perm.features: Feature Selection via Monte Carlo Permutation

In cdeterman/OmicsMarkeR: Classification and Feature Selection for 'Omics' Datasets

Description

Applies Monte Carlo permutations to user specified models. The user can either use the results from fs.stability or provide specified model parameters.

Usage

perm.features(fs.model = NULL, X, Y, method, sig.level = 0.05, nperm = 10,
  allowParallel = FALSE, verbose = TRUE, ...)

Arguments

fs.model	Object containing results from fs.stability
X	A scaled matrix or dataframe containing numeric values of each feature
Y	A factor vector containing group membership of samples
method	A vector listing models to be fit. Available options are "plsda" (Partial Least Squares Discriminant Analysis), "rf" (Random Forest), "gbm" (Gradient Boosting Machine), "svm" (Support Vector Machines), "glmnet" (Elastic-net Generalized Linear Model), and "pam" (Prediction Analysis of Microarrays)
sig.level	Desired significance level for features, default sig.level = .05
nperm	Number of permutations, default nperm = 10
allowParallel	Logical argument dictating if parallel processing is allowed via foreach package. Default allowParallel = FALSE
verbose	Logical argument whether output printed automatically in 'pretty' format.
...	Extra arguments that the user would like to apply to the models

Value

sig.level	User-specified significance level
num.sig.features	Number of significant features
sig.features	Dataframe of significant features

Author(s)

Charles Determan Jr.

References

Wongravee K., et. al. (2009) Monte-Carlo methods for determining optimal number of significant variables. Application to mouse urinary profiles. Metabolomics 5:387-406.

Examples

dat.discr <- create.discr.matrix(
    create.corr.matrix(
        create.random.matrix(nvar = 50, 
                             nsamp = 100, 
                             st.dev = 1, 
                             perturb = 0.2)),
    D = 10
)

vars <- dat.discr$discr.mat
groups <- dat.discr$classes

fits <- fs.stability(vars, 
                     groups, 
                     method = c("plsda", "rf"), 
                     f = 10, 
                     k = 3, 
                     k.folds = 10, 
                     verbose = 'none')

# permute variables/features
perm.features(fits, vars, groups, "rf",
              sig.level = .05, nperm = 10)

cdeterman/OmicsMarkeR documentation built on May 13, 2019, 2:35 p.m.