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lines 6-75 of file: example/user/zero_meas_value.py
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{xrst_begin user_zero_meas_value.py}
{xrst_spell
  bmi
}

Exclude Measurement Value Covariate Multipliers From Predictions
################################################################

Purpose
*******
The measurement value covariate multipliers are intended to represent
biases in measurements.
The :ref:`predict_command@zero_meas_value` option for the predict command
enables one to exclude these biases during predictions.

no_effect_iota_true
*******************
The only rate in this example is iota.
The value of this rate, before any effects, used to simulate the data is
{xrst_code py}'''
no_effect_iota_true = 1e-2
r'''{xrst_code}

reference_income
****************
Income is a covariate between zero and one and its reference value is 1:
{xrst_code py}'''
reference_income    = 1.0;
r'''{xrst_code}

beta_income_true
****************
The higher income, the more likely that incidence will
be recorded. We model this using a meas_value covariate multiplier
one income. The value of this multiplier used to simulate the data is
{xrst_code py}'''
beta_income_true    = 0.5;
r'''{xrst_code}

reference_bmi
*************
Body mass index, bmi, is a covariate and its reference value is 20:
{xrst_code py}'''
reference_bmi       = 20.0;
r'''{xrst_code}

beta_bmi_true
*************
The high bmi, the more likely that the disease will be contracted.
We model this using a rate_value covariate multiplier on bmi.
The value of this multiplier (used to simulate the data is
{xrst_code py}'''
beta_bmi_true       = 1.0 / 5.0
r'''{xrst_code}

n_data
******
The number of simulated data points for this example is
{xrst_code py}'''
n_data               = 10
r'''{xrst_code}

Source Code
***********
{xrst_literal
   # BEGIN PYTHON
   # END PYTHON
}

{xrst_end user_zero_meas_value.py}