roboliq-processor

Module: stanModel

Function to generate an MCMC model for use with Stan.

Source:

Methods

<inner> addCentralizedParameter_mean(name, withLoc, n, nScales [, nName], limits)

Add variables for a centralized mean

Parameters:
Name Type Argument Description
name string

name of variable
withLoc boolean

whether to include a location data variable
n integer

array size, or 1 for a scalar
nScales integer

number of scale parameters; 1 = same scale for each item in array; n = one scale parameter for each item.
nName string <optional>

optional name for array size (to be used in place of n in some places)
limits string

a limits modifier ("" if no limits)
Source:

<inner> addLiquid(model, k, spec)

Add a liquid to the model.

Parameters:
Name Type Description
model object

the model
k string

liquid name
spec object

concentration specification

Properties
Name Type Argument Default Description
type string <optional>
 "fixed"

set to "fixed" for a concentration, "normal" for a normally distributed concentration. If set to "normal", you will need to supply the "loc" and "scale" values as input data when running Stan.
value number <optional>
 0

if fixed, this is the fixed concentration - otherwise the parameters will be estimated.
Source:

<inner> aspirate(model, args)

Add an aspiration operation to the model.

Parameters:
Name Type Description
model object

the model
args object
Properties
Name Type Description
p string

the name of the pipetting parameters
t number | string

the tip identifier
d number

volume in microliters
well string

the well name
Source:

<inner> assignLiquid(model, well, k)

Assign a liquid to a well in the model.

Parameters:
Name Type Description
model object

the model
well string

the well name
k string

the liquid name
Source:

<inner> createEmptyModel(subclassNodes, betaDs, gammaDs)

Create the initial empty model. You will add pipetting and measurement actions will be added to.

Parameters:
Name Type Description
subclassNodes Array.<number>

sorted volumes after which a subclass starts (e.g. for subclasses 3-15,15.01-500,500.01-1000, subclassNodes = [3,15,500,1000])
betaDs Array.<number>

volumes for which we want a beta parameter (dispense bias)
gammaDs Array.<number>

volumes for which we want a gamma parameter (unintended dilution)
Source:
Returns:

an object with mostly empty properties representing the model's random variables and labware.

Type
object

<inner> dispense(model, args)

Add a dispense operation to the model.

Parameters:
Name Type Description
model object

the model
args object
Properties
Name Type Description
p string

the name of the pipetting parameters
t number | string

the tip identifier
d number

volume in microliters
well string

the well name
Source:

<inner> measureAbsorbance(model, wells)

Add an absorbance measurement to the model.

Parameters:
Name Type Description
model object

the model
wells Array.<string>

the names of the measured wells
Source:

<inner> measureWeight(model, l)

Add a weight measurement to the model.

Parameters:
Name Type Description
model object

the model
l string

the labware name
Source:

<inner> printModel(model [, basename])

Print the Stan model to stdout, and save a basename.R file that holds indexes to associate the random variables back to labware.

Parameters:
Name Type Argument Default Description
model string

the model
basename string <optional>
 "stanModel"

the basename for the R output
Source: