pmforest {model4you} | R Documentation |
Input a parametric model and get a forest.
pmforest(model, data = NULL, zformula = ~., ntree = 500L, perturb = list(replace = FALSE, fraction = 0.632), mtry = NULL, applyfun = NULL, cores = NULL, control = ctree_control(teststat = "quad", testtype = "Univ", mincriterion = 0, saveinfo = FALSE, lookahead = TRUE, ...), trace = FALSE, ...) ## S3 method for class 'pmforest' gettree(object, tree = 1L, saveinfo = TRUE, coeffun = coef, ...)
model |
a model object. The model can be a parametric model with a single binary covariate. |
data |
data. If |
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. |
ntree |
number of trees. |
perturb |
a list with arguments replace and fraction determining which type of
resampling with |
mtry |
number of input variables randomly sampled as candidates at each
node (Default |
applyfun |
see |
cores |
see |
control |
control parameters, see |
trace |
a logical indicating if a progress bar shall be printed while the forest grows. |
... |
additional parameters passed on to model fit such as weights. |
object |
an object returned by pmforest. |
tree |
an integer, the number of the tree to extract from the forest. |
saveinfo |
logical. Should the model info be stored in terminal nodes? |
coeffun |
function that takes the model object and returns the coefficients. Useful when coef() does not return all coefficients (e.g. survreg). |
cforest object
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") } }