# Compute model-based tree from model. Input a parametric model and get a model-based tree. ```r 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. ## Returns 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$converged`. See example below. ## Examples ```r if(require("TH.data") & require("survival")) { ## base model bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE) survreg_plot(bmod) ## partitioned model tr <- pmtree(bmod) plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot, confint = TRUE)) summary(tr) summary(tr, node = 1:2) logLik(bmod) logLik(tr) ## Sometimes the number of participant in each treatment group needs to ## be of a certain size. This can be accomplished using converged ## Each treatment group should have more than 33 observations ctrl <- ctree_control(lookahead = TRUE) ctrl$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"))) } ```

pmtree function