OmicsMarkeR (version 1.4.2)

fit.only.model: Fit Models without Feature Selection

Description

Applies models to high-dimensional data for classification.

Usage

fit.only.model(X, Y, method, p = 0.9, optimize = TRUE, tuning.grid = NULL, k.folds = if (optimize) 10 else NULL, repeats = if (optimize) 3 else NULL, resolution = if (optimize) 3 else NULL, metric = "Accuracy", 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)

p

Percent of data to by 'trained'

optimize

Logical argument determining if each model should be optimized. Default "optimize = TRUE"

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. 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)

allowParallel

Logical argument dictating if parallel processing is allowed via foreach package. Default allowParallel = FALSE

verbose

Logical argument if should output progress

...

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

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

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

fit <- fit.only.model(X=vars, 
                      Y=groups, 
                      method="plsda", 
                      p = 0.9)