data.gen: Data Generation

In gesso: Hierarchical GxE Interactions in a Regularized Regression Model

Description

Generates genotypes data matrix G (sample_size by p), vector of environmental measurments E, and an outcome vector Y of size sample_size. Simulates training, validation, and test datasets.

Usage

data.gen(sample_size = 100, p = 20, n_g_non_zero = 15, n_gxe_non_zero = 10, 
         family = "gaussian", mode = "strong_hierarchical", 
         normalize = FALSE, normalize_response = FALSE, 
         seed = 1, pG = 0.2, pE = 0.3,
         n_confounders = NULL)

Arguments

sample_size	sample size of the data
p	total number of main effects
n_g_non_zero	number of non-zero main effects to generate
n_gxe_non_zero	number of non-zero interaction effects to generate
family	"gaussian" for continous outcome Y and "binomial" for binary 0/1 outcome
mode	either "strong_hierarchical", "hierarchical", or "anti_hierarchical". In the strong hierarchical mode the hierarchical structure is maintained (beta_g = 0 then beta_gxe = 0) and also |beta_g| >= |beta_gxe|. In the hierarchical mode the hierarchical structure is maintained, but |beta_G| < |beta_gxe|. In the anti_hierarchical mode the hierarchical structure is violated (beta_g = 0 then beta_gxe != 0).
normalize	TRUE to normalize matrix G and vector E
normalize_response	TRUE to normalize vector Y
pG	genotypes prevalence, value from 0 to 1
pE	environment prevalence, value from 0 to 1
seed	random seed
n_confounders	number of confounders to generate, either NULL or >1

Value

A list of simulated datasets and generating coefficients

G_train, G_valid, G_test	generated genotypes matrices
E_train, E_valid, E_test	generated vectors of environmental values
Y_train, Y_valid, Y_test	generated outcome vectors
C_train, C_valid, C_test	generated confounders matrices
GxE_train, GxE_valid, GxE_test	generated GxE matrix
Beta_G	main effect coefficients vector
Beta_GxE	interaction coefficients vector
beta_0	intercept coefficient value
beta_E	environment coefficient value
Beta_C	confounders coefficient values
index_beta_non_zero, index_beta_gxe_non_zero, index_beta_zero, index_beta_gxe_zero	inner data generation variables
n_g_non_zero	number of non-zero main effects generated
n_gxe_non_zero	number of non-zero interactions generated
n_total_non_zero	total number of non-zero variables
SNR_g	signal-to-noise ratio for the main effects
SNR_gxe	signal-to-noise ratio for the interactions
family, p, sample_size, mode, seed	input simulation parameters

Examples

data = data.gen(sample_size=100, p=100)
G = data$G_train; GxE = data$GxE_train
E = data$E_train; Y = data$Y_train

gesso documentation built on Nov. 30, 2021, 9:09 a.m.