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 glmnet model.
1 2 3 4 5 6 7 | 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())
|
.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 |
survival_rate_cutoff |
A numeric vector of length one; for |
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_glmnet
.
Other recipes: easy_analysis
,
easy_avNNet
,
easy_deep_neural_network
,
easy_glinternet
,
easy_neural_network
,
easy_random_forest
,
easy_support_vector_machine
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## 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))
## End(Not run)
|
Loaded easyml 0.1.0. Also loading ggplot2.
Loading required namespace: ggplot2
[1] "Generating coefficients from multiple model builds:"
[1] "Generating predictions for a single train test split:"
[1] "Generating measures of model performance over multiple train test splits:"
[1] "Generating coefficients from multiple model builds:"
[1] "Generating predictions for a single train test split:"
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
[1] "Generating measures of model performance over multiple train test splits:"
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Warning messages:
1: from glmnet Fortran code (error code -99); Convergence for 99th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
2: from glmnet Fortran code (error code -92); Convergence for 92th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
3: from glmnet Fortran code (error code -90); Convergence for 90th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
4: from glmnet Fortran code (error code -92); Convergence for 92th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
5: from glmnet Fortran code (error code -92); Convergence for 92th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
6: from glmnet Fortran code (error code -90); Convergence for 90th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
7: from glmnet Fortran code (error code -92); Convergence for 92th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
8: from glmnet Fortran code (error code -92); Convergence for 92th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
9: from glmnet Fortran code (error code -95); Convergence for 95th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
10: from glmnet Fortran code (error code -92); Convergence for 92th lambda value not reached after maxit=100000 iterations; solutions for larger lambdas returned
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