R Graphical Manual

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Last data update: 2014.03.03

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R Release (3.2.3)
CranContrib
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Results 131 - 140 of 182600 found.
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splitClusters (Package: randomUniformForest) :

Given one (or many) cluster(s), the function splits it in two new clusters.

● Data Source: CranContrib
● Keywords: clustering, dimension, learning, reduction, unsupervised
● Alias: splitClusters
● 0 images

autoMPG (Package: randomUniformForest) : Auto MPG Data Set

Revised from CMU StatLib library, data concerns city-cycle fuel consumption.

● Data Source: CranContrib
● Keywords:
● Alias: autoMPG
● 0 images

postProcessingVotes (Package: randomUniformForest) : Post-processing for Regression

Post-processing use OOB votes and predicted values to build more accurate estimates of Response values. Note that post-processing can not ensure that new estimate will have a lower error. It works for many cases but not all.

● Data Source: CranContrib
● Keywords:
● Alias: postProcessingVotes
● 0 images

ConcreteCompressiveStrength (Package: randomUniformForest) : Concrete Compressive Strength Data Set

Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients.

● Data Source: CranContrib
● Keywords:
● Alias: ConcreteCompressiveStrength
● 0 images

importance.randomUniformForest (Package: randomUniformForest) : Variable Importance for random Uniform Forests

Compute object that leads to a full analysis of features (importance, dependency, interactions, selection, ...).

● Data Source: CranContrib
● Keywords:
● Alias: importance, importance.randomUniformForest, plot.importance, print.importance
● 0 images

internalFunctions (Package: randomUniformForest) : All internal functions

Internal functions for random Uniform Forests

● Data Source: CranContrib
● Keywords:
● Alias: CheckSameValuesInAllAttributes, L1InformationGainCPP, MDSscale, NAfactor2matrix, classifyMatrixCPP, dates2numeric, filterOutliers, insert.in.vector, leafNode, perspWithcol, plotTreeCore, randomUniformForestCore, randomUniformForestCore.predict, rmNA, rmNoise, specClust, standardize_vect, uniformDecisionTree, which.is.duplicate, which.is.nearestCenter
● 0 images

simulationData (Package: randomUniformForest) : Simulation of Gaussian vector

Simulate a Gaussian vector with 'p' independent components of length 'n'. Parameters of each component are uniformly random and are taken between -10 and 10, with (absolute) standard deviation equals mean.

● Data Source: CranContrib
● Keywords:
● Alias: simulationData
● 0 images

predict.randomUniformForest (Package: randomUniformForest) : Predict method for random Uniform Forests objects

Prediction of test data with random Uniform Forests. Many options are allowed, the default one rendering exactly the same type of variable than the one of training labels.

● Data Source: CranContrib
● Keywords:
● Alias: predict, predict.randomUniformForest
● 0 images

as.supervised (Package: randomUniformForest) :

Turn an unsupervised object of class unsupervised into a supervised one of class RandomUniformForest, allowing prediction of next unlabelled datasets, full analysis of variable importance in the unsupervised case and incremental unsupervised learning.

● Data Source: CranContrib
● Keywords: learning, supervised, unsupervised
● Alias: as.supervised
● 0 images

rm.trees (Package: randomUniformForest) : Remove trees from a random Uniform Forest

Remove any number of trees from a random Uniform Forest, especially in case of incremental learning.

● Data Source: CranContrib
● Keywords:
● Alias: rm.trees
● 0 images