socr.model interface

This is the model object where all the computation , data manipulation and storage of data happens. All the data is stored in the private variables. socr.model is defined in the object literal pattern and the function returns all publicly available methods and properties.

Private Variables

_dataset={}

This object contains the initial dataset created by the user (both incase of simulation and datadriven mode).For example, when the user generates 2 datasets from the binomial coin toss experiment, _dataset ={0:{"index":"0", "keys":"H,T,H,H,H", "values":"1,0,1,1,1", "name":null}, 1:{"index":"1", "keys":"H,T,T,H,T", "values":"1,0,0,1,0", "name":null}}

_bootstrapGroupKeys

This object contains all the random samples generated by the user. Every group has random samples from all the initial group of datasets.

_bootstrapGroupValues

Why there are keys and values? Its because in some form of data input (like coin toss), the "key" contains the symbolic meaningful reference whereas the "value" contains the mathematical equivalent value.

Structure of both bootstrapGroupKeys and Values `{

                    "0": [ [..],[..],[..],..] ,
		"1": [ [..],[..],[..],..] ,
		 ..
	}`

Private Methods

_getRandomInt(min,max)

Returns a random number in the range [min,max]

_generateMean(sampleNumber,K)

sampleNumber is the random sample Number. For example, if the user has generated 1000 random samples, then 0<sampleNumber<1000. K is the number of dataset groups in the initial dataset.

Returns the calculated mean for the supplied sample number and K.

_generateCount(sampleNumber,K)

Returns the calculated total count value for the sampleNumber and K provided.

_generateStandardDev(sampleNumber,K)

##Public Properties

##Public Methods

generateTrail

generateSample()

generateStep()

getMean

getMeanOf

getMeanOfDataset

getDataset

setDataset

getSample

getSamples

getStopCount

setStopCount

setN

getN

getRSampleCount

setRSampleCount

reset

resetVariables

getK