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GENetLib is a Python library for gene–environment interaction analysis via deep learning.

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sim_data_scalar

Description

This function is used to generate the example data for functions scalar_ge and grid_scalar_ge. Users can customize the outcomes using the parameter shown in the parameter table below.

See also at scalar_ge and grid_scalar_ge.

Usage

sim_data_scalar(rho_G, rho_E, dim_G, dim_E, n, dim_E_Sparse=0, ytype='Survival', n_inter=None, linear=True, seed=0)

Parameters

This part shows the meanings and data types of parameters. Users can check the table below to customize the simulation data.

Parameter

Description

rho_G

numeric, correlation of gene variables.

rho_E

numeric, correlation of environment variables.

dim_G

numeric, dimension of gene variables.

dim_E

numeric, dimension of environment variables.

n

numeric, sample size.

dim_E_Sparse

numeric, dimension of sparse environment variables.

ytype

character, “Survival”, “Binary” or “Continuous” type of the output y. If not specified, the default is survival.

n_inter

numeric, number of interaction effect variables.

linear

bool, “True” or “False”, whether or not to generate linear data. The default is True.

seed

numeric, random seeds each time when data is generated.

Value

The function sim_data_scalar outputs a dictionary including generated data and the positions of interaction effect variables.

  • y: An array representing the response variable. When the type of output data is “survival”, output y is an n*2 array that consists:

  1. The minimum of the survival time and censoring time.

  2. The event indicator.

  • G: A matrix representing the scalar genetic variables.

  • E: A matrix representing the scalar environmental covariates.

  • GE: A matrix representing the G-E interaction variables.

  • interpos: An array contains the positions of interaction effect variables.

Examples

Here is a quick example for using this function:

from GENetLib.sim_data import sim_data_scalar
scalar_survival_linear = sim_data_scalar(rho_G = 0.25, rho_E = 0.3, dim_G = 500, dim_E = 5, n = 1500, dim_E_Sparse = 2, ytype = 'Survival', n_inter = 30)
scalar_survival_linear_y = scalar_survival_linear['y']
scalar_survival_linear_G = scalar_survival_linear['G']
scalar_survival_linear_E = scalar_survival_linear['E']
scalar_survival_linear_GE = scalar_survival_linear['GE']
scalar_survival_linear_inter = scalar_survival_linear['interpos']

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