model4you (version 0.9-5)

pmtree: Compute model-based tree from model.

Description

Input a parametric model and get a model-based tree.

Usage

pmtree(model, data = NULL, zformula = ~., control = ctree_control(),
  coeffun = coef, ...)

Arguments

model

a model object. The model can be a parametric model with a binary covariate.

data

data. If NULL (default) the data from the model object are used.

zformula

formula describing which variable should be used for partitioning. Default is to use all variables in data that are not in the model (i.e. ~ .).

control

control parameters, see ctree_control.

coeffun

function that takes the model object and returns the coefficients. Useful when coef() does not return all coefficients (e.g. survreg).

...

additional parameters passed on to model fit such as weights.

Value

ctree object

Details

Sometimes the number of participant in each treatment group needs to be of a certain size. This can be accomplished by setting control$converged. See example below.

Examples

# NOT RUN {
if(require("TH.data") & require("survival")) {
  ## base model
  bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)
  survreg_plot(bmod)
  
  ## partitioned model
  tr <- pmtree(bmod)
  plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot, 
                                            confint = TRUE))
  summary(tr)
  summary(tr, node = 1:2)
  
  logLik(bmod)
  logLik(tr)
  
  
  ## Sometimes the number of participant in each treatment group needs to 
  ## be of a certain size. This can be accomplished using converged
  
  ## Each treatment group should have more than 33 observations
  ctrl <- ctree_control(lookahead = TRUE)
  ctrl$converged <- function(mod, data, subset) {
      all(table(data$horTh[subset]) > 33)
  }
  
  tr2 <- pmtree(bmod, control = ctrl)
  plot(tr2, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
      confint = TRUE))
  
  summary(tr2[[5]]$data$horTh)
}


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")))
}
# }