predict.pmtree: pmtree predictions

In model4you: Stratified and Personalised Models Based on Model-Based Trees and Forests

pmtree predictions

Description

Compute predictions from pmtree object.

Usage

## S3 method for class 'pmtree'
predict(
  object,
  newdata = NULL,
  type = "node",
  predict_args = list(),
  perm = NULL,
  ...
)

Arguments

object	pmtree object.
newdata	an optional data frame in which to look for variables with which to predict, if omitted, object$data is used.
type	character denoting the type of predicted value. The terminal node is returned for "node". If type = "pass" the model predict method is used and arguments can be passed to it via predict_args. If type = "coef" the the model coefficients are returned.
predict_args	If type = "pass" arguments can be passed on to the model predict function.
perm	an optional character vector of variable names (or integer vector of variable location in newdata). Splits of nodes with a primary split in any of these variables will be permuted (after dealing with surrogates). Note that surrogate split in the perm variables will no be permuted.
...	passed on to predict.party (e.g. perm).

Value

predictions

Examples

if(require("psychotools")) {
  data("MathExam14W", package = "psychotools")
  
  ## scale points achieved to [0, 100] percent
  MathExam14W$tests <- 100 * MathExam14W$tests/26
  MathExam14W$pcorrect <- 100 * MathExam14W$nsolved/13
  
  ## select variables to be used
  MathExam <- MathExam14W[ , c("pcorrect", "group", "tests", "study",
                               "attempt", "semester", "gender")]
  
  ## compute base model
  bmod_math <- lm(pcorrect ~ group, data = MathExam)
  lm_plot(bmod_math, densest = TRUE)
  
  ## compute tree
  (tr_math <- pmtree(bmod_math, control = ctree_control(maxdepth = 2)))
  plot(tr_math, terminal_panel = node_pmterminal(tr_math, plotfun = lm_plot, 
                                                 confint = FALSE))
  plot(tr_math, terminal_panel = node_pmterminal(tr_math, plotfun = lm_plot, 
                                                 densest = TRUE,
                                                 confint = TRUE))
  
  ## predict
  newdat <- MathExam[1:5, ]
  
  # terminal nodes
  (nodes <- predict(tr_math, type = "node", newdata = newdat))
  
  # response
  (pr <- predict(tr_math, type = "pass", newdata = newdat))
  
  # response including confidence intervals, see ?predict.lm
  (pr1 <- predict(tr_math, type = "pass", newdata = newdat,
                  predict_args = list(interval = "confidence")))
}

model4you documentation built on April 3, 2025, 5:41 p.m.