# Estimation Methods – catsim.estimation¶

Estimators are the objects responsible for estimating of examinees proficiency values, given a dichotomous (binary) response vector and an array of the items answered by the examinee. In the domain of IRT, there are two main types of ways of estimating $$\hat\theta$$: and these are the Bayesian methods and maximum-likelihood ones.

Maximum-likelihood methods choose the $$\hat\theta$$ value that maximizes the likelihood (see catsim.irt.log_likelihood()) of an examinee having a certain response vector, given the corresponding item parameters.

Bayesian methods used a priori information (usually assuming proficiency and parameter distributions) to make new estimations. The knowledge of new estimations is then used to make new assumptions about the parameter distributions, refining future estimations.

All implemented classes in this module inherit from a base abstract class Estimator. Simulator allows that a custom estimator be used during the simulation, as long as it also inherits from Estimator. catsim implements a few types of maximum-likelihood estimators.

class catsim.estimation.DifferentialEvolutionEstimator(bounds: tuple)[source]

Estimator that uses scipy.optimize.differential_evolution() to minimize the negative log-likelihood function

Parameters: bounds – a tuple containing both lower and upper bounds for the differential evolution algorithm search space. In theory, it is best if they represent the minimum and maximum possible $$\theta$$ values; in practice, one could also use the smallest and largest difficulty parameters in the item bank, in case no better bounds for $$\theta$$ exist.
avg_evaluations

Average number of function evaluations for all tests the estimator has been used

Returns: average number of function evaluations
calls

How many times the estimator has been called to maximize/minimize the log-likelihood function

Returns: number of times the estimator has been called to maximize/minimize the log-likelihood function
estimate(index: int = None, items: numpy.ndarray = None, administered_items: list = None, response_vector: list = None, **kwargs) → float[source]

Uses scipy.optimize.differential_evolution() to return the theta value that minimizes the negative log-likelihood function, given the current state of the test for the given examinee.

Return type: float index (int) – index of the current examinee in the simulator items (ndarray) – a matrix containing item parameters in the format that catsim understands (see: catsim.cat.generate_item_bank()) administered_items (list) – a list containing the indexes of items that were already administered response_vector (list) – a boolean list containing the examinee’s answers to the administered items the current $$\hat\theta$$
evaluations

Total number of times the estimator has evaluated the log-likelihood function during its existence

Returns: number of function evaluations
class catsim.estimation.HillClimbingEstimator(precision: int = 6, dodd: bool = False, verbose: bool = False)[source]

Estimator that uses a hill-climbing algorithm to maximize the likelihood function

Parameters: precision – number of decimal points of precision verbose – verbosity level of the maximization method
avg_evaluations

Average number of function evaluations for all tests the estimator has been used

Returns: average number of function evaluations
calls

How many times the estimator has been called to maximize/minimize the log-likelihood function

Returns: number of times the estimator has been called to maximize/minimize the log-likelihood function
dodd

Whether Dodd’s method will be called by estimator in case the response vector is composed solely of right or wrong answers.

Returns: boolean value indicating if Dodd’s method will be used or not.
estimate(index: int = None, items: numpy.ndarray = None, administered_items: list = None, response_vector: list = None, est_theta: float = None, **kwargs) → float[source]
Returns the theta value that minimizes the negative log-likelihood function, given the current state of the
test for the given examinee.
Return type: float index (int) – index of the current examinee in the simulator items (ndarray) – a matrix containing item parameters in the format that catsim understands (see: catsim.cat.generate_item_bank()) administered_items (list) – a list containing the indexes of items that were already administered response_vector (list) – a boolean list containing the examinee’s answers to the administered items est_theta (float) – a float containing the current estimated proficiency the current $$\hat\theta$$
evaluations

Total number of times the estimator has evaluated the log-likelihood function during its existence

Returns: number of function evaluations

## Comparison between estimators¶

The plots below show a comparison of the different estimator available. Given three dichotomous (binary) response vectors with different numbers of correct answers, all the estimators find values for $$\hat\theta$$ that maximize the log-likelihood function. Some estimators evaluate the log-likelihood less times than others, while reaching similar results, which may make them (although not necessarily) more efficient estimators. Fig. 1 (png, hires.png, pdf) Fig. 2 (png, hires.png, pdf)