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Last data update: 2014.03.03

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Results 41 - 50 of 182600 found.
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predict.drandomForest (Package: randomForest.ddR) : distributed predict method for applying a random forest objects on a darray or a dframe

This function can be used to apply a model of type drandomForest or randomForest to a new data for prediction.

● Data Source: CranContrib
● Keywords: classification, regression
● Alias: predict.drandomForest
● 0 images

rfsrc.news (Package: randomForestSRC) : Show the NEWS file

Show the NEWS file of the randomForestSRC package.

● Data Source: CranContrib
● Keywords: documentation
● Alias: rfsrc.news
● 0 images

randomForestSRC-package (Package: randomForestSRC) :

This package provides a unified treatment of Breiman's random forests (Breiman 2001) for a variety of data settings. Regression and classification forests are grown when the response is numeric or categorical (factor), while survival and competing risk forests (Ishwaran et al. 2008, 2012) are grown for right-censored survival data. Multivariate regression and classification responses as well as mixed outcomes (regression/classification responses) are also handled as are unsupervised forests. Different splitting rules invoked under deterministic or random splitting are available for all families. Variable predictiveness can be assessed using variable importance (VIMP) measures for single, as well as grouped variables. Variable selection is implemented using minimal depth variable selection (Ishwaran et al. 2010). Missing data (for x-variables and y-outcomes) can be imputed on both training and test data. The underlying code is based on Ishwaran and Kogalur's now retired randomSurvivalForest package (Ishwaran and Kogalur 2007), and has been significantly refactored for improved computational speed.

● Data Source: CranContrib
● Keywords: package
● Alias: randomForestSRC-package
● 0 images

stat.split (Package: randomForestSRC) : Acquire Split Statistic Information

Extract split statistic information from the forest. The function returns a list of length ntree, in which each element corresponds to a tree. The element [[b]] is itself a vector of length xvar.names identified by its x-variable name. Each element [[b]]$xvar contains the complete list of splits on xvar with associated identifying information. The information is as follows:

● Data Source: CranContrib
● Keywords: splitting behavior
● Alias: stat.split, stat.split.rfsrc
● 0 images

rf2rfz (Package: randomForestSRC) : Save RF-SRC in .rfz Compressed Format

rf2rfz saves a RF-SRC object as a .rfz compressed file that is readable by the randomForestSRC Java plugin that is capable of visualizing the trees in the forest.

● Data Source: CranContrib
● Keywords: forest
● Alias: rf2rfz
● 0 images

plot.variable (Package: randomForestSRC) : Plot Marginal Effect of Variables

Plot the marginal effect of an x-variable on the class probability (classification), response (regression), mortality (survival), or the expected years lost (competing risk) from a RF-SRC analysis. Users can select between marginal (unadjusted, but fast) and partial plots (adjusted, but slow).

● Data Source: CranContrib
● Keywords: plot
● Alias: plot.variable, plot.variable.rfsrc
● 0 images

plot.competing.risk (Package: randomForestSRC) : Plots for Competing Risks

Plot the ensemble cumulative incidence function (CIF) and cause-specific cumulative hazard function (CSCHF) from a competing risk analysis.

● Data Source: CranContrib
● Keywords: plot
● Alias: plot.competing.risk, plot.competing.risk.rfsrc
● 0 images

max.subtree (Package: randomForestSRC) : Acquire Maximal Subtree Information

Extract maximal subtree information from a RF-SRC object. Used for variable selection and identifying interactions between variables.

● Data Source: CranContrib
● Keywords: variable selection
● Alias: max.subtree, max.subtree.rfsrc
● 0 images

rfsrcSyn (Package: randomForestSRC) : Synthetic Random Forests

Grows a synthetic random forest (RF) using RF machines as synthetic features. Applies only to regression and classification settings.

● Data Source: CranContrib
● Keywords: forest, predict
● Alias: rfsrcSyn, rfsrcSyn.rfsrc
● 0 images

rfsrc (Package: randomForestSRC) : Random Forests for Survival, Regression and Classification (RF-SRC)

A random forest (Breiman, 2001) is grown using user supplied training data. Applies when the response (outcome) is numeric, categorical (factor), or right-censored (including competing risk), and yields regression, classification, and survival forests, respectively. The resulting forest, informally referred to as a RF-SRC object, contains many useful values which can be directly extracted by the user and/or parsed using additional functions (see the examples below). This is the main entry point to the randomForestSRC package.

● Data Source: CranContrib
● Keywords: forest
● Alias: randomForestSRC, rfsrc
● 0 images