mobForest v1.3.1
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Model Based Random Forest Analysis
Functions to implements random forest method for model based
recursive partitioning. The mob() function, developed by Zeileis et al. (2008),
within 'party' package, is modified to construct model-based decision trees based
on random forests methodology. The main input function mobforest.analysis() takes
all input parameters to construct trees, compute out-of-bag errors, predictions,
and overall accuracy of forest. The algorithm performs parallel computation
using cluster functions within 'parallel' package.
Readme
mobForest 
mobForest R Package
mobForest implements random forest method for model based recursive partitioning. The mob() function, developed by Zeileis et al (2008), within party package, is modified to construct model-based decision trees based on random forests methodology. The main input function mobforest.analysis() takes all input parameters to construct trees, compute out-of-bag errors, predictions, and overall accuracy of forest. The algorithm performs parallel computation using clusterApply() function within the parallel package.
Installation
install.packages("mobForest")
Usage
To run the example, you will need the mlbench package. It contains a boston housing dataset for machine learning algorithms to run benchmark tests on.
library(mlbench)
set.seed(1111)
# Random Forest analysis of model based recursive partitioning load data
data("BostonHousing", package = "mlbench")
BostonHousing <- BostonHousing[1:90, c("rad", "tax", "crim", "medv", "lstat")]
# Recursive partitioning based on linear regression model medv ~ lstat with 3 trees. 1 core/processor used.
rfout <- mobforest.analysis(as.formula(medv ~ lstat), c("rad", "tax", "crim"),
mobforest_controls = mobforest.control(ntree = 3, mtry = 2, replace = TRUE,
alpha = 0.05, bonferroni = TRUE, minsplit = 25), data = BostonHousing,
processors = 1, model = linearModel, seed = 1111)
rfout
Functions in mobForest
| Name | Description | |
| compute.acc | Predictive accuracy estimates across trees for logistic regression model | |
| compute.r2 | Predictive accuracy estimates across trees for linear or poisson regression | |
| compute.mse | Predictive accuracy estimates (MSE) across trees for linear or poisson regression model. | |
| mob_fit_checksplit | Utility Function. Taken from party package to remove ":::" warning | |
| mob_fit_childweights | Utility Function. Taken from party package to remove ":::" warning | |
| mobforest.output | Model-based random forest object | |
| mobforest.output-class | Class "mobforest.output" of mobforest model | |
| residual.plot | Produces two plots: a) histogram of residuals, b) predicted Vs residuals. This feature is applicable only when linear regression is considered as the node model. | |
| mobforest.control-class | Class "mobforest.control" of mobForest model | |
| mobforest.control | Control parameters for random forest | |
| string.formula | Model in the formula object converted to a character | |
| get.pred.values | Get predictions summarized across trees for out-of-bag cases or all cases for cases from new test data | |
| get.varimp | Variable importance scores computed through random forest analysis | |
| prediction.output-class | Class "prediction.output" of mobForest model | |
| mob_fit_splitnode | Utility Function. Taken from party package to remove ":::" warning | |
| mobforest.analysis | Model-based random forest analysis | |
| tree.predictions | Predictions from tree model | |
| varimp.output | Variable importance matrix containing the decrease in predictive accuracy after permuting the variables across all trees | |
| varimp.output-class | Class "varimp.output" of mobforest model | |
| varimplot | A plot with variable importance score on X-axis and variable name on Y-axis. | |
| prediction.output | Predictions and predictive accuracy estimates | |
| mob_fit_fluctests | Utility Function. Taken from party package to remove ":::" warning | |
| mob_fit_getlevels | Utility Function. Taken from party package to remove ":::" warning | |
| print.estimates | Predictive Accuracy Report | |
| predictive.acc | Predictive performance across all trees | |
| get.mf.object.glm | Fit a general linear model to a mobForest model | |
| get.mf.object.lm | Fit a linear model to a mobForest model | |
| mob_fit_getobjfun | Utility Function. Taken from party package to remove ":::" warning | |
| mob_fit_setupnode | Utility Function. Taken from party package to remove ":::" warning | |
| logistic.acc | Contingency table: Predicted vs. Observed Outcomes | |
| bootstrap | This method computes predicted outcome for each observation in the data frame using the tree model supplied as an input argument. | |
| mob.rf.tree | Model based recursive partitioning - randomized subset of partition variables considered during each split. | |
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Details
| Type | Package |
| Date | 2019-07-31 |
| License | GPL (>= 2) |
| RoxygenNote | 6.1.1 |
| NeedsCompilation | no |
| Packaged | 2019-07-31 20:21:51 UTC; krjones |
| Repository | CRAN |
| Date/Publication | 2019-07-31 21:10:03 UTC |
| imports | graphics , methods , modeltools , stats |
| suggests | lattice , mlbench (>= 2.1) , testthat (>= 1.0.2) |
| depends | parallel (>= 3.4.1) , party (>= 1.2-4) , sandwich (>= 2.4.0) , strucchange (>= 1.5-1) , zoo (>= 1.8-0) |
| Contributors | Carolin Strobl, Torsten Hothorn, Kurt Hornik, Achim Zeileis, Kasey Jones, Nikhil Garge, Barry Eggleston, Georgiy Bobashev, Benjamin Carper |
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