fit.only.model: Fit Models without Feature Selection

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

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

Author(s)

Charles Determan Jr

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)

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