# Personalised model Compute personalised models from cforest object. ```r pmodel( x = NULL, model = NULL, newdata = NULL, OOB = TRUE, fun = coef, return_attr = c("modelcall", "data", "similarity") ) ``` ## Arguments - `x`: cforest object or matrix of weights. - `model`: model object. If NULL the model in `x$info$model` is used. - `newdata`: new data. If NULL cforest learning data is used. Ignored if `x` is a matrix. - `OOB`: In case of using the learning data, should patient similarities be computed out of bag? - `fun`: function to apply on the personalised model before returning. The default `coef` returns a matrix of personalised coefficients. For returning the model objects use `identity`. - `return_attr`: which attributes to add to the object returned. If it contains `"modelcall"` the call of the base model is returned, if it contains `"data"` the data, and if it contains `"similarity"` the matrix of similarity weights is added. ## Returns depends on fun. ## Examples ```r library("model4you") if(require("mvtnorm") & require("survival")) { ## function to simulate the data sim_data <- function(n = 500, p = 10, beta = 3, sd = 1){ ## treatment lev <- c("C", "A") a <- rep(factor(lev, labels = lev, levels = lev), length = n) ## correlated z variables sigma <- diag(p) sigma[sigma == 0] <- 0.2 ztemp <- rmvnorm(n, sigma = sigma) z <- (pnorm(ztemp) * 2 * pi) - pi colnames(z) <- paste0("z", 1:ncol(z)) z1 <- z[,1] ## outcome y <- 7 + 0.2 * (a %in% "A") + beta * cos(z1) * (a %in% "A") + rnorm(n, 0, sd) data.frame(y = y, a = a, z) } ## simulate data set.seed(123) beta <- 3 ntrain <- 500 ntest <- 50 simdata <- simdata_s <- sim_data(p = 5, beta = beta, n = ntrain) tsimdata <- tsimdata_s <- sim_data(p = 5, beta = beta, n = ntest) simdata_s$cens <- rep(1, ntrain) tsimdata_s$cens <- rep(1, ntest) ## base model basemodel_lm <- lm(y ~ a, data = simdata) ## forest frst_lm <- pmforest(basemodel_lm, ntree = 20, perturb = list(replace = FALSE, fraction = 0.632), control = ctree_control(mincriterion = 0)) ## personalised models # (1) return the model objects pmodels_lm <- pmodel(x = frst_lm, newdata = tsimdata, fun = identity) class(pmodels_lm) # (2) return coefficients only (default) coefs_lm <- pmodel(x = frst_lm, newdata = tsimdata) # compare predictive objective functions of personalised models versus # base model sum(objfun(pmodels_lm)) # -RSS personalised models sum(objfun(basemodel_lm, newdata = tsimdata)) # -RSS base model if(require("ggplot2")) { ## dependence plot dp_lm <- cbind(coefs_lm, tsimdata) ggplot(tsimdata) + stat_function(fun = function(z1) 0.2 + beta * cos(z1), aes(color = "true treatment\neffect")) + geom_point(data = dp_lm, aes(y = aA, x = z1, color = "estimates lm"), alpha = 0.5) + ylab("treatment effect") + xlab("patient characteristic z1") } } ```

pmodel function