Package 'model4you' reference manual

Title:	Stratified and Personalised Models Based on Model-Based Trees and Forests
Description:	Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.
Authors:	Heidi Seibold [aut, cre], Achim Zeileis [aut], Torsten Hothorn [aut]
Maintainer:	Heidi Seibold <[email protected]>
License:	GPL-2 \| GPL-3
Version:	0.9-7
Built:	2024-09-19 13:27:09 UTC
Source:	https://github.com/r-forge/partykit

Add model information to a personalised-model-ctree

Description

For internal use.

Usage

.add_modelinfo(x, nodeids, data, model, coeffun)
.add_modelinfo(x, nodeids, data, model, coeffun)

Arguments

`x`	constparty object.
`nodeids`	node ids, usually the terminal ids.
`data`	data.
`model`	model.
`coeffun`	function that takes the model object and returns the coefficients. Useful when coef() does not return all coefficients (e.g. survreg).

Value

tree with added info. Class still to be added.

Fit function when model object is given

Description

Use update function to refit model and extract info such as coef, logLik and estfun.

Usage

.modelfit(model, data, coeffun = coef, weights, control, parm = NULL)
.modelfit(model, data, coeffun = coef, weights, control, parm = NULL)

Arguments

`model`	model object.
`data`	data.
`coeffun`	function that takes the model object and returns the coefficients. Useful when coef() does not return all coefficients (e.g. survreg).
`weights`	weights.
`control`	control options from `ctree_control`.
`parm`	which parameters should be used for instability test?

Value

A function returning a list of

`coefficients`	`coef`.
`objfun`	`logLik`.
`object`	the model object.
`converged`	Did the model converge?
`estfun`	`estfun`.

Prepare input for ctree/cforest from input of pmtree/pmforest

Description

Prepare input for ctree/cforest from input of pmtree/pmforest

Usage

.prepare_args(model, data, zformula, control, ...)
.prepare_args(model, data, zformula, control, ...)

Arguments

`model`	model.
`data`	an optional data frame.
`zformula`	ormula describing which variable should be used for partitioning.
`control`	ontrol parameters, see `ctree_control`.
`...`	other arguments.

Value

args to be passed to ctree/cforest.

Plot for a given logistic regression model (glm with binomial family) with one binary covariate.

Description

Can be used on its own but is also useable as plotfun in node_pmterminal.

Usage

binomial_glm_plot(
  mod,
  data = NULL,
  plot_data = FALSE,
  theme = theme_classic(),
  ...
)
binomial_glm_plot(
  mod,
  data = NULL,
  plot_data = FALSE,
  theme = theme_classic(),
  ...
)

Arguments

`mod`	A model of class glm with binomial family.
`data`	optional data frame. If NULL the data stored in mod is used.
`plot_data`	should the data in form of a mosaic type plot be plotted?
`theme`	A ggplot2 theme.
`...`	ignored at the moment.

Examples

set.seed(2017)

# number of observations
n <- 1000

# balanced binary treatment
# trt <- factor(rep(c("C", "A"), each = n/2),
#               levels = c("C", "A"))

# unbalanced binary treatment
trt <- factor(c(rep("C", n/4), rep("A", 3*n/4)),
              levels = c("C", "A"))

# some continuous variables
x1 <- rnorm(n)
x2 <- rnorm(n)

# linear predictor
lp <- -0.5 + 0.5*I(trt == "A") + 1*I(trt == "A")*I(x1 > 0)

# compute probability with inverse logit function
invlogit <- function(x) 1/(1 + exp(-x))
pr <- invlogit(lp)

# bernoulli response variable
y <- rbinom(n, 1, pr)
dat <- data.frame(y, trt, x1, x2)

# logistic regression model
mod <- glm(y ~ trt, data = dat, family = "binomial")
binomial_glm_plot(mod, plot_data = TRUE)

# logistic regression model tree
ltr <- pmtree(mod)
plot(ltr, terminal_panel = node_pmterminal(ltr,
                                           plotfun = binomial_glm_plot,
                                           confint = TRUE,
                                           plot_data = TRUE))

set.seed(2017)

# number of observations
n <- 1000

# balanced binary treatment
# trt <- factor(rep(c("C", "A"), each = n/2),
#               levels = c("C", "A"))

# unbalanced binary treatment
trt <- factor(c(rep("C", n/4), rep("A", 3*n/4)),
              levels = c("C", "A"))

# some continuous variables
x1 <- rnorm(n)
x2 <- rnorm(n)

# linear predictor
lp <- -0.5 + 0.5*I(trt == "A") + 1*I(trt == "A")*I(x1 > 0)

# compute probability with inverse logit function
invlogit <- function(x) 1/(1 + exp(-x))
pr <- invlogit(lp)

# bernoulli response variable
y <- rbinom(n, 1, pr)
dat <- data.frame(y, trt, x1, x2)

# logistic regression model
mod <- glm(y ~ trt, data = dat, family = "binomial")
binomial_glm_plot(mod, plot_data = TRUE)

# logistic regression model tree
ltr <- pmtree(mod)
plot(ltr, terminal_panel = node_pmterminal(ltr,
                                           plotfun = binomial_glm_plot,
                                           confint = TRUE,
                                           plot_data = TRUE))

Table of coefficients for survreg model

Description

This function is mostly useful for plotting a pmtree. The generic plotting does not show the estimate and confidence interval of the scale parameter. This one does.

Usage

coeftable.survreg(model, confint = TRUE, digits = 2, intree = FALSE)
coeftable.survreg(model, confint = TRUE, digits = 2, intree = FALSE)

Arguments

`model`	model of class `survreg`
`confint`	should a confidence interval be computed? Default: TRUE
`digits`	integer, used for formating numbers. Default: 2
`intree`	is the table plotted within a tree? Default: FALSE

Value

None.

Examples

if(require("survival") & require("TH.data")) {
  ## Load data
  data(GBSG2, package = "TH.data")

  ## Weibull model
  bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)

  ## Coefficient table
  grid.newpage()
  coeftable.survreg(bmod)

  ## partitioned model
  tr <- pmtree(bmod)

  ## plot
  plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
    confint = TRUE, coeftable = coeftable.survreg))
}

if(require("survival") & require("TH.data")) {
  ## Load data
  data(GBSG2, package = "TH.data")

  ## Weibull model
  bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)

  ## Coefficient table
  grid.newpage()
  coeftable.survreg(bmod)

  ## partitioned model
  tr <- pmtree(bmod)

  ## plot
  plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
    confint = TRUE, coeftable = coeftable.survreg))
}

Survival plot for a given coxph model with one binary covariate.

Description

Can be used on its own but is also useable as plotfun in node_pmterminal.

Usage

coxph_plot(mod, data = NULL, theme = theme_classic(), yrange = NULL)
coxph_plot(mod, data = NULL, theme = theme_classic(), yrange = NULL)

Arguments

`mod`	A model of class coxph.
`data`	optional data frame. If NULL the data stored in mod is used.
`theme`	A ggplot2 theme.
`yrange`	Range of the y variable to be used for plotting. If NULL it will be 0 to max(y).

Examples

if(require("survival")) {
  coxph_plot(coxph(Surv(futime, fustat) ~ factor(rx), ovarian))
}

if(require("survival")) {
  coxph_plot(coxph(Surv(futime, fustat) ~ factor(rx), ovarian))
}

Density plot for a given lm model with one binary covariate.

Description

Can be used on its own but is also useable as plotfun in node_pmterminal.

Usage

lm_plot(
  mod,
  data = NULL,
  densest = FALSE,
  theme = theme_classic(),
  yrange = NULL
)
lm_plot(
  mod,
  data = NULL,
  densest = FALSE,
  theme = theme_classic(),
  yrange = NULL
)

Arguments

`mod`	A model of class lm.
`data`	optional data frame. If NULL the data stored in mod is used.
`densest`	should additional to the model density kernel density estimates (see `geom_density`) be computed?
`theme`	A ggplot2 theme.
`yrange`	Range of the y variable to be used for plotting. If NULL the range in the data will be used.

Details

In case of an offset, the value of the offset variable will be set to the median of the values in the data.

Examples

## example taken from ?lm
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
data <- data.frame(weight, group)
lm.D9 <- lm(weight ~ group, data = data)
lm_plot(lm.D9)

## example taken from ?glm (modified version)
data(anorexia, package = "MASS")
anorexia$treatment <- factor(anorexia$Treat != "Cont")
anorex.1 <- glm(Postwt ~ treatment + offset(Prewt),
                family = gaussian, data = anorexia)
lm_plot(anorex.1)

## example taken from ?lm
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
data <- data.frame(weight, group)
lm.D9 <- lm(weight ~ group, data = data)
lm_plot(lm.D9)

## example taken from ?glm (modified version)
data(anorexia, package = "MASS")
anorexia$treatment <- factor(anorexia$Treat != "Cont")
anorex.1 <- glm(Postwt ~ treatment + offset(Prewt),
                family = gaussian, data = anorexia)
lm_plot(anorex.1)

Extract log-Likelihood

Description

Extract sum of log-Likelihood contributions of all terminal nodes. By default the degrees of freedom from the models are used but optionally degrees of freedom for splits can be incorporated.

Usage

## S3 method for class 'pmtree'
logLik(object, dfsplit = 0, newdata = NULL, weights = NULL, perm = NULL, ...)
## S3 method for class 'pmtree'
logLik(object, dfsplit = 0, newdata = NULL, weights = NULL, perm = NULL, ...)

Arguments

`object`	pmtree object.
`dfsplit`	degrees of freedom per selected split.
`newdata`	an optional new data frame for which to compute the sum of objective functions.
`weights`	weights.
`perm`	the number of permutations performed (see `varimp`).
`...`	ignored.

Value

Returns an object of class logLik.

Panel-Generator for Visualization of pmtrees

Description

The plot method for party and constparty objects are rather flexible and can be extended by panel functions. The pre-defined panel-generating function of class grapcon_generator for pmtrees is documented here.

Usage

node_pmterminal(
  obj,
  coeftable = TRUE,
  digits = 2,
  confint = TRUE,
  plotfun,
  nid = function(node) paste0(nam[id_node(node)], ", n = ", node$info$nobs),
  ...
)
node_pmterminal(
  obj,
  coeftable = TRUE,
  digits = 2,
  confint = TRUE,
  plotfun,
  nid = function(node) paste0(nam[id_node(node)], ", n = ", node$info$nobs),
  ...
)

Arguments

`obj`	an object of class party.
`coeftable`	logical or function. If logical: should a table with coefficients be added to the plot (TRUE/FALSE)? If function: A function comparable to `coeftable.survreg`.
`digits`	integer, used for formating numbers.
`confint`	Should a confidence interval be computed.
`plotfun`	Plotting function to be used. Needs to be of format `function(mod, data)` where `mod` is the model object. See examples for more details.
`nid`	function to retrieve info on what is plottet as node ids.
`...`	arguments passed on to plotfun.

Examples

if(require("survival")) {
## compute survreg model
mod_surv <- survreg(Surv(futime, fustat) ~ factor(rx), ovarian,
  dist = 'weibull')
survreg_plot(mod_surv)

## partition model and plot
tr_surv <- pmtree(mod_surv)
plot(tr_surv, terminal_panel = node_pmterminal(tr_surv, plotfun = survreg_plot,
                                               confint = TRUE))
}

if(require("survival") & require("TH.data")) {
  ## Load data
  data(GBSG2, package = "TH.data")

  ## Weibull model
  bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)

  ## Coefficient table
  grid.newpage()
  coeftable.survreg(bmod)

  ## partitioned model
  tr <- pmtree(bmod)

  ## plot with specific coeftable
  plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
    confint = TRUE, coeftable = coeftable.survreg))
}

if(require("survival")) {
## compute survreg model
mod_surv <- survreg(Surv(futime, fustat) ~ factor(rx), ovarian,
  dist = 'weibull')
survreg_plot(mod_surv)

## partition model and plot
tr_surv <- pmtree(mod_surv)
plot(tr_surv, terminal_panel = node_pmterminal(tr_surv, plotfun = survreg_plot,
                                               confint = TRUE))
}

if(require("survival") & require("TH.data")) {
  ## Load data
  data(GBSG2, package = "TH.data")

  ## Weibull model
  bmod <- survreg(Surv(time, cens) ~ horTh, data = GBSG2, model = TRUE)

  ## Coefficient table
  grid.newpage()
  coeftable.survreg(bmod)

  ## partitioned model
  tr <- pmtree(bmod)

  ## plot with specific coeftable
  plot(tr, terminal_panel = node_pmterminal(tr, plotfun = survreg_plot,
    confint = TRUE, coeftable = coeftable.survreg))
}

Objective function

Description

Get the contributions of an objective function. For glm these are the (weighted) log-likelihood contributions, for lm the negative (weighted) squared error.

Usage

objfun(x, ...)

## S3 method for class 'survreg'
objfun(x, newdata = NULL, weights = NULL, ...)

## S3 method for class 'lm'
objfun(x, newdata = NULL, weights = NULL, ...)

## S3 method for class 'glm'
objfun(x, newdata = NULL, weights = NULL, log = TRUE, ...)
objfun(x, ...)

## S3 method for class 'survreg'
objfun(x, newdata = NULL, weights = NULL, ...)

## S3 method for class 'lm'
objfun(x, newdata = NULL, weights = NULL, ...)

## S3 method for class 'glm'
objfun(x, newdata = NULL, weights = NULL, log = TRUE, ...)

Arguments

`x`	model object.
`...`	further arguments passed on to `objfun` methods.
`newdata`	optional. New data frame. Can be useful for model evaluation / benchmarking.
`weights`	optional. Prior weights. See `glm` or `lm`.
`log`	should the log-Likelihood contributions or the Likelhood contributions be returned?

Value

vector of objective function contributions.

Examples

## Example taken from ?stats::glm
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12) 
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
logLik_contributions <- objfun(glm.D93)
sum(logLik_contributions)
logLik(glm.D93)

if(require("survival")) {
  x <- survreg(Surv(futime, fustat) ~ rx, ovarian, dist = "weibull")
  newdata <- ovarian[3:5, ]

  sum(objfun(x))
  x$loglik

  objfun(x, newdata = newdata)
}

## Example taken from ?stats::glm
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12) 
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
logLik_contributions <- objfun(glm.D93)
sum(logLik_contributions)
logLik(glm.D93)

if(require("survival")) {
  x <- survreg(Surv(futime, fustat) ~ rx, ovarian, dist = "weibull")
  newdata <- ovarian[3:5, ]

  sum(objfun(x))
  x$loglik

  objfun(x, newdata = newdata)
}

Objective function of personalised models

Description

Get the contributions of an objective function (e.g. likelihood contributions) and the sum thereof (e.g. log-Likelihood).

Usage

## S3 method for class 'pmodel_identity'
objfun(x, ...)

## S3 method for class 'pmodel_identity'
logLik(object, add_df = 0, ...)
## S3 method for class 'pmodel_identity'
objfun(x, ...)

## S3 method for class 'pmodel_identity'
logLik(object, add_df = 0, ...)

Arguments

x, object

object of class pmodel_identity (obtained by pmodel(..., fun = identity)).

...

additional parameters passed on to objfun.

add_df

it is not very clear what the degrees of freedom are in personalised models. With this argument you can add/substract degrees of freedom at your convenience. Default is 0 which means adding up the degrees of freedom of all individual models.

For examples see pmodel.

Objective function of a given pmtree

Description

Returns the contributions to the objective function or the sum thereof (if sum = TRUE).

Usage

## S3 method for class 'pmtree'
objfun(x, newdata = NULL, weights = NULL, perm = NULL, sum = FALSE, ...)
## S3 method for class 'pmtree'
objfun(x, newdata = NULL, weights = NULL, perm = NULL, sum = FALSE, ...)

Arguments

`x`	pmtree object.
`newdata`	an optional new data frame for which to compute the sum of objective functions.
`weights`	weights.
`perm`	the number of permutations performed (see `varimp`).
`sum`	should the sum of objective functions be computed.
`...`	passed on to `predict.party`. Note that `objfun.pmtree(x, sum = TRUE)` is much faster than `sum(objfun.pmtree(x))`.

Value

objective function or the sum thereof

Examples

## generate data
set.seed(2)
n <- 1000
trt <- factor(rep(1:2, each = n/2))
age <- sample(40:60, size = n, replace = TRUE)
eff <- -1 + I(trt == 2) + 1 * I(trt == 2) * I(age > 50)
expit <- function(x) 1/(1 + exp(-x))
success <- rbinom(n = n, size = 1, prob = expit(eff))
dat <- data.frame(success, trt, age)

## compute base model
bmod1 <- glm(success ~ trt, data = dat, family = binomial)

## copmute tree
(tr1 <- pmtree(bmod1, data = dat))

## compute log-Likelihood
logLik(tr1)
objfun(tr1, newdata = dat, sum = TRUE)
objfun(tr1, sum = TRUE)

## log-Likelihood contributions of first 
## 5 observations
nd <- dat[1:5, ]
objfun(tr1, newdata = nd)
## generate data
set.seed(2)
n <- 1000
trt <- factor(rep(1:2, each = n/2))
age <- sample(40:60, size = n, replace = TRUE)
eff <- -1 + I(trt == 2) + 1 * I(trt == 2) * I(age > 50)
expit <- function(x) 1/(1 + exp(-x))
success <- rbinom(n = n, size = 1, prob = expit(eff))
dat <- data.frame(success, trt, age)

## compute base model
bmod1 <- glm(success ~ trt, data = dat, family = binomial)

## copmute tree
(tr1 <- pmtree(bmod1, data = dat))

## compute log-Likelihood
logLik(tr1)
objfun(tr1, newdata = dat, sum = TRUE)
objfun(tr1, sum = TRUE)

## log-Likelihood contributions of first 
## 5 observations
nd <- dat[1:5, ]
objfun(tr1, newdata = nd)

Check if model has only one factor covariate.

Description

See https://stackoverflow.com/questions/50504386/check-that-model-has-only-one-factor-covariate/50514499#50514499

Usage

one_factor(object)
one_factor(object)

Arguments

object

model.

Value

Returns TRUE if model has a single factor covariate, FALSE otherwise.

Compute model-based forest from model.

Description

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

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

Examples

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")
  }
}

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")
  }
}

Personalised model

Description

Compute personalised models from cforest object.

Usage

pmodel(
  x = NULL,
  model = NULL,
  newdata = NULL,
  OOB = TRUE,
  fun = coef,
  return_attr = c("modelcall", "data", "similarity")
)
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.

Value

depends on fun.

Examples

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")
  }
}

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")
  }
}

Test if personalised models improve upon base model.

Description

This is a rudimentary test if there is heterogeneity in the model parameters. The null-hypothesis is: the base model is the correct model.

Usage

pmtest(forest, pmodels = NULL, data = NULL, B = 100)

## S3 method for class 'heterogeneity_test'
plot(x, ...)
pmtest(forest, pmodels = NULL, data = NULL, B = 100)

## S3 method for class 'heterogeneity_test'
plot(x, ...)

Arguments

`forest`	pmforest object.
`pmodels`	pmodel_identity object (pmodel(..., fun = identity)).
`data`	data.
`B`	number of bootstrap samples.
`x`	object of class heterogeneity_test.
`...`	ignored.

Value

list where the first element is the p-value und the second element is a data.frame with all neccessary infos to compute the p-value.

The test statistic is the difference in objective function between the base model and the personalised models. To compute the distribution under the Null we draw parametric bootstrap samples from the base model. For each bootstrap sample we again compute the difference in objective function between the base model and the personalised models. If the difference in the original data is greater than the difference in the bootstrap samples, we reject the null-hypothesis.

Examples

## Not run: 
set.seed(123)
n <- 160
trt <- factor(rep(0:1, each = n/2))
y <- 4 + (trt == 1) + rnorm(n)
z <- matrix(rnorm(n * 2), ncol = 2)

dat <- data.frame(y, trt, z)

mod <- lm(y ~ trt, data = dat)

## Note that ntree should usually be higher
frst <- pmforest(mod, ntree = 20) 
pmods <- pmodel(frst, fun = identity)

## Note that B should be at least 100
## The low B is just for demonstration 
## purposes.
tst <- pmtest(forest = frst, 
              pmodels = pmods, 
              B = 10) 
tst$pvalue
tst
plot(tst)

## End(Not run)

## Not run: 
set.seed(123)
n <- 160
trt <- factor(rep(0:1, each = n/2))
y <- 4 + (trt == 1) + rnorm(n)
z <- matrix(rnorm(n * 2), ncol = 2)

dat <- data.frame(y, trt, z)

mod <- lm(y ~ trt, data = dat)

## Note that ntree should usually be higher
frst <- pmforest(mod, ntree = 20) 
pmods <- pmodel(frst, fun = identity)

## Note that B should be at least 100
## The low B is just for demonstration 
## purposes.
tst <- pmtest(forest = frst, 
              pmodels = pmods, 
              B = 10) 
tst$pvalue
tst
plot(tst)

## End(Not run)

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,
  ...
)
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.

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.

Value

ctree object

Examples

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")))
}
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 predictions

Description

Compute predictions from pmtree object.

Usage

## S3 method for class 'pmtree'
predict(
  object,
  newdata = NULL,
  type = "node",
  predict_args = list(),
  perm = NULL,
  ...
)
## S3 method for class 'pmtree'
predict(
  object,
  newdata = NULL,
  type = "node",
  predict_args = list(),
  perm = NULL,
  ...
)

Arguments

`object`	pmtree object.
`newdata`	an optional data frame in which to look for variables with which to predict, if omitted, `object$data` is used.
`type`	character denoting the type of predicted value. The terminal node is returned for `"node"`. If `type = "pass"` the model predict method is used and arguments can be passed to it via `predict_args`. If `type = "coef"` the the model coefficients are returned.
`predict_args`	If `type = "pass"` arguments can be passed on to the model predict function.
`perm`	an optional character vector of variable names (or integer vector of variable location in `newdata`). Splits of nodes with a primary split in any of these variables will be permuted (after dealing with surrogates). Note that surrogate split in the perm variables will no be permuted.
`...`	passed on to predict.party (e.g. `perm`).

Value

predictions

Examples



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")))
}
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")))
}

Methods for pmtree

Description

Print and summary methods for pmtree objects.

Usage

## S3 method for class 'pmtree'
print(
  x,
  node = NULL,
  FUN = NULL,
  digits = getOption("digits") - 4L,
  footer = TRUE,
  ...
)

## S3 method for class 'pmtree'
summary(object, node = NULL, ...)

## S3 method for class 'summary.pmtree'
print(x, digits = 4, ...)

## S3 method for class 'pmtree'
coef(object, node = NULL, ...)
## S3 method for class 'pmtree'
print(
  x,
  node = NULL,
  FUN = NULL,
  digits = getOption("digits") - 4L,
  footer = TRUE,
  ...
)

## S3 method for class 'pmtree'
summary(object, node = NULL, ...)

## S3 method for class 'summary.pmtree'
print(x, digits = 4, ...)

## S3 method for class 'pmtree'
coef(object, node = NULL, ...)

Arguments

`x`	object.
`node`	node number, if any.
`FUN`	formatinfo function.
`digits`	number of digits.
`footer`	should footer be included?
`...`	further arguments passed on to `print.party`.
`object`	object.

Value

Residual sum of squares

Description

Returns the sum of the squared residuals for a given object.

Usage

rss(object, ...)

## Default S3 method:
rss(object, ...)
rss(object, ...)

## Default S3 method:
rss(object, ...)

Arguments

`object`	model object.
`...`	passed on to specific methods.

Value

sum of the squared residuals.

Examples

## example from ?lm
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
rss(lm.D9)
## example from ?lm
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
rss(lm.D9)

Survival plot for a given survreg model with one binary covariate.

Description

Can be used on its own but is also useable as plotfun in node_pmterminal.

Usage

survreg_plot(mod, data = NULL, theme = theme_classic(), yrange = NULL)
survreg_plot(mod, data = NULL, theme = theme_classic(), yrange = NULL)

Arguments

`mod`	A model of class survreg.
`data`	optional data frame. If NULL the data stored in mod is used.
`theme`	A ggplot2 theme.
`yrange`	Range of the y variable to be used for plotting. If NULL it will be 0 to max(y).

Examples

if(require("survival")) {
  survreg_plot(survreg(Surv(futime, fustat) ~ factor(rx), ovarian))
}

if(require("survival")) {
  survreg_plot(survreg(Surv(futime, fustat) ~ factor(rx), ovarian))
}

Variable Importance for pmforest

Description

See varimp.cforest.

Usage

## S3 method for class 'pmforest'
varimp(
  object,
  nperm = 1L,
  OOB = TRUE,
  risk = function(x, ...) -objfun(x, sum = TRUE, ...),
  conditional = FALSE,
  threshold = 0.2,
  ...
)
## S3 method for class 'pmforest'
varimp(
  object,
  nperm = 1L,
  OOB = TRUE,
  risk = function(x, ...) -objfun(x, sum = TRUE, ...),
  conditional = FALSE,
  threshold = 0.2,
  ...
)

Arguments

`object`	DESCRIPTION.
`nperm`	the number of permutations performed.
`OOB`	a logical determining whether the importance is computed from the out-of-bag sample or the learning sample (not suggested).
`risk`	the risk to be evaluated. By default the objective function (e.g. log-Likelihood) is used.
`conditional`	a logical determining whether unconditional or conditional computation of the importance is performed.
`threshold`	the value of the test statistic or 1 - p-value of the association between the variable of interest and a covariate that must be exceeded inorder to include the covariate in the conditioning scheme for the variable of interest (only relevant if conditional = TRUE).
`...`	passed on to `objfun`.

Value

A vector of 'mean decrease in accuracy' importance scores.

Package 'model4you'

Help Index

Add model information to a personalised-model-ctree

Description

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Fit function when model object is given

Description

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Prepare input for ctree/cforest from input of pmtree/pmforest

Description

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Plot for a given logistic regression model (glm with binomial family) with one binary covariate.

Description

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Table of coefficients for survreg model

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Survival plot for a given coxph model with one binary covariate.

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Density plot for a given lm model with one binary covariate.

Description

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Details

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Extract log-Likelihood

Description

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See Also

Panel-Generator for Visualization of pmtrees

Description

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Objective function

Description

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Objective function of personalised models

Description

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Objective function of a given pmtree

Description

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Check if model has only one factor covariate.

Description

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Compute model-based forest from model.

Description

Usage

Arguments

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See Also

Examples

Personalised model

Description