sim_data_scalar
=========================

.. _simdatascalar-label:

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 :ref:`scalar_ge <scalarge-label>` and :ref:`grid_scalar_ge <gridscalarge-label>`.

Usage
------

.. code-block:: python

   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.

.. list-table:: 
   :widths: 30 70
   :header-rows: 1
   :align: center

   * - 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:

.. code-block:: python

   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']