# pmforest

##### Compute model-based forest from model.

Input a parametric model and get a forest.

##### Usage

```
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 pmforest
gettree(object, tree = 1L, saveinfo = TRUE,
coeffun = coef, ...)

##### Arguments

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

- data
data. If

`NULL`

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.

`~ .`

).- ntree
number of trees.

- perturb
a list with arguments replace and fraction determining which type of resampling with

`replace = TRUE`

referring to the n-out-of-n bootstrap and`replace = FALSE`

to sample splitting. fraction is the number of observations to draw without replacement.- mtry
number of input variables randomly sampled as candidates at each node (Default

`NULL`

corresponds to`ceiling(sqrt(nvar))`

). Bagging, as special case of a random forest without random input variable sampling, can be performed by setting mtry either equal to Inf or equal to the number of input variables.- applyfun
see

`cforest`

.- cores
see

`cforest`

.- control
control parameters, see

`ctree_control`

.- 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).

##### Value

cforest object

##### See Also

##### Examples

```
# NOT RUN {
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")
}
}
# }
```

*Documentation reproduced from package model4you, version 0.9-5, License: GPL-2 | GPL-3*