easy_glinternet: Easily build and evaluate a penalized regression model with...
In easyml: Easily Build and Evaluate Machine Learning Models

Description Usage Arguments Value See Also Examples

This function wraps the easyml core framework, allowing a user to easily run the easyml methodology for a glinternet model.

easy_glinternet(.data, dependent_variable, family = "gaussian",
  resample = NULL, preprocess = preprocess_scale, measure = NULL,
  exclude_variables = NULL, categorical_variables = NULL,
  train_size = 0.667, foldid = NULL, survival_rate_cutoff = 0.05,
  n_samples = 1000, n_divisions = 1000, n_iterations = 10,
  random_state = NULL, progress_bar = TRUE, n_core = 1,
  coefficients = FALSE, variable_importances = FALSE, predictions = TRUE,
  model_performance = TRUE, model_args = list())

`.data`	A data.frame; the data to be analyzed.
`dependent_variable`	A character vector of length one; the dependent variable for this analysis.
`family`	A character vector of length one; the type of regression to run on the data. Choices are one of c("gaussian", "binomial"). Defaults to "gaussian".
`resample`	A function; the function for resampling the data. Defaults to NULL.
`preprocess`	A function; the function for preprocessing the data. Defaults to NULL.
`measure`	A function; the function for measuring the results. Defaults to NULL.
`exclude_variables`	A character vector; the variables from the data set to exclude. Defaults to NULL.
`categorical_variables`	A character vector; the variables that are categorical. Defaults to NULL.
`train_size`	A numeric vector of length one; specifies what proportion of the data should be used for the training data set. Defaults to 0.667.
`foldid`	A vector with length equal to `length(y)` which identifies cases belonging to the same fold.
`survival_rate_cutoff`	A numeric vector of length one; for `easy_glmnet`, specifies the minimal threshold (as a percentage) a coefficient must appear out of n_samples. Defaults to 0.05.
`n_samples`	An integer vector of length one; specifies the number of times the coefficients and predictions should be generated. Defaults to 1000.
`n_divisions`	An integer vector of length one; specifies the number of times the data should be divided when replicating the measures of model performance. Defaults to 1000.
`n_iterations`	An integer vector of length one; during each division, specifies the number of times the predictions should be generated. Defaults to 10.
`random_state`	An integer vector of length one; specifies the seed to be used for the analysis. Defaults to NULL.
`progress_bar`	A logical vector of length one; specifies whether to display a progress bar during calculations. Defaults to TRUE.
`n_core`	An integer vector of length one; specifies the number of cores to use for this analysis. Currently only works on Mac OSx and Unix/Linux systems. Defaults to 1.
`coefficients`	A logical vector of length one; whether or not to generate coefficients for this analysis.
`variable_importances`	A logical vector of length one; whether or not to generate variable importances for this analysis.
`predictions`	A logical vector of length one; whether or not to generate predictions for this analysis.
`model_performance`	A logical vector of length one; whether or not to generate measures of model performance for this analysis.
`model_args`	A list; the arguments to be passed to the algorithm specified.

A list of class easy_glinternet.

Other recipes: easy_analysis, easy_avNNet, easy_deep_neural_network, easy_glmnet, easy_neural_network, easy_random_forest, easy_support_vector_machine

## Not run: 
library(easyml) # https://github.com/CCS-Lab/easyml

# Gaussian
data("prostate", package = "easyml")
results <- easy_glinternet(prostate, "lpsa", 
                           n_samples = 10, n_divisions = 10, 
                           n_iterations = 2, random_state = 12345, 
                           n_core = 1)

# Binomial
data("cocaine_dependence", package = "easyml")
results <- easy_glinternet(cocaine_dependence, "diagnosis", 
                           family = "binomial", 
                           exclude_variables = c("subject"), 
                           categorical_variables = c("male"), 
                           preprocess = preprocess_scale, 
                           n_samples = 10, n_divisions = 10, 
                           n_iterations = 2, random_state = 12345, 
                           n_core = 1)

## End(Not run)