mobForest (version 1.3.1)

bootstrap: This method computes predicted outcome for each observation in the data frame using the tree model supplied as an input argument.

Description

This method computes predicted outcome for each observation in the data frame using the tree model supplied as an input argument.

Usage

bootstrap(i, data, main_model, partition_vars, mtry, new_test_data,
  mobforest_controls, fraction, model, family, prob_cutoff = 0.5)

Arguments

i

the tree

data

A data frame containing the variables in the model.

main_model

A model in character format

partition_vars

A vector of partition variables

mtry

A Random subset of partition variables to be considered at each

new_test_data

A data frame representing test data for validating random forest model. This data is not used in in tree building process.

mobforest_controls

The mobforest_controls passed into mobforest.analysis

fraction

number of observations to draw without replacement (only relevant if replace = FALSE)

model

A model of class "StatModel" used for fitting observations in current node. This parameter allows fitting a linear model or generalized linear model with formula y ~ x_1 + ... + x_k. The Parameter "linearModel" fits linear model. The parameter "glinearModel" fits Poisson or logistic regression model depending upon the specification of parameter "family" (explained next). If "family" is specified as binomial() then logistic regression is performed. If the "family" is specified as poisson() then Poisson regression is performed.

family

A description of error distribution and link function to be used in the model. This parameter needs to be specified if generalized linear model is considered. The parameter "binomial()" is to be specified when logistic regression is considered and "poisson()" when Poisson regression is considered as the node model. The values allowed for this parameter are binomial() and poisson().

prob_cutoff

In case of logistic regression as a node model, the predicted probabilities for OOB cases are converted into classes (yes/no, high/low, etc as specified) based on this probability cutoff. If logistic regression is not considered as node model, the prob_cutoff = NULL. By default it is 0.5 when parameter not specified (and logistic regression considered).

Value

A list model performance metrics including R2/accuracy, predictions, MSE, and variable importance

Examples

# NOT RUN {
formula <- as.formula(medv ~ lstat)
# load data
data("BostonHousing", package = "mlbench")
mobforest_controls <- 
  mobforest.control(ntree = 1, mtry = 2, replace = TRUE,
                    alpha = 0.05, bonferroni = TRUE, minsplit = 25)

out <- bootstrap(i, data = BostonHousing, main_model = string.formula(formula),
                 partition_vars = partition_vars <- c("rad", "crim", "tax"),
                 mtry = 2, new_test_data = as.data.frame(matrix(0,0,0)),
                 mobforest_controls = mobforest_controls, fraction = 1,
                 model = linearModel, family = "", prob_cutoff = .5)
out
# }