points(data$x, pch = as.character(data$y))
#'
#' ## local membership
#' loc.grid <- prior(fit, newdata = grid)
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(loc.grid[,1], length(seq(-6,6,0.2))), add = TRUE)
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(loc.grid[,2], length(seq(-6,6,0.2))), add = TRUE)
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(loc.grid[,3], length(seq(-6,6,0.2))), add = TRUE)
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(loc.grid[,4], length(seq(-6,6,0.2))), add = TRUE)
FLXMCLmajority <- function(formula = . ~ ., ...) {
z <- new("FLXMCLmajority", weighted = TRUE, formula = formula,
name = "Mixture of Majority Classifiers")
z@defineComponent <- expression({
predict <- function(x) {
pred <- getS3method("predict", "majority")(fit, newdata = x, ...)
lev <- levels(pred$class)
ng <- length(lev)
if (ng > ncol(pred$posterior)) {
posterior <- matrix(0, nrow(pred$posterior), ng)
rownames(posterior) <- rownames(pred$posterior)
colnames(posterior) <- lev
posterior[,colnames(pred$posterior)] <- pred$posterior
return(posterior)
} else
return(pred$posterior)
}
logLik <- function(x, y) {
post <- getS3method("predict", "majority")(fit, newdata = x, ...)$posterior
ng <- length(attr(y, "lev"))
# print(head(post))
# print(head(y))
if (ng > ncol(post)) {
ll <- rep(0, nrow(post))
col.index <- match(y, colnames(post), 0)
row.index <- which(col.index > 0)
ll[row.index] <- post[cbind(row.index, col.index[row.index])]
} else {
ll <- post[cbind(rownames(post), as.character(y))]
}
ll <- ifelse(ll == 0, -10000, log(ll))
# print(head(ll))
return(ll)
}
new("FLXcomponent", parameters = list(prior = fit$prior),
logLik = logLik, predict = predict, df = fit$df)
})
z@preproc.y <- function(grouping) {
if (!is.factor(grouping))
warning("'grouping' was coerced to a factor")
g <- as.factor(grouping)
lev <- levels(g)
g <- as.matrix(g)
attr(g, "lev") <- lev
g
}
z@fit <- function(x, y, w) {
lev <- attr(y, "lev")
fit <- majority(x, factor(y, levels = lev), weights = w)
fit$df <- length(lev) - 1
with(fit, eval(z@defineComponent))
}
z
}
#' @rdname FLXMCLmajority
# @aliases FLXgetModelmatrix,FLXMCLmajority-method
#'
#' @family mixtures majority
#'
#' @import flexmix
#' @export
setMethod("FLXgetModelmatrix", signature(model = "FLXMCLmajority"),
function (model, data, formula, lhs = TRUE, ...) {
formula <- flexmix:::RemoveGrouping(formula)
if (length(grep("\\|", deparse(model@formula))))
stop("no grouping variable allowed in the model")
if (is.null(model@formula))
model@formula = formula
model@fullformula = update(terms(formula, data = data),
model@formula)
if (lhs) {
mf <- if (is.null(model@terms))
model.frame(model@fullformula, data = data, na.action = NULL)
else model.frame(model@terms, data = data, na.action = NULL)
model@terms <- attr(mf, "terms")
modely <- model.response(mf)
model@y <- model@preproc.y(modely)
}
else {
mt1 <- if (is.null(model@terms))
terms(model@fullformula, data = data)
else model@terms
mf <- model.frame(delete.response(mt1), data = data,
na.action = NULL)
model@terms <- attr(mf, "terms")
}
attr(model@terms, "intercept") <- 0 ## intercept removed
X <- model.matrix(model@terms, data = mf)
model@contrasts <- attr(X, "contrasts")
model@x <- X
model@x <- model@preproc.x(model@x)
model@xlevels <- .getXlevels(model@terms, mf)
model
})
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