summary.elastic_net_var_select: Summary function for the output of elastic_net_var_select

In AlexiaJM/LEGIT: Latent Environmental & Genetic InTeraction (LEGIT) Model

Description

Summary function for the output of elastic_net_var_select

Usage

summary.elastic_net_var_select

Arguments

object	An object of class "elastic_net_var_select", usually, a result of a call to elastic_net_var_select.
...	Further arguments passed to or from other methods.

Format

An object of class function of length 1.

Value

Returns the unique IMLEGIT models resulting from the glmnet path with associated information. Also gives the cross-validation information if asked.

References

Alexia Jolicoeur-Martineau, Ashley Wazana, Eszter Szekely, Meir Steiner, Alison S. Fleming, James L. Kennedy, Michael J. Meaney, Celia M.T. Greenwood and the MAVAN team. Alternating optimization for GxE modelling with weighted genetic and environmental scores: examples from the MAVAN study (2017). arXiv:1703.08111.

Examples

## Not run: 
N = 1000
train = example_3way(N, sigma=1, logit=FALSE, seed=7)
g1_bad = rbinom(N,1,.30)
g2_bad = rbinom(N,1,.30)
g3_bad = rbinom(N,1,.30)
g4_bad = rbinom(N,1,.30)
g5_bad = rbinom(N,1,.30)
train$G = cbind(train$G, g1_bad, g2_bad, g3_bad, g4_bad, g5_bad)
lv = list(G=train$G, E=train$E)
fit = elastic_net_var_select(train$data, lv, y ~ G*E)
summary(fit)
best_model(fit, criterion="BIC")
 # Instead of taking the best, if you want the model with "Model index"=17 from summary, do
plot(fit)
# With Cross-validation
fit = elastic_net_var_select(train$data, lv, y ~ G*E, cross_validation=TRUE, cv_iter=1, cv_folds=5)
best_model(fit, criterion="cv_R2")
# Elastic net only applied on G
fit = elastic_net_var_select(train$data, lv, y ~ G*E, c(1))
# Elastic net only applied on E
fit = elastic_net_var_select(train$data, lv, y ~ G*E, c(2))
# Most E variables not removed, use lambda_mult > 1 to remove more
fit = elastic_net_var_select(train$data, lv, y ~ G*E, c(2), lambda_mult=5)
# Lasso (only L1 regularization)
fit = elastic_net_var_select(train$data, lv, y ~ G*E, alpha=1)
# Want more lambdas (useful if # of variables is large)
fit = elastic_net_var_select(train$data, lv, y ~ G*E, n_lambda = 200)

## End(Not run)

AlexiaJM/LEGIT documentation built on Feb. 14, 2021, 8:24 p.m.