fs.stability: Classification & Feature Selection

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

Description

Applies models to high-dimensional data to both classify and determine important features for classification. The function bootstraps a user-specified number of times to facilitate stability metrics of features selected thereby providing an important metric for biomarker investigations, namely whether the important variables can be identified if the models are refit on 'different' data.

Usage

fs.stability(X, Y, method, k = 10, p = 0.9, f = NULL,
  stability.metric = "jaccard", optimize = TRUE,
  optimize.resample = FALSE, tuning.grid = NULL, k.folds = if (optimize)
  10 else NULL, repeats = if (k.folds == "LOO") NULL else if (optimize) 3 else
  NULL, resolution = if (is.null(tuning.grid) && optimize) 3 else NULL,
  metric = "Accuracy", model.features = FALSE, allowParallel = FALSE,
  verbose = "none", ...)

Arguments

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)
k	Number of bootstrapped interations
p	Percent of data to by 'trained'
f	Number of features desired. If rank correlation is desired, set "f = NULL"
stability.metric	string indicating the type of stability metric. Avialable options are "jaccard" (Jaccard Index/Tanimoto Distance), "sorensen" (Dice-Sorensen's Index), "ochiai" (Ochiai's Index), "pof" (Percent of Overlapping Features), "kuncheva" (Kuncheva's Stability Measures), "spearman" (Spearman Rank Correlation), and "canberra" (Canberra Distance)
optimize	Logical argument determining if each model should be optimized. Default "optimize = TRUE"
optimize.resample	Logical argument determining if each resample should be re-optimized. Default "optimize.resample = FALSE" - Only one optimization run, subsequent models use initially determined parameters
tuning.grid	Optional list of grids containing parameters to optimize for each algorithm. Default "tuning.grid = NULL" lets function create grid determined by "res"
k.folds	Number of folds generated during cross-validation. May optionally be set to "LOO" for leave-one-out cross-validation. Default "k.folds = 10"
repeats	Number of times cross-validation repeated. Default "repeats = 3"
resolution	Resolution of model optimization grid. Default "resolution = 3"
metric	Criteria for model optimization. Available options are "Accuracy" (Predication Accuracy), "Kappa" (Kappa Statistic), and "AUC-ROC" (Area Under the Curve - Receiver Operator Curve)
model.features	Logical argument if should have number of features selected to be determined by the individual model runs. Default "model.features = FALSE"
allowParallel	Logical argument dictating if parallel processing is allowed via foreach package. Default allowParallel = FALSE
verbose	Character argument specifying how much output progress to print. Options are 'none', 'minimal' or 'full'.
...	Extra arguments that the user would like to apply to the models

Value

Methods	Vector of models fit to data
performance	Performance metrics of each model and bootstrap iteration
RPT	Robustness-Performance Trade-Off as defined in Saeys 2008
features	List concerning features determined via each algorithms feature selection criteria.

• metric: Stability metric applied

• features: Matrix of selected features

• stability: Matrix of pairwise comparions and average stability

stability.models	Function perturbation metric - i.e. how similar are the features selected by each model.
original.best.tunes	If "optimize.resample = TRUE" then returns list of optimized parameters for each bootstrap.
final.best.tunes	If "optimize.resample = TRUE" then returns list of optimized parameters for each bootstrap of models refit to selected features.
specs	List with the following elements:

• total.samples: Number of samples in original dataset

• number.features: Number of features in orginal dataset

• number.groups: Number of groups

• group.levels: The specific levels of the groups

• number.observations.group: Number of observations in each group

Author(s)

Charles Determan Jr

References

Saeys Y., Abeel T., et. al. (2008) Machine Learning and Knowledge Discovery in Databases. 313-325. http://link.springer.com/chapter/10.1007/978-3-540-87481-2_21

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')

OmicsMarkeR documentation built on April 28, 2020, 6:54 p.m.