elasticnet: Second Phase of Filtering
In jillbo1000/gwas3: Three Phase Filtering for GWAS

elasticnet uses the genotypes and phenotypes that pass through the first phase of SNP filtering and computes LASSO and elastic net models to further filter the SNPs. Lambda is determined using cross-validation with the LASSO model. That value of lambda is used for all elastic net and LASSO models. A list containing datasets of genotypes are returned for alpha values of 0, 0.005, 0.01, 0.1, 0.25, 0.5, 0.75, 0.9, and 1.0. Note that the model with alpha = 0 will retain all of the SNPs passed into the function. This option is included so the user can easily compare the performance of the final SNP selction methods without the second stage filtering with models that have the additional filtering. The final datasets contain the SNPs that have non-zero coefficients.

1	elasticnet(x, y)

`x`	A matrix or dataframe containing the genotypes that passed through the first phase of filtering.
`y`	A numeric vector containing the phenotypes. The function will determine if the response is binary or continuous.

Returns a list where each member of the list is a dataset that contains the genotypes for the SNPs that have non-zero coefficients and the alpha value used in the elastic net model. The list elements are numbered rather than named to make it easier to use as inputs into other functions. For example, result[[1]]$data contains the dataset and result[[1]]$alpha contains the value of alpha used to compute the model.

jillbo1000/gwas3 documentation built on June 14, 2019, 3:08 a.m.