Easily build and evaluate a penalized regression model.

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

easy_glmnet(.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 = TRUE, variable_importances = FALSE,
  predictions = TRUE, model_performance = TRUE, model_args = list())

Arguments

.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.

Value

A list of class easy_glmnet.

See also

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

Examples

# NOT RUN {
library(easyml) # https://github.com/CCS-Lab/easyml

# Gaussian
data("prostate", package = "easyml")
results <- easy_glmnet(prostate, "lpsa",
                       n_samples = 10, n_divisions = 10,
                       n_iterations = 2, random_state = 12345,
                       n_core = 1, model_args = list(alpha = 1.0))

# Binomial
data("cocaine_dependence", package = "easyml")
results <- easy_glmnet(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, model_args = list(alpha = 1.0))
# }