Function reference • uqsa

Model and data

Functions for loading model and data and construct a mathematical model (ODE or stochastic) and corresponding code.

uqsa_example(): Load an example model for this package

model_from_tsv(): model_from_tsv loads the content from a series of tsv files

as_ode(): Interpret a model as an ODE

as_cme(): Interprets the provided model as a stochastic model

write_and_compile(): Writes code to file and compiles

values(): Find values in a data.frame that is derived from a tsv file or similar

experiments(): Extract Measured Data and Simulation Experiment Instructions

standard_error_matrix(): Standard Error Matrix from an errors object

conservation_law_analysis(): Reduce the size of the system

kinetic_law_matrix(): Split Kinetic Law

parameter_stoichiometry(): Find the stoichiometry for a given parameter name

parameterConversion(): Returns information about parameter conversion

replace_powers(): replace_powers

stoichiometry(): This function returns a list of named stoichiometric vectors

stoichiometric_matrix(): The stoichiometric matrix of a reaction network

unit.from.string(): Unit Interpreter

unit.id(): Converts a unit to a string that works as an identifier

unit.info(): Prints an interpretation string of a unit

unit_as_character(): converts a unit data.frame into a printable string

units_from_table(): Get units from a data.frame column

yJacobian(): Jacobian of string-math

`c_path<-`(): Add information about the model's C code

c_path(): Retrieve information about the model's C code

generate_code(): Construct Code

print(<cme>): Print a Summary about the CME model

print(<ode>): Print a summary about the ode

shlib(): Compile C code to shared library

`so_path<-`(): Add information about compiled code

so_path(): Retrieve information about compiled code

write_c_code(): Write the C code to a file

Simulations

Functions for simulating the model

simulator.c(): This creates a closure that simulates the model

simstoch(): Simulate stochastic model

simfi(): This creates a closure that simulates the model, similar to simulator.c

scrnn(): scrnn returns a closure around gsl_odeiv2_CRNN()

gsl_odeiv2_fi(): simulates an ode model with extra work

gsl_odeiv2_CRNN(): simulates a CRNN ode model with extra work

name_method(): Reverse look-up of method name from key

Prior

Functions for constructing priors

rNormalPrior(): rNormalPrior returns a random vector generator

rUniformPrior(): rUniformPrior returns a random vector generator

rCopulaPrior(): rCopulaPrior returns a function that generates random values from the copula model

dNormalPrior(): dNormalPrior creates the density function of a multivariate normal distribution with independent components

dUniformPrior(): dUniformPrior creates a uniform density function

dCopulaPrior(): dCopulaPrior creates a prior probability density function

gNormalPrior(): gNormalPrior creates the gradient function of a multivariate normal distribution with independent components, in log-space

fitCopula(): Makes a Probability Density Estimate (from a sample)

Uncertainty quantification - likelihood based

MCMC methods for Bayesian parameter inference

mcmc(): Markov Chain Monte Carlo

mcmc_init(): Initialize the Markov chain

mcmc_mpi(): The MPI version of the mcmc function

metropolis_update(): Metropolis Update is an MCMC update function

high_level_metropolis(): High Level Metropolis function

smmala_update(): SMMALA Update is an MCMC update function

high_level_smmala(): High Level SMMALA function

logLikelihoodFunc(): Default log-likelihood function

ll(): Default Log-likelihood Function

gllf(): Default gradient-log-likelihood Function

fi(): Default gradient-Log-likelihood Function

tune_step_size(): Find a good Step-Size for a given MCMC Algorithm

change_temperature(): Should 2 Markov chains exchange their temperatures

loadSample_mpi(): This function merges mpi-samples into one

gatherSample(): gatherSample collects all sample points, from all files, with the given temperature

gatherReplicas(): Collect statistical Replicas

Uncertainty quantification - ABC

Approximate Bayesian Computation (ABC) MCMC for parameter inference

makeObjective(): creates Objective functions from ingredients

preCalibration(): Determine a starting value for ABC's delta

ABCMCMC(): Performs and Approximate Bayesian Computation Sampling of Model Parameters

ABCSMC(): Performs and Approximate Bayesian Computation as a Particle Filter

checkFitWithPreviousExperiments(): ABC acceptance of currently sampled values given old data (Prior)

Global Sensitivity Analysis

gsa_binning(): Global Sensitivity Analysis

gsa_saltelli(): Outputs the global sensitivity scores SI and SIT, calculated by the Sobol-Homma-Saltelli method

saltelli_prior(): Sample for the Sobol-Homma-Saltelli Global Sensitivity Analysis

Parameter mappings

Parameter transformations from sampling space back to model space

logParMap(): NATURAL LOG parameter mapping used by the MCMC module

logParMapJac(): NATURAL LOG parameter mapping, jacobian

log10ParMap(): LOG10 parameter mapping used by the MCMC module

log10ParMapJac(): LOG10 parameter mapping, jacobian

log2ParMap(): LOG2 parameter mapping used by the MCMC module

log2ParMapJac(): LOG2 parameter mapping, jacobian

Helper Functions and Operators

`%@%`: Fetch an Attribute

`%as%`: %as% is a binary operator on strings with units in them

`%has%`: checks whether a variable has the named attributes

`%otherwise%`: This function can be used to specify default values

determinePrefix(): Determine a prefix from a character vector str of similar contents

`modify<-`(): Modifies a value

`base<-`(): Convert to linear space

column(): Get j-th column with names

print(<experiments>): prints the simulation experiments

print(<mcmcVariable>): print information about the mcmc variable

print(<simulation>): prints the simulation results

Plotting functions

pcDist(): plots a sample in parallel coordinates

plot_time_series_base(): plot function for experiments

sensitivity.graph(): plot the sensitivity matrix

showPosterior(): showPosterior makes a pairs plot for a sample

Internal functions

ggplot_time_series(): Plot time series simulations with experimental data

ggplot_time_series_states(): Plot time series simulation with state variables

onlyCoefficients(): Returns a list of reaction coefficients

onlyNames(): Returns only the names in a reaction formula

CRNN(): CRNN creates C code for a chemical reaction neural network

clear_yacas_environment(): Clear Yacas variables

defaultDistance(): default distance function for one experiment

formulae(): Find a column that contains some kind of mathematic expression in a data.frame

initialCount(): Initial count of reacting Compounds

linear_scale(): Interprets a character vector as names of logarithms

moleCountConversion(): This function calculates the conversion from moles to particle counts

parse_concise(): Read Concise Error Notation

`reaction<-`(): Add a Reaction to an ODE

update_values(): Updates the named values of vector v with values mentioned in data.frame d

simple.unit(): Simple unit from string

unit.kind(): find unit category

unit.scale(): Unit scale from SI prefix