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## simple wrapper function to specify fitter and return class
lmtree <- function(formula, data, subset, na.action, weights, offset, cluster, ...)
{
## TODO: variance as model parameter
## use dots for setting up mob_control
control <- mob_control(...)
if(control$vcov != "opg") {
warning('only vcov = "opg" supported in lmtree')
control$vcov <- "opg"
}
if(!is.null(control$prune)) {
if(is.character(control$prune)) {
control$prune <- tolower(control$prune)
control$prune <- match.arg(control$prune, c("aic", "bic", "none"))
control$prune <- switch(control$prune,
"aic" = {
function(objfun, df, nobs) (nobs[1L] * log(objfun[1L]) + 2 * df[1L]) < (nobs[1L] * log(objfun[2L]) + 2 * df[2L])
}, "bic" = {
function(objfun, df, nobs) (nobs[1L] * log(objfun[1L]) + log(nobs[2L]) * df[1L]) < (nobs[1L] * log(objfun[2L]) + log(nobs[2L]) * df[2L])
}, "none" = {
NULL
})
}
if(!is.function(control$prune)) {
warning("unknown specification of 'prune'")
control$prune <- NULL
}
}
## keep call
cl <- match.call(expand.dots = TRUE)
## extend formula if necessary
f <- Formula::Formula(formula)
if(length(f)[2L] == 1L) {
attr(f, "rhs") <- c(list(1), attr(f, "rhs"))
formula[[3L]] <- formula(f)[[3L]]
} else {
f <- NULL
}
## call mob
m <- match.call(expand.dots = FALSE)
if(!is.null(f)) m$formula <- formula
m$fit <- lmfit
m$control <- control
if("..." %in% names(m)) m[["..."]] <- NULL
m[[1L]] <- as.call(quote(partykit::mob))
rval <- eval(m, parent.frame())
## extend class and keep original call
rval$info$call <- cl
class(rval) <- c("lmtree", class(rval))
return(rval)
}
## actual fitting function for mob()
lmfit <- function(y, x, start = NULL, weights = NULL, offset = NULL, cluster = NULL, ...,
estfun = FALSE, object = FALSE)
{
## add intercept-only regressor matrix (if missing)
## NOTE: does not have terms/formula
if(is.null(x)) x <- matrix(1, nrow = NROW(y), ncol = 1L,
dimnames = list(NULL, "(Intercept)"))
## call lm fitting function
if(is.null(weights) || identical(as.numeric(weights), rep.int(1, length(weights)))) {
z <- lm.fit(x, y, offset = offset, ...)
weights <- 1
} else {
z <- lm.wfit(x, y, w = weights, offset = offset, ...)
}
## list structure
rval <- list(
coefficients = z$coefficients,
objfun = sum(weights * z$residuals^2),
estfun = NULL,
object = NULL
)
## add estimating functions (if desired)
if(estfun) {
rval$estfun <- as.vector(z$residuals) * weights * x[, !is.na(z$coefficients), drop = FALSE]
}
## add model (if desired)
if(object) {
class(z) <- c(if(is.matrix(z$fitted)) "mlm", "lm")
z$offset <- if(is.null(offset)) 0 else offset
z$contrasts <- attr(x, "contrasts")
z$xlevels <- attr(x, "xlevels")
cl <- as.call(expression(lm))
cl$formula <- attr(x, "formula")
if(!is.null(offset)) cl$offset <- attr(x, "offset")
z$call <- cl
z$terms <- attr(x, "terms")
rval$object <- z
}
return(rval)
}
## methods
print.lmtree <- function(x,
title = "Linear model tree", objfun = "residual sum of squares", ...)
{
print.modelparty(x, title = title, objfun = objfun, ...)
}
predict.lmtree <- function(object, newdata = NULL, type = "response", ...)
{
## FIXME: possible to get default?
if(is.null(newdata) & !identical(type, "node")) stop("newdata has to be provided")
predict.modelparty(object, newdata = newdata, type = type, ...)
}
plot.lmtree <- function(x, terminal_panel = node_bivplot,
tp_args = list(), tnex = NULL, drop_terminal = NULL, ...)
{
nreg <- if(is.null(tp_args$which)) x$info$nreg else length(tp_args$which)
if(nreg < 1L & missing(terminal_panel)) {
plot.constparty(as.constparty(x),
tp_args = tp_args, tnex = tnex, drop_terminal = drop_terminal, ...)
} else {
if(is.null(tnex)) tnex <- if(is.null(terminal_panel)) 1L else 2L * nreg
if(is.null(drop_terminal)) drop_terminal <- !is.null(terminal_panel)
plot.modelparty(x, terminal_panel = terminal_panel,
tp_args = tp_args, tnex = tnex, drop_terminal = drop_terminal, ...)
}
}
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