| Type: | Package |
| Title: | Binomial Random Forest Feature Selection |
| Version: | 0.1.0 |
| URL: | https://www.biorxiv.org/content/10.1101/681973v1.abstract |
| Description: | The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| biocViews: | Software, GenePrediction, StatisticalMethod, DecisionTree, DimensionReduction, ExperimentalDesign |
| Imports: | randomForest, data.table, stats, rlist |
| Suggests: | foreach, knitr, rmarkdown, correlbinom |
| RoxygenNote: | 7.0.2 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2020-03-26 16:11:02 UTC; samirrachidzaim-admin |
| Author: | Samir Rachid Zaim [aut, cre] |
| Maintainer: | Samir Rachid Zaim <samirrachidzaim@math.arizona.edu> |
| Repository: | CRAN |
| Date/Publication: | 2020-03-26 17:00:13 UTC |
random forest feature selection based on binomial exact test
Description
cv.binomialRF is the cross-validated form of the binomialRF, where K-fold crossvalidation is conducted to assess the feature's significance. Using the cvFolds=K parameter, will result in a K-fold cross-validation where the data is 'chunked' into K-equally sized groups and then the averaged result is returned.
Usage
.cv_binomialRF(X, y, cvFolds = 5, fdr.threshold = 0.05,
fdr.method = "BY", ntrees = 2000, keep.both = FALSE)
Arguments
X |
design matrix |
y |
class label |
cvFolds |
how many times should we perform cross-validation |
fdr.threshold |
fdr.threshold for determining which set of features are significant |
fdr.method |
how should we adjust for multiple comparisons (i.e., |
ntrees |
how many trees should be used to grow the |
keep.both |
should we keep the naive binomialRF as well as the correlated adjustment |
Value
a data.frame with 4 columns: Feature Name, cross-validated average for Frequency Selected, CV Median (Probability of Selecting it randomly), CV Median(Adjusted P-value based on fdr.method), and averaged number of times selected as signficant.
References
Zaim, SZ; Kenost, C.; Lussier, YA; Zhang, HH. binomialRF: Scalable Feature Selection and Screening for Random Forests to Identify Biomarkers and Their Interactions, bioRxiv, 2019.
Examples
set.seed(324)
###############################
### Generate simulation data
###############################
X = matrix(rnorm(1000), ncol=10)
trueBeta= c(rep(10,5), rep(0,5))
z = 1 + X %*% trueBeta
pr = 1/(1+exp(-z))
y = as.factor(rbinom(100,1,pr))
###############################
### Run cross-validation
###############################
random forest feature selection based on binomial exact test
Description
binomialRF is the R implementation of the feature selection algorithm by (Zaim 2019)
Usage
binomialRF(X,y, fdr.threshold = .05,fdr.method = 'BY',
ntrees = 2000, percent_features = .5,
keep.both=FALSE, user_cbinom_dist=NULL,
sampsize=round(nrow(X)*.63))
Arguments
X |
design matrix |
y |
class label |
fdr.threshold |
fdr.threshold for determining which set of features are significant |
fdr.method |
how should we adjust for multiple comparisons (i.e., |
ntrees |
how many trees should be used to grow the |
percent_features |
what percentage of L do we subsample at each tree? Should be a proportion between (0,1) |
keep.both |
should we keep the naive binomialRF as well as the correlated adjustment |
user_cbinom_dist |
insert either a pre-specified correlated binomial distribution or calculate one via the R package |
sampsize |
how many samples should be included in each tree in the randomForest |
Value
a data.frame with 4 columns: Feature Name, Frequency Selected, Probability of Selecting it randomly, Adjusted P-value based on fdr.method
References
Zaim, SZ; Kenost, C.; Lussier, YA; Zhang, HH. binomialRF: Scalable Feature Selection and Screening for Random Forests to Identify Biomarkers and Their Interactions, bioRxiv, 2019.
Examples
set.seed(324)
###############################
### Generate simulation data
###############################
X = matrix(rnorm(1000), ncol=10)
trueBeta= c(rep(10,5), rep(0,5))
z = 1 + X %*% trueBeta
pr = 1/(1+exp(-z))
y = as.factor(rbinom(100,1,pr))
###############################
### Run binomialRF
###############################
require(correlbinom)
rho = 0.33
ntrees = 250
cbinom = correlbinom(rho, successprob = calculateBinomialP(10, .5), trials = ntrees,
precision = 1024, model = 'kuk')
binom.rf <-binomialRF(X,y, fdr.threshold = .05,fdr.method = 'BY',
ntrees = ntrees,percent_features = .5,
keep.both=FALSE, user_cbinom_dist=cbinom,
sampsize=round(nrow(X)*rho))
print(binom.rf)
calculate the probability, p, to conduct a binomial exact test
Description
calculateBinomialP returns a probability of randomly selecting a feature as the root node in a decision tree. This is a generic function that is called internally in binomialRF but that may also be called directly if needed. The arguments ...
should be, L= Total number of features in X, and percent_features= what percent of L is subsampled in the randomForest call.
Usage
calculateBinomialP(L, percent_features)
Arguments
L |
the total number of features in X. Should be a positive integer >1 |
percent_features |
what percentage of L do we subsample at each tree? Should be a proportion between (0,1) |
Value
If L is an integeter returns a probability value for selecting predictor Xj randomly
Examples
calculateBinomialP(110, .4)
calculateBinomialP(13200, .5)
calculate the probability, p, to conduct a binomial exact test
Description
calculateBinomialP_Interaction returns a probability of randomly selecting a feature as the root node in a decision tree. This is a generic function that is called internally in binomialRF but that may also be called directly if needed. The arguments ...
should be, L= Total number of features in X, and percent_features= what percent of L is subsampled in the randomForest call.
Usage
calculateBinomialP_Interaction(L, percent_features, K = 2)
Arguments
L |
the total number of features in X. Should be a positive integer >1 |
percent_features |
what percentage of L do we subsample at each tree? Should be a proportion between (0,1) |
K |
interaction level |
Value
If L is an integeter returns a probability value for selecting predictor Xj randomly
Examples
calculateBinomialP_Interaction(110, .4,2 )
random forest feature selection based on binomial exact test
Description
binomialRF is the R implementation of the feature selection algorithm by (Zaim 2019)
Usage
geneset_binomialRF(binomialRF_object, gene_ontology, cutoff = 0.2)
Arguments
binomialRF_object |
the binomialRF object output |
gene_ontology |
a two- or three-column representation of a gene ontology with gene and geneset names |
cutoff |
a real-valued number between 0 and 1, used as a p-value threshold |
Value
a data.frame with 4 columns: Geneset Name, P-value, Adjusted P-value based on fdr.method
References
Zaim, SZ; Kenost, C.; Lussier, YA; Zhang, HH. binomialRF: Scalable Feature Selection and Screening for Random Forests to Identify Biomarkers and Their Interactions, bioRxiv, 2019.
random forest feature selection based on binomial exact test
Description
k_binomialRF is the R implementation of the interaction feature selection algorithm by (Zaim 2019). k_binomialRF extends the binomialRF algorithm by searching for k-way interactions.
Usage
k_binomialRF(X, y, fdr.threshold = 0.05, fdr.method = "BY",
ntrees = 2000, percent_features = 0.3, K = 2, cbinom_dist = NULL,
sampsize = nrow(X) * 0.4)
Arguments
X |
design matrix |
y |
class label |
fdr.threshold |
fdr.threshold for determining which set of features are significant |
fdr.method |
how should we adjust for multiple comparisons (i.e., |
ntrees |
how many trees should be used to grow the |
percent_features |
what percentage of L do we subsample at each tree? Should be a proportion between (0,1) |
K |
for multi-way interactions, how deep should the interactions be? |
cbinom_dist |
user-supplied correlated binomial distribution |
sampsize |
user-supplied sample size for random forest |
Value
a data.frame with 4 columns: Feature Name, Frequency Selected, Probability of Selecting it randomly, Adjusted P-value based on fdr.method
References
Zaim, SZ; Kenost, C.; Lussier, YA; Zhang, HH. binomialRF: Scalable Feature Selection and Screening for Random Forests to Identify Biomarkers and Their Interactions, bioRxiv, 2019.
Examples
set.seed(324)
###############################
### Generate simulation data
###############################
X = matrix(rnorm(1000), ncol=10)
trueBeta= c(rep(10,5), rep(0,5))
z = 1 + X %*% trueBeta
pr = 1/(1+exp(-z))
y = rbinom(100,1,pr)
###############################
### Run interaction model
###############################
require(correlbinom)
rho = 0.33
ntrees = 250
cbinom = correlbinom(rho, successprob = calculateBinomialP_Interaction(10, .5,2),
trials = ntrees, precision = 1024, model = 'kuk')
k.binom.rf <-k_binomialRF(X,y, fdr.threshold = .05,fdr.method = 'BY',
ntrees = ntrees,percent_features = .5,
cbinom_dist=cbinom,
sampsize=round(nrow(X)*rho))
A prebuilt distribution for correlated binary data
Description
This data contains probability mass functions (pmf's) for correlated binary data for various parameters. The sum of correlated exchangeable binary data is a generalization of the binomial distribution that deals with correlated trials. The correlation in decision trees occurs as the subsampling and bootstrapping step in random forests touch the same data, creating a co-dependency. This data contains some pre-calculated distributions for random forests with 500, 1000, and 2000 trees with 10, 100, and 1000 features. For more distributions, they can be calculated via the correlbinom R package.
Usage
pmf_list
Format
A list of lists
References
Witt, Gary. "A Simple Distribution for the Sum of Correlated, Exchangeable Binary Data." Communications in Statistics-Theory and Methods 43, no. 20 (2014): 4265-4280.