denovo.grid: Denovo Grid Generation

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

Description

Greates grid for optimizing selected models

Usage

denovo.grid(data, method, res)

Arguments

data	data of method to be tuned
method	vector indicating the models to generate grids. 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)
res	Resolution of model optimization grid.

Value

A list containing dataframes of all combinations of parameters for each model:

Author(s)

Charles Determan Jr

See Also

"expand.grid" for generating grids of specific parameters desired. However, NOTE that you must still convert the generated grid to a list.

Examples

# random test data
set.seed(123)
dat.discr <- create.discr.matrix(
    create.corr.matrix(
        create.random.matrix(nvar = 50, 
                             nsamp = 100, 
                             st.dev = 1, 
                             perturb = 0.2)),
    D = 10
)

df <- data.frame(dat.discr$discr.mat, .classes = dat.discr$classes)

# create tuning grid
denovo.grid(df, "gbm", 3)

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