pauwels2014 (version 1.0)

add_noise: Noise generative process for the simulations

Description

Specifies a noise generative process for the simulations. This describes how the true dynamics of the system is perturbed by noise.

Usage

add_noise(data_theta_Ts)

Arguments

data_theta_Ts

A time series data matrix, the first column representing time and the remaining columns representing time course of various quantities of interest.

Value

A time series data matrix of the same size as the input.

Details

The default generative process is to add independant gaussian heteoscedastic noise to all columns, except the first one representing time. The noise model is gaussian with variance of the form (0.01 + 0.04 * m^2) where m is the mean.

Examples

data(experiment_list1)
data(observables)

## Generate the knowledge object with correct parameter value
knobj <- generate_our_knowledge(transform_params)

## Generate a time cours matrix
tempCourse <- simulate_experiment(
 knobj$global_parameters$true_params_T, 
 knobj, 
 experiment_list1$nothing
)[
 knobj$global_parameters$tspan %in% observables[["mrnaLow"]]$reso, 
 observables[["mrnaLow"]]$obs
]

## Add noise to the time course matrix
add_noise(tempCourse)