Help for package rfVarImpOOB

Title:

Unbiased Variable Importance for Random Forests

Version:

1.0.3

Date:

2022-06-30

Depends:

R (≥ 3.2.2), stats, randomForest

Imports:

ggplot2, ggpubr, dplyr,titanic,magrittr,ranger

Suggests:

knitr,rmarkdown

Author:

Markus Loecher <Markus.Loecher@gmail.com>

Maintainer:

Markus Loecher <Markus.Loecher@gmail.com>

Description:

Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also <doi:10.1080/03610926.2020.1764042>. The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split. This gain is aggregated for each split variable in a tree and averaged across trees.

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

Repository:

CRAN

LazyData:

true

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2022-07-01 08:16:57 UTC; loecherm

Date/Publication:

2022-07-01 14:40:02 UTC

computes accuracy of a vector

Description

Accuracy is defined as the proportion of correct labels

Usage

Accuracy(y, yHat, dig = 8)

Arguments

y

vector of categorical/nominal values

yHat

prediction/estimate

dig

number of digits

Value

Accuracy defined as proportion of values equal to majority

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples









Accuracy(c(rep(0,9),1), 1)


Accuracy(c(rep(0,9),1), 0)

computes inbag and OOB Gini importance averaged over all trees in a forest

Description

workhorse function of this package

Usage

GiniImportanceForest(RF, data, ylabel = "Survived", zeroLeaf = TRUE, 


    agg = c("mean", "median", "none")[1], score = c("PMDI21", 


        "MDI", "MDA", "MIA")[1], Predictor = Mode, verbose = 0)

Arguments

RF

object returned by call to randomForest()

data

data which was used to train the RF. NOTE: assumes setting of inbag=TRUE while training

ylabel

name of dependent variable

zeroLeaf

if TRUE discard the information gain due to splits resulting in n=1

agg

method of aggregating importance scores across trees. If "none" return the raw arrays (for debugging)

score

scoring method:MDI=mean decrease impurity (Gini),MDA=mean decrease accuracy (permutation),MIA=mean increase accuracy

Predictor

function to estimate node prediction, such as Mode or mean or median. Alternatively, pass an array of numbers as replacement for the yHat column of tree

verbose

level of verbosity

Value

matrix with variable importance scores and their stdevs

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples






data("titanic_train", package = "rfVarImpOOB",  envir = environment())


set.seed(123)


ranRows=sample(nrow(titanic_train), 300)


data=titanic_train[ranRows,]





RF = randomForest::randomForest(formula = Survived ~ Sex + Pclass + PassengerId,


                                data=data,


                                ntree=5,importance=TRUE,


                                mtry=3,keep.inbag=TRUE, 


                                nodesize = 20)


data$Survived = as.numeric(data$Survived)-1


VI_Titanic = GiniImportanceForest(RF, data,ylab="Survived")

computes Gini information gain for one tree from randomForest

Description

computes importance scores for an individual tree.

These can be based on Gini impurity or Accuracy or logloss

Usage

GiniImportanceTree(bag, RF, k, ylabel = "Survived", returnTree = FALSE, 


    zeroLeaf = TRUE, score = c("PMDI21", "MDI", "MDA", "MIA")[1], 


    Predictor = Mode, verbose = 0)

Arguments

bag

data to compute the Gini gain for

RF

object returned by call to randomForest()

k

which tree

ylabel

name of dependent variable

returnTree

if TRUE returns the tree data frame otherwise the aggregated Gini importance grouped by split variables

zeroLeaf

if TRUE discard the information gain due to splits resulting in n=1

score

scoring method:PMDI=mean decrease penalized Gini impurity (note:the last digit is the exponent of the penalty!),

MDI=mean decrease impurity (Gini), MDA=mean decrease accuracy (permutation),

MIA=mean increase accuracy

Predictor

function to estimate node prediction, such as Mode or mean or median. Alternatively, pass an array of numbers as replacement for the yHat column of tree

verbose

level of verbosity

Value

if returnTree==TRUE returns the tree data frame otherwise the aggregated Gini importance grouped by split variables

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples






rfTit = rfTitanic(nRows = 500,nodesize=10)


rfTit$data$Survived = as.numeric(rfTit$data$Survived)-1


k=1


tmp <- InOutBags(rfTit$RF, rfTit$data, k)


IndivTree =getTree(rfTit$RF,k)


#plot(as.party(tmp))#does not work


InTree = GiniImportanceTree(tmp$inbag,rfTit$RF,k,returnTree=TRUE)


OutTree = GiniImportanceTree(tmp$outbag,rfTit$RF,k,returnTree=TRUE)

separates data into inbag and outbag

Description

convenience function to mitigate risk of improperly disentangling train/test

NOTE: the original row names (too dangerous for repeated rows) are not kept but instead recorded in a separate column

Usage

InOutBags(RF, data, k, inclRowNames = TRUE, NullRowNames = TRUE, 


    verbose = 0)

Arguments

RF

object returned by call to randomForest()

data

data which was used to train the RF. NOTE: assumes setting of inbag=TRUE while training

k

tree number

inclRowNames

create extra column of original row names

NullRowNames

if TRUE set row names to NULL

verbose

level of verbosity

Value

inbag and outbag subsets of the original data

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples



rfTit = rfTitanic(nRows = 200,nodesize=10, ntree = 5)





k=1


tmp <- InOutBags(rfTit$RF, rfTit$data, k)

computes the mode of an array

Description

returns the mode of a vector

Usage

Mode(x)

Arguments

x

vector to find mode of

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples






Mode(rep(letters[1:3],1:3))


Mode(c(TRUE,TRUE,FALSE))


Mode(c(TRUE,TRUE,FALSE,FALSE))

Arabidopsis thaliana

Description

RNA editing is the process whereby RNA is modified from the sequence of the corresponding DNA template [1]. For instance, cytidine-to-uridine conversion (abbreviated C-to-U conversion) is common in plant mitochondria. The mechanisms of this conversion remain largely unknown, although the role of neighboring nucleotides is emphasized. Cummings and Myers [1] suggest to use information from sequence regions flanking the sites of interest to predict editing in Arabidopsis thaliana, Brassicanapus and Oryza sativa based on random forests. The Arabidopsis thaliana data of [1] can be loaded from the journal Web site.

For each of the 876 observations, the data set gives

the response at the site of interest (binary: edited/not edited) and as potential predictor variables the 40 nucleotides at positions -20 to 20, relative to the edited site (4 categories), cp: the codon position (4 categories), fe: the estimated folding energy (continuous) and dfe: the difference in estimated folding energy between pre- edited and edited sequences (continuous).

Usage

arabidopsis

Format

Data frame with columns

edit: binary:the response at the site of interest
X.k: nucleotides at positions -k, relative to the edited site (4 categories)
Xk: nucleotides at positions k, relative to the edited site (4 categories)
cp: the codon position (4 categories),
fe: the estimated folding energy (continuous)
dfe: the difference in estimated folding energy between pre- edited and edited sequences (continuous)

Source

[1] Cummings, Michael P, and Daniel S Myers. Simple Statistical Models Predict C-to-U Edited Sites in Plant Mitochondrial RNA. BMC Bioinformatics, 2004, 7.

Examples

arabidopsis

compute Gini impurity for binary values only

Description

simple function to compute simple or penalized Gini impurity

The "penalty" compares the class probabilities pHat with a reference estimate pEst

which would typically serve as a prediction (e.g. in a tree node).

Usage

gini_index(pHat, pEst = NULL, k = 2, kind = 1, w = 2)

Arguments

pHat

probabilities from the current data,

pEst

estimated class probabilities (typically from an earlier inbag estimation). Only pass if you intend to compute the "validation-penalized Gini"

k

exponent of penalty term: abs(pHat-pEst)^k

kind

kind of penalty

w

weights, default is 2 if you pass just a single probability instead of the vector (p,1-p)

Value

simple or penalized Gini impurity

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples



#Test binary case:





gini_index(0.5,0.5,kind=1)


gini_index(0.9,0.1,kind=1)


gini_index(0.1,0.9,kind=1)





gini_index(0.5,0.5,kind=2)


gini_index(0.9,0.1,kind=2)


gini_index(0.1,0.9,kind=2)








gini_index(0.5,0.5,kind=3)


gini_index(0.9,0.1,kind=3)


gini_index(0.1,0.9,kind=3)

computes Gini index

Description

computes Gini index

Usage

gini_process(classes, splitvar = NULL)

Arguments

classes

vector of factors/categorical vars

splitvar

split variable

Value

Gini index

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples



#Test binary case:





#50/50split


gini_process(c(rep(0,10),rep(1,10)))#0.5 CORRECT !


#10/90split


gini_process(c(rep(0,1),rep(1,9)))#0.18= CORRECT ! 


#0/100split


gini_process(factor(c(rep(0,0),rep(1,10)), levels=c(0,1)))#0








#Test binary case:





#25/25/25/25 split


gini_process(factor(c(rep(0,5),rep(1,5),rep(2,5),


                      rep(3,5)), levels=c(0:3)))#0.75 = 4*0.25*0.75 CORRECT !


#10/10/10/70 split


gini_process(factor(c(rep(0,1),rep(1,1),rep(2,1),


                      rep(3,7)), levels=c(0:3)))#0.48 = 3*0.1*0.9+0.7*0.3  CORRECT !


#0/0/0/100 split


gini_process(factor(c(rep(0,0),rep(1,0),rep(2,0),


                      rep(3,20)), levels=c(0:3)))#0. CORRECT !

Compute the Lp norm of a vector.

Description

Compute the Lp norm of a vector.

Usage

lpnorm(x, p = 2)

Arguments

x

vector to compute the Lp norm of

p

parameter of p norm

Value

Lp norm of a vector or NA

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples



lpnorm(1:10)


lpnorm(matrix(1:25, 5, 5))


lpnorm(split(1:25, rep(1:5, each = 5)))





lpnorm(1:10, 1)


lpnorm(matrix(1:25, 5, 5), 1)


lpnorm(split(1:25, rep(1:5, each = 5)), 1)





lpnorm(rnorm(10), 0)


lpnorm(matrix(rnorm(25), 5, 5), 0)


lpnorm(split(rnorm(25), rep(1:5, each = 5)), 0)





lpnorm(-5:5, Inf)


lpnorm(matrix(-25:-1, 5, 5), Inf)


lpnorm(split(-25:-1, rep(1:5, each = 5)), Inf)

computes log loss for multiclass problem

Description

computes log loss for multiclass problem

Usage

mlogloss(actual, pred_m, eps = 0.001)

Arguments

actual

integer vector with truth labels, values range from 0 to n - 1 classes

pred_m

predicted probs: column 1 => label 0, column 2 => label 1 and so on

eps

numerical cutoff taken very high

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples






# require(nnet)


# set.seed(1)


# actual = as.integer(iris$Species) - 1


# fit = nnet(Species ~ ., data = iris, size = 2)


# pred = predict(fit, iris)#note this is a 3-column prediction matrix!


# 


# mlogloss(actual, pred) # 0.03967





#library(titanic)


#baseline prediction


#data(titanic_train, package="titanic")


yHat = mean(titanic_train$Survived)#0.383838


mlogloss(titanic_train$Survived,yHat)


#try factors


titanic_train$Survived = as.factor(titanic_train$Survived)


mlogloss(titanic_train$Survived,yHat)

creates barplots for variable importances

Description

creates barplots for variable importances

Usage

plotVI(VIbench, order_by = "Gini_OOB", decreasing = TRUE)

Arguments

VIbench

matrix with importance scores as returned by GiniImportanceForest

order_by

how to order

decreasing

which direction to sort

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples



data("titanic_train", package = "rfVarImpOOB",  envir = environment())


set.seed(123)


ranRows=sample(nrow(titanic_train), 300)


data=titanic_train[ranRows,]





RF = randomForest::randomForest(formula = Survived ~ Sex + Pclass + PassengerId,


                                       data=data,


                                       ntree=5,importance=TRUE,


                                       mtry=3,keep.inbag=TRUE, 


                                       nodesize = 20)


data$Survived = as.numeric(data$Survived)-1


VI_Titanic = GiniImportanceForest(RF, data,ylab="Survived")


plotVI(VI_Titanic,decreasing = TRUE)

creates barplots for variable importances

Description

creates barplots for variable importances including permutation scores

Usage

plotVI2(VIbench, decreasing = TRUE, with_MDA = TRUE, ordered_by = "inbag", 


    score = "Gini Importance", horizontal = TRUE, fill = "order", 


    labelSize = 10, nrow = 3)

Arguments

VIbench

matrix with importance scores as returned by GiniImportanceForest

decreasing

which direction to sort

with_MDA

also visualize mean decrease in accuracy (permutation importance)

ordered_by

how to order

score

type of importance score: Gini, MIA,..

horizontal

horizontal barplot instead of vertical ?

fill

fill style for barplots; use e.g. shQuote("blue") to pass color strings

labelSize

size of axis labels

nrow

number of rows of ploztz arrangement

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples



data("titanic_train", package = "rfVarImpOOB",  envir = environment())


set.seed(123)


ranRows=sample(nrow(titanic_train), 300)


data=titanic_train[ranRows,]





RF = randomForest::randomForest(formula = Survived ~ Sex + Pclass + PassengerId,


                         data=data,


                    ntree=5,importance=TRUE,


                    mtry=3,keep.inbag=TRUE, 


                    nodesize = 20)


data$Survived = as.numeric(data$Survived)-1


VI_Titanic = GiniImportanceForest(RF, data,ylab="Survived")


plotVI2(VI_Titanic,decreasing = TRUE)

recursive traversal of tree assigning row numbers of data for each node and leaf

Description

Recursive calling stops at leaf after which the function propagates back up the tree

Usage

preorder2(treeRow, bag, tree, verbose = 0)

Arguments

treeRow

current row of tree dataframe to be

bag

The data for the current row

tree

tree (from randomForest::getTree to be traversed

verbose

level of verbosity

Value

tree with rownames in column node

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples


data("titanic_train", package = "rfVarImpOOB",  envir = environment())



set.seed(123)

ranRows=sample(nrow(titanic_train), 300)



RF = randomForest::randomForest(formula = Survived ~ Sex + Pclass + PassengerId,

                    data=titanic_train[ranRows,],

                    ntree=5,importance=TRUE,

                    mtry=3,keep.inbag=TRUE, 

                    nodesize = 1)

k=2

tree = randomForest::getTree(RF, k, labelVar = TRUE) 

tree$node=NA

attr(tree, "rflib") = "randomForest"
inbag = rep(rownames(RF$inbag),time=RF$inbag[,k])

#trainBag=titanic_train[inbag,]

trainBag=titanic_train[ranRows,][inbag,]

tree=preorder2(1,trainBag,tree)

fit a random forest model on the titanic data

Description

convenience function to reduce overhead of repeatedly fitting RF to titanic data

Usage

rfTitanic(formel = Survived ~ Sex + Pclass + PassengerId, nRows = 500, 


    ntree = 10, mtry = 3, nodesize = 1)

Arguments

formel

formula

nRows

subsample size

ntree

number of trees

mtry

mtry

nodesize

nodesize

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Examples



rfTit = rfTitanic(nRows = 500,nodesize=10)

splits the data from parent node into left and right children

Description

The function properly splits on factor levels

Usage

splitBag(treeRow, bag, tree)

Arguments

treeRow

current row of tree dataframe to be

bag

The data for the current row

tree

tree (from randomForest::getTree)

Value

list with elements left_daughter, right_daughter

Author(s)

Markus Loecher <Markus.Loecher@gmail.com>

Titanic train data.

Description

Titanic train data.

Usage

titanic_train

Format

Data frame with columns

PassengerId: Passenger ID
Survived: Passenger Survival Indicator
Pclass: Passenger Class
Name: Name
Sex: Sex
Age: Age
SibSp: Number of Siblings/Spouses Aboard
Parch: Number of Parents/Children Aboard
Ticket: Ticket Number
Fare: Passenger Fare
Cabin: Cabin
Embarked: Port of Embarkation

Source

https://www.kaggle.com/c/titanic/data

Examples

titanic_train