compute_bic

chi2

params

data_points

<p>Compute the Bayesian Information Criterion. Assuming additive Gaussian
measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2</p>

Provides an API for analysing repetitive parameter estimations and simulations of
mathematical models. Examples of mathematical models are Ordinary Differential equations (ODEs)
or Stochastic Differential Equations (SDEs) models. Among the analyses for parameter
estimation 'sbpiper' calculates statistics and generates plots for parameter density, PCA of the best
fits, parameter profile likelihood estimations (PLEs), and 2D parameter PLEs. These results can
be generated using all or a subset of the best computed parameter sets. Among the analyses
for model simulation 'sbpiper' calculates statistics and generates plots for deterministic
and stochastic time courses via cartesian and heatmap plots. Plots for the scan of one or two model
parameters can also be generated. This package is primarily used by the software 'SBpipe'.
Citation: Dalle Pezze P, Le Nov<c3><a8>re N. SBpipe: a collection of pipelines for automating
repetitive simulation and analysis tasks. BMC Systems Biology. 2017;11:46. <doi:10.1186/s12918-017-0423-3>.

Piero Dalle Pezze

sbpiper

Data Analysis Functions for 'SBpipe' Package

compute_bic function

Compute the Bayesian Information Criterion. Assuming additive Gaussian
measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2 — compute_bic

Compute the Bayesian Information Criterion. Assuming additive Gaussian
measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2

compute_bic: Compute the Bayesian Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2

Description

Usage

Arguments

Value

Examples