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

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sim_data_func

Description

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

See also at func_ge and grid_scalar_ge.

Usage

sim_data_func(n, m, ytype, input_type = 'SNP', 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

n

numeric, sample size.

m

numeric, the sequence length of each sample.

ytype

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

input_type

character, “SNP” or “func” type of the input gene variables. If not specified, the default is “SNP”.

seed

numeric, random seeds each time when data is generated.

Value

The function sim_data_func outputs a dictionary including response variable y, scalar variable z and sequence (genotypes) data X.

  • y: An array 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.

  • X: A matrix or a list of fd objects.

  1. When input_type = “SNP”, a matrix representing the sequence data, with the number of rows equal to the number of samples.

  1. When input_type = “func”, a list contains functional objects denoted as fd, with the number of rows equal to the number of samples.

  • location: A list defining the sampling sites of the sequence (genotypes) data.

  • Z: A matrix representing the scalar covariates, with the number of rows equal to the number of samples.

Examples

Here is a quick example for using this function:

from GENetLib.sim_data import sim_data_func
func_continuous = sim_data_func(n = 1000, m = 100, ytype = 'Continuous', seed = 1)
X = func_continuous['X']
y = func_continuous['y']
Z = func_continuous['Z']
location = func_continuous['location']

When users want to generate fd objects:

from GENetLib.sim_data import sim_data_func
func_continuous = sim_data_func(n = 1000, m = 100, input_type = 'SNP', ytype = 'Continuous', seed = 1)
X = func_continuous['X']
y = func_continuous['y']
Z = func_continuous['Z']
location = func_continuous['location']

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