Class Cat

constructor

• new Cat(destructuredParam?: CatInput): Cat

  Create a Cat object. This expects an single object parameter with the following keys

  Parameters

  • destructuredParam: CatInput = {}

    method: ability estimator, e.g. MLE or EAP, default = 'MLE' itemSelect: the method of item selection, e.g. "MFI", "random", "closest", default method = 'MFI' nStartItems: first n trials to keep non-adaptive selection startSelect: rule to select first n trials theta: initial theta estimate minTheta: lower bound of theta maxTheta: higher bound of theta priorDist: the prior distribution type (only applies to EAP estimator) priorPar: the prior distribution parameters (only applies to EAP estimator) randomSeed: set a random seed to trace the simulation

  Returns Cat

itemSelect

maxTheta

method

minTheta

nStartItems

priorDist

priorPar

startSelect

nItems

• get nItems(): number

  Return the number of items that have been observed so far.

  Returns number

prior

• get prior(): [number, number][]

  Returns [number, number][]

resps

• get resps(): (0 | 1)[]

  Returns (0 | 1)[]

seMeasurement

• get seMeasurement(): number

  Returns number

theta

• get theta(): number

  Returns number

zetas

• get zetas(): Zeta[]

  Returns Zeta[]

findNextItem

• findNextItem(
      stimuli: Stimulus[],
      itemSelect?: string,
      deepCopy?: boolean,
  ):
      | { nextStimulus: Stimulus
      | undefined; remainingStimuli: Stimulus[] }
      | {
          nextStimulus: {
              a?: number;
              b?: number;
              c?: number;
              d?: number;
              difficulty?: number;
              discrimination?: number;
              fisherInformation: number;
              guessing?: number;
              slipping?: number;
          };
          remainingStimuli: {
              a?: number;
              b?: number;
              c?: number;
              d?: number;
              difficulty?: number;
              discrimination?: number;
              fisherInformation: number;
              guessing?: number;
              slipping?: number;
          }[];
      }

  find the next available item from an input array of stimuli based on a selection method

  remainingStimuli is sorted by fisher information to reduce the computation complexity for future item selection

  Parameters

  • stimuli: Stimulus[]

    an array of stimulus

  • itemSelect: string = ...

    the item selection method

  • deepCopy: boolean = true

    default deepCopy = true

  Returns
      | { nextStimulus: Stimulus
      | undefined; remainingStimuli: Stimulus[] }
      | {
          nextStimulus: {
              a?: number;
              b?: number;
              c?: number;
              d?: number;
              difficulty?: number;
              discrimination?: number;
              fisherInformation: number;
              guessing?: number;
              slipping?: number;
          };
          remainingStimuli: {
              a?: number;
              b?: number;
              c?: number;
              d?: number;
              difficulty?: number;
              discrimination?: number;
              fisherInformation: number;
              guessing?: number;
              slipping?: number;
          }[];
      }

updateAbilityEstimate

• updateAbilityEstimate(
      zeta: Zeta | Zeta[],
      answer: 0 | 1 | (0 | 1)[],
      method?: string,
  ): void

  use previous response patterns and item params to calculate the estimate ability based on a defined method

  Parameters

  • zeta: Zeta | Zeta[]

    last item param

  • answer: 0 | 1 | (0 | 1)[]

    last response pattern

  • method: string = ...

  Returns void