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#' @export
makeRLearner.regr.lm = function() {
makeRLearnerRegr(
cl = "regr.lm",
package = "stats",
par.set = makeParamSet(
makeNumericLearnerParam(id = "tol", default = 1e-7, lower = 0),
makeLogicalLearnerParam(id = "singular.ok", default = TRUE, tunable = FALSE)
),
properties = c("numerics", "factors", "se", "weights"),
name = "Simple Linear Regression",
short.name = "lm",
callees = c("lm", "lm.fit", "lm.wfit")
)
}
#' @export
trainLearner.regr.lm = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset)
if (is.null(.weights)) {
f = getTaskFormula(.task)
stats::lm(f, data = d, ...)
} else {
f = getTaskFormula(.task)
stats::lm(f, data = d, weights = .weights, ...)
}
}
#' @export
predictLearner.regr.lm = function(.learner, .model, .newdata, ...) {
if (.learner$predict.type == "response") {
predict(.model$learner.model, newdata = .newdata, se.fit = FALSE, ...)
} else {
p = predict(.model$learner.model, newdata = .newdata, se.fit = TRUE, ...)
cbind(p$fit, p$se.fit)
}
}
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