ItemResponseModel

All Known Subinterfaces:

ItemResponseModelWithGradient

All Known Implementing Classes:

AbstractItemResponseModel, AbstractItemResponseModelWithGradient, Irm3PL, Irm4PL, IrmBinary, IrmGPCM, IrmGPCM2, IrmGRM, IrmPCM, IrmPCM2
```
public interface ItemResponseModel
```
An item response model describes the relationship between the probability of a score category and person ability (i.e. the latent trait). Unidimensional item response models involve a single latent trait and one or more item parameters. This interface accounts for the methods that all item response models should have. Note that the terms person ability, examinee proficiency, and latent trait are used interchangeably. They are synonyms.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`double`	`acceptAllProposalValues()` Proposal values for every item parameter are obtained at each iteration of the estimation routine.
`double`	`addPriorsToLogLikelihood(double loglike, double[] iparam)` Adds prior probabilities to the loglikelihood.
`double[]`	`addPriorsToLogLikelihoodGradient(double[] loglikegrad, double[] iparam)` Adds log-prior probabilities to the item loglikelihood.
`double`	`cumulativeProbability(double theta, int category)` Mainly here for the graded response model
`double`	`derivTheta(double theta)` Computes the first derivative with respect to person ability.
`double`	`expectedValue(double theta)` For a binary item, `probability(double, int)` and the excpected value returned by this method are the same thing.
`double`	`getDifficulty()` Gets the item difficulty parameter.
`double`	`getDifficultyStdError()` Gets the item difficulty standard error.
`double`	`getDiscrimination()` Gets item discrimination.
`double`	`getDiscriminationStdError()` Gets the standard error for the item discrimination estimate.
`java.lang.String`	`getGroupId()` Gets the item group ID code.
`double`	`getGuessing()` Gets the pseudo-guessing (i.e.
`double`	`getGuessingStdError()` Gets the guessing parameter estimate standard error.
`ItemFitStatistic`	`getItemFitStatistic()`
`double[]`	`getItemParameterArray()`
`ItemScoring`	`getItemScoring()`
`double`	`getMaxScoreWeight()` Gets the largest possible score weight.
`double`	`getMinScoreWeight()` Gets the lowest possible score weight.
`VariableName`	`getName()` Gets the name of the item.
`int`	`getNcat()` Gets the number of response categories.
`int`	`getNumberOfEstimatedParameters()` Number of estimated parameters does not count any values fixed during estimation.
`int`	`getNumberOfParameters()` Gets the number of item parameters in the response model.
`double`	`getProposalDifficulty()` A proposal difficulty value is obtained during each iteration of the estimation routine.
`double`	`getScalingConstant()`
`byte[]`	`getScoreWeights()` Gets an array fo score weights.
`double`	`getSlipping()` Gets the slipping (i.e.
`double`	`getSlippingStdError()` Gets the slipping parameter estimate standard error.
`double[]`	`getStepParameters()` Polytomous item response models may have step parameters.
`double[]`	`getStepStdError()` Gets that standard errors for each step parameter estimate.
`double[]`	`getThresholdParameters()` Polytomous item response models may use an overall item difficulty parameter and two or more threshold parameters.
`double[]`	`getThresholdStdError()` Gets the array of standard errors fort eh threshold parameter estimates.
`IrmType`	`getType()` Gets the type of item response model.
`double[]`	`gradient(double theta, double[] iparam, int category, double D)` Computes the gradientAt (vector of first partial derivatives) with respect to the item parameters.
`double[]`	`gradient(double theta, int category)` Computes the gradientAt at theta.
`void`	`incrementMeanMean(org.apache.commons.math3.stat.descriptive.moment.Mean meanDiscrimination, org.apache.commons.math3.stat.descriptive.moment.Mean meanDifficulty)` Mean/mean linking coefficients are computed from teh mean item difficulty and mean item discrimination.
`void`	`incrementMeanSigma(org.apache.commons.math3.stat.descriptive.moment.Mean mean, org.apache.commons.math3.stat.descriptive.moment.StandardDeviation sd)` Mean/sigma linking coefficients are computed from teh mean and standard deviation of item difficulty.
`boolean`	`isFixed()` A fixed item will use its initial values as the item parameters and no further estimation or update will be applied to the item parameters.
`double`	`itemInformationAt(double theta)` Computes the item information function at theta.
`double[]`	`nonZeroPrior(double[] param)` If the prior density for a parameter is zero, adjust parameter to the nearest non zero value.
`double`	`probability(double theta, double[] iparam, int response, double D)` Computes the probability of a response using item parameter values passed in iparam.
`double`	`probability(double theta, int response)` Computes the probability of response.
`void`	`scale(double intercept, double slope)` Performs a linear transformation of item parameters and standard errors.
`void`	`setDifficulty(double difficulty)` Set difficulty parameter to an existing value.
`void`	`setDifficultyPrior(ItemParamPrior prior)`
`void`	`setDifficultyStdError(double StdError)` Item difficulty standard error may be computed external to the class.
`void`	`setDiscrimination(double discrimination)` Set discrimination parameter to an existing value.
`void`	`setDiscriminationPrior(ItemParamPrior prior)`
`void`	`setDiscriminationStdError(double StdError)` The standard error may be computed external to the class.
`void`	`setFixed(boolean isFixed)` A fixed item will use its initial values as the item parameters and no further estimation or update will be applied to the item parameters.
`void`	`setGroupId(java.lang.String groupId)` An item may be assigned to a group of items.
`void`	`setGuessing(double guessing)` Set lower asymptote parameter to an existing value.
`void`	`setGuessingPrior(ItemParamPrior prior)`
`void`	`setGuessingStdError(double StdError)` The guessing parameter standard error may be computed external to the class.
`void`	`setItemFitStatistic(ItemFitStatistic fitStatistic)`
`void`	`setItemScoring(ItemScoring itemScoring)`
`void`	`setName(VariableName name)` Sets the name of the item.
`void`	`setProposalDifficulty(double difficulty)` A proposal difficulty value is obtained during each iteration of the estimation routine.
`void`	`setProposalDiscrimination(double discrimination)` Set the proposed discrimination estimate.
`void`	`setProposalGuessing(double guessing)` A proposal guessing parameter value is obtained during each iteration of the estimation routine.
`void`	`setProposalSlipping(double slipping)` A proposal slipping parameter value is obtained during each iteration of the estimation routine.
`void`	`setProposalStepParameters(double[] step)`
`void`	`setProposalThresholds(double[] thresholds)` Sets the proposed threshold parameters estimates to particular values.
`void`	`setScoreWeights(double[] scoreWeight)` A polytomous item is scored with two or more ordinal categories such as 0, 1, 2, 3 or 1, 2, 3, 4, 5.
`void`	`setSlipping(double slipping)` Set upper asymptote parameter to an existing value.
`void`	`setSlippingPrior(ItemParamPrior prior)`
`void`	`setSlippingStdError(double StdError)` The slipping parameter standard error may be computed external to the class.
`void`	`setStandardErrors(double[] x)`
`void`	`setStepParameters(double[] step)`
`void`	`setStepPriorAt(ItemParamPrior prior, int index)`
`void`	`setStepStdError(double[] stdError)` Sets the standard error for the step parameter estimates.
`void`	`setThresholdParameters(double[] thresholds)` Sets the threshold parameters to particular values.
`void`	`setThresholdStdError(double[] stdError)` Set the threshold standard errors.
`double`	`tSharpExpectedValue(double theta, double intercept, double slope)` Computes probability of a response under a linear transformation.
`double`	`tSharpProbability(double theta, int response, double intercept, double slope)` Computes probability of a response under a linear transformation.
`double`	`tStarExpectedValue(double theta, double intercept, double slope)` Computes item expected value under a linear transformation.
`double`	`tStarProbability(double theta, int response, double intercept, double slope)` Computes probability of a response under a linear transformation.

- Method Detail
  - probability
```
double probability(double theta,
                   int response)
```
    Computes the probability of response. It ranges between 0 and 1.
    
    Parameters:
    
    theta - a person ability value.
    
    response - an item response (i.e. a person's score on an item).
    
    Returns:
    
    the probability of the response.
  - probability
```
double probability(double theta,
                   double[] iparam,
                   int response,
                   double D)
```
    Computes the probability of a response using item parameter values passed in iparam. It does NOT use the item parameters stored in the object.
    
    Parameters:
    
    theta - person ability parameter.
    
    iparam - array of item parameters. The order is important and will be unique to each implementation of the interface.
    
    response - an item response category.
    
    D - a sclaing constant that is either 1 or 1.7.
    
    Returns:
    
    probability of a response.
  - expectedValue
```
double expectedValue(double theta)
```
    For a binary item, probability(double, int) and the excpected value returned by this method are the same thing. For a polytomous item, the expected value ranges from the minimum possible item score and the maximum possible item score.
    
    Parameters:
    
    theta - a person ability value.
    
    Returns:
    
    a person's expected score on the item.
  - cumulativeProbability
```
double cumulativeProbability(double theta,
                             int category)
```
    Mainly here for the graded response model
    
    Parameters:
    
    theta - a person ability value
    
    category - response category
    
    Returns:
    
    the probability of responding at or above the given category
  - setDiscriminationPrior
```
void setDiscriminationPrior(ItemParamPrior prior)
```
  - setDifficultyPrior
```
void setDifficultyPrior(ItemParamPrior prior)
```
  - setGuessingPrior
```
void setGuessingPrior(ItemParamPrior prior)
```
  - setSlippingPrior
```
void setSlippingPrior(ItemParamPrior prior)
```
  - setStepPriorAt
```
void setStepPriorAt(ItemParamPrior prior,
                    int index)
```
  - setItemScoring
```
void setItemScoring(ItemScoring itemScoring)
```
  - nonZeroPrior
```
double[] nonZeroPrior(double[] param)
```
    If the prior density for a parameter is zero, adjust parameter to the nearest non zero value.
  - getItemScoring
```
ItemScoring getItemScoring()
```
  - scale
```
void scale(double intercept,
           double slope)
```
    Performs a linear transformation of item parameters and standard errors.
    
    Parameters:
    
    intercept - intercept transformation coefficient.
    
    slope - slope transformation coefficient.
  - addPriorsToLogLikelihood
```
double addPriorsToLogLikelihood(double loglike,
                                double[] iparam)
```
    Adds prior probabilities to the loglikelihood. This method is used in {@link com.itemanalysis.psychometrics.irt.estimation.MarginalMaximumLikelihoodEstimation
    
    Parameters:
    
    ll - loglikelihood value
  - gradient
```
double[] gradient(double theta,
                  int category)
```
    Computes the gradientAt at theta. This method uses the item parameters stored in the object.
    
    Parameters:
    
    theta - person ability value
    
    Returns:
  - gradient
```
double[] gradient(double theta,
                  double[] iparam,
                  int category,
                  double D)
```
    Computes the gradientAt (vector of first partial derivatives) with respect to the item parameters. This method uses item parameters passed to teh method. It does NOT use item parameters stored in the object. Note: The second argument (int k) is not actually used by this class. It is here to satisfy the interface.
    
    Parameters:
    
    theta - person ability estimate.
    
    iparam - array of item parameters.
    
    category - response category
    
    D - scaling constant that is either 1 or 1.7
    
    Returns:
    
    an array of first partial derivatives (i.e. the gradientAt).
  - addPriorsToLogLikelihoodGradient
```
double[] addPriorsToLogLikelihoodGradient(double[] loglikegrad,
                                          double[] iparam)
```
    Adds log-prior probabilities to the item loglikelihood. This method is used in MarginalMaximumLikelihoodEstimation.
    
    Parameters:
    
    loglikegrad -
  - getNumberOfParameters
```
int getNumberOfParameters()
```
    Gets the number of item parameters in the response model.
    
    Returns:
    
    number of item parameters.
  - getNumberOfEstimatedParameters
```
int getNumberOfEstimatedParameters()
```
    Number of estimated parameters does not count any values fixed during estimation.
    
    Returns:
  - getItemParameterArray
```
double[] getItemParameterArray()
```
  - setStandardErrors
```
void setStandardErrors(double[] x)
```
  - getNcat
```
int getNcat()
```
    Gets the number of response categories. This value is 2 for binary items and greater than 2 for polytomous items.
    
    Returns:
    
    number of response categories.
  - getScalingConstant
```
double getScalingConstant()
```
  - isFixed
```
boolean isFixed()
```
    A fixed item will use its initial values as the item parameters and no further estimation or update will be applied to the item parameters. This method checks whether the item has been set as a fixed item.
    
    Returns:
  - setFixed
```
void setFixed(boolean isFixed)
```
    A fixed item will use its initial values as the item parameters and no further estimation or update will be applied to the item parameters. This method is how the item is set to fixed status or not.
    
    Parameters:
    
    isFixed -
  - getDifficulty
```
double getDifficulty()
```
    Gets the item difficulty parameter.
    
    Returns:
    
    item difficulty.
  - setDifficulty
```
void setDifficulty(double difficulty)
```
    Set difficulty parameter to an existing value. If you are using this method to fix an item parameter during estimation, you must also set the proposal value in setProposalDifficulty(double).
  - getProposalDifficulty
```
double getProposalDifficulty()
```
    A proposal difficulty value is obtained during each iteration of the estimation routine. This method gets the proposal item difficulty values. This method is needed for estimating item difficulty.
    
    Returns:
    
    proposed item difficulty value.
  - setProposalDifficulty
```
void setProposalDifficulty(double difficulty)
```
    A proposal difficulty value is obtained during each iteration of the estimation routine. This method sets the proposal value.
    
    Parameters:
    
    difficulty - proposed item difficulty value.
  - getDifficultyStdError
```
double getDifficultyStdError()
```
    Gets the item difficulty standard error.
    
    Returns:
    
    item difficulty standard error.
  - setDifficultyStdError
```
void setDifficultyStdError(double StdError)
```
    Item difficulty standard error may be computed external to the class. This method sets the difficulty standard error to a computed value.
    
    Parameters:
    
    StdError - item difficulty standard error.
  - acceptAllProposalValues
```
double acceptAllProposalValues()
```
    Proposal values for every item parameter are obtained at each iteration of the estimation routine. The proposal values for each parameters are obtained for each in turn using the estimated values from the previous iteration. For example, a proposal difficulty estimate for itemA is obtained in iteration k+1 using estimates from iteration k. Then, a proposal difficulty estimate for itemB is obtained in iteration k+1 using estimates from iteration k (even though a new estimate exists for itemA). After obtaining proposal values for every item on the test, the proposal values can be accepted as the new parameter estimates. This method must be called to accept the proposal values as the new estimates. Return the largest difference between the old and new parameters.
  - getDiscrimination
```
double getDiscrimination()
```
    Gets item discrimination.
    
    Returns:
    
    item discrimination.
  - setDiscrimination
```
void setDiscrimination(double discrimination)
```
    Set discrimination parameter to an existing value. If you are using this method to fix an item parameter during estimation, you must also set the proposal value with setProposalDiscrimination(double).
  - setProposalDiscrimination
```
void setProposalDiscrimination(double discrimination)
```
    Set the proposed discrimination estimate.
    
    Parameters:
    
    discrimination - proposed item discrimination value.
  - getDiscriminationStdError
```
double getDiscriminationStdError()
```
    Gets the standard error for the item discrimination estimate.
    
    Returns:
    
    item discrimination standard error.
  - setDiscriminationStdError
```
void setDiscriminationStdError(double StdError)
```
    The standard error may be computed external to the class. It can be set to a specific value with this method.
    
    Parameters:
    
    StdError - item discrimination standard error.
  - getGuessing
```
double getGuessing()
```
    Gets the pseudo-guessing (i.e. lower asymptote) parameter.
    
    Returns:
    
    guessing parameter.
  - setGuessing
```
void setGuessing(double guessing)
```
    Set lower asymptote parameter to an existing value. If you are using this method to fix an item parameter during estimation, you must also set the proposal value in setProposalGuessing(double).
  - setProposalGuessing
```
void setProposalGuessing(double guessing)
```
    A proposal guessing parameter value is obtained during each iteration of the estimation routine. This method sets the proposal value.
    
    Parameters:
    
    guessing - proposed guessing parameter estimate.
  - getGuessingStdError
```
double getGuessingStdError()
```
    Gets the guessing parameter estimate standard error.
    
    Returns:
    
    guessing parameter estimate standard error.
  - setGuessingStdError
```
void setGuessingStdError(double StdError)
```
    The guessing parameter standard error may be computed external to the class. Use this method to set the standard error to a particular value.
    
    Parameters:
    
    StdError - standard error for the guessing parameter estimate.
  - getSlipping
```
double getSlipping()
```
    Gets the slipping (i.e. upper asymptote) parameter.
    
    Returns:
    
    slipping parameter.
  - setSlipping
```
void setSlipping(double slipping)
```
    Set upper asymptote parameter to an existing value. If you are using this method to fix an item parameter during estimation, you must also set the proposal value in setProposalSlipping(double).
  - setProposalSlipping
```
void setProposalSlipping(double slipping)
```
    A proposal slipping parameter value is obtained during each iteration of the estimation routine. This method sets the proposal value.
    
    Parameters:
    
    slipping - proposed slipping parameter estimate.
  - getSlippingStdError
```
double getSlippingStdError()
```
    Gets the slipping parameter estimate standard error.
    
    Returns:
    
    slipping parameter estimate standard error.
  - setSlippingStdError
```
void setSlippingStdError(double StdError)
```
    The slipping parameter standard error may be computed external to the class. Use this method to set the standard error to a particular value.
    
    Parameters:
    
    StdError - standard error for the slipping parameter estimate.
  - setStepParameters
```
void setStepParameters(double[] step)
```
  - setProposalStepParameters
```
void setProposalStepParameters(double[] step)
```
  - getStepParameters
```
double[] getStepParameters()
```
    Polytomous item response models may have step parameters. Gets the array of step parameters. Model that do not have step parameters must implement an empty method
    
    Returns:
    
    array of step parameters.
  - getThresholdParameters
```
double[] getThresholdParameters()
```
    Polytomous item response models may use an overall item difficulty parameter and two or more threshold parameters. The difficulty and threshold parameters can be combined to produce the step parameters.
    
    Returns:
    
    array of threshold parameters.
  - getStepStdError
```
double[] getStepStdError()
```
    Gets that standard errors for each step parameter estimate.
    
    Returns:
    
    an array of step standard errors.
  - setStepStdError
```
void setStepStdError(double[] stdError)
```
    Sets the standard error for the step parameter estimates.
    
    Parameters:
    
    stdError - an array of standard errors for the step parameters.
  - setThresholdParameters
```
void setThresholdParameters(double[] thresholds)
```
    Sets the threshold parameters to particular values. These values will be updated during estimateion unless the item is fixed in setFixed(boolean).
    
    Parameters:
    
    thresholds - array of threshold parameters.
  - setProposalThresholds
```
void setProposalThresholds(double[] thresholds)
```
    Sets the proposed threshold parameters estimates to particular values. These values will be updated during estimation unless the item is fixed in setFixed(boolean).
    
    Parameters:
    
    thresholds - array of proposed threshold parameter estimates.
  - getThresholdStdError
```
double[] getThresholdStdError()
```
    Gets the array of standard errors fort eh threshold parameter estimates.
    
    Returns:
    
    array of standard errors.
  - setThresholdStdError
```
void setThresholdStdError(double[] stdError)
```
    Set the threshold standard errors.
    
    Parameters:
    
    stdError - an array of standard errors for the threshold paramter estimates.
  - setScoreWeights
```
void setScoreWeights(double[] scoreWeight)
              throws org.apache.commons.math3.exception.DimensionMismatchException
```
    A polytomous item is scored with two or more ordinal categories such as 0, 1, 2, 3 or 1, 2, 3, 4, 5. The score weights are not the same as the array index of the threshold or step parameter estimates. They are weights applied to each category. The score weights are set with this method. If no score weights are set, then the default score wieghts are typically 0, 1, 2, and so on.
    
    Parameters:
    
    scoreWeight - an array of category score weights.
    
    Throws:
    
    org.apache.commons.math3.exception.DimensionMismatchException
  - setName
```
void setName(VariableName name)
```
    Sets the name of the item.
    
    Parameters:
    
    name - an item name
  - getName
```
VariableName getName()
```
    Gets the name of the item.
    
    Returns:
    
    item name.
  - setGroupId
```
void setGroupId(java.lang.String groupId)
```
    An item may be assigned to a group of items. The group membership code is the group ID. This method sets the groupId for this item.
    
    Parameters:
    
    groupId - the group ID.
  - getGroupId
```
java.lang.String getGroupId()
```
    Gets the item group ID code.
    
    Returns:
    
    item group ID.
  - getMinScoreWeight
```
double getMinScoreWeight()
```
    Gets the lowest possible score weight.
    
    Returns:
    
    minimum score weight.
  - getMaxScoreWeight
```
double getMaxScoreWeight()
```
    Gets the largest possible score weight.
    
    Returns:
    
    maximum score weight.
  - getScoreWeights
```
byte[] getScoreWeights()
```
    Gets an array fo score weights.
    
    Returns:
  - setItemFitStatistic
```
void setItemFitStatistic(ItemFitStatistic fitStatistic)
```
  - getItemFitStatistic
```
ItemFitStatistic getItemFitStatistic()
```
  - derivTheta
```
double derivTheta(double theta)
```
    Computes the first derivative with respect to person ability.
    
    Parameters:
    
    theta - a person ability value.
    
    Returns:
    
    first derivative wrt theta.
  - itemInformationAt
```
double itemInformationAt(double theta)
```
    Computes the item information function at theta.
    
    Parameters:
    
    theta - person ability value.
    
    Returns:
    
    item information at theta.
  - tStarProbability
```
double tStarProbability(double theta,
                        int response,
                        double intercept,
                        double slope)
```
    Computes probability of a response under a linear transformation. This method is mainly used for the characteristic curve linking methods (see StockingLordMethod). It applies the linear transformation such that the New form is transformed to the Old Form.
    
    Parameters:
    
    theta - examinee proficiency value
    
    response - target category
    
    intercept - linking coefficient for intercept
    
    slope - linking coefficient for slope
    
    Returns:
    
    probability of a response using transformed values.
  - tSharpProbability
```
double tSharpProbability(double theta,
                         int response,
                         double intercept,
                         double slope)
```
    Computes probability of a response under a linear transformation. This method is mainly used for the characteristic curve linking methods (see StockingLordMethod). It applies the linear transformation such that the Old form is transformed to the New Form.
    
    Parameters:
    
    theta - examinee proficiency value
    
    response - target category
    
    intercept - linking coefficient for intercept
    
    slope - linking coefficient for slope
    
    Returns:
    
    probability of a response using transformed values.
  - tStarExpectedValue
```
double tStarExpectedValue(double theta,
                          double intercept,
                          double slope)
```
    Computes item expected value under a linear transformation. This method is mainly used for the characteristic curve linking methods (see StockingLordMethod). It applies the linear transformation such that the New form is transformed to the Old Form.
    
    Parameters:
    
    theta - person ability value
    
    intercept - intercept linking coefficient.
    
    slope - slope linking coefficient.
    
    Returns:
    
    expected value under a linear transformation.
  - tSharpExpectedValue
```
double tSharpExpectedValue(double theta,
                           double intercept,
                           double slope)
```
    Computes probability of a response under a linear transformation. This method is mainly used for the characteristic curve linking methods (see StockingLordMethod). It applies the linear transformation such that the Old form is transformed to the New Form.
    
    Parameters:
    
    theta - examinee proficiency value
    
    intercept - linking coefficient for intercept
    
    slope - linking coefficient for slope
    
    Returns:
    
    expected value under a linear transformation.
  - incrementMeanSigma
```
void incrementMeanSigma(org.apache.commons.math3.stat.descriptive.moment.Mean mean,
                        org.apache.commons.math3.stat.descriptive.moment.StandardDeviation sd)
```
    Mean/sigma linking coefficients are computed from teh mean and standard deviation of item difficulty. The summary statistics are computed in a storeless manner. This method allows for teh incremental update to item difficulty summary statistics by combining them with other summary statistics.
    
    Parameters:
    
    mean - item difficulty mean.
    
    sd - item difficulty standard deviation.
  - incrementMeanMean
```
void incrementMeanMean(org.apache.commons.math3.stat.descriptive.moment.Mean meanDiscrimination,
                       org.apache.commons.math3.stat.descriptive.moment.Mean meanDifficulty)
```
    Mean/mean linking coefficients are computed from teh mean item difficulty and mean item discrimination. The summary statistics are computed in a storeless manner. This method allows for teh incremental update to item difficulty summary statistics by combining them with other summary statistics.
    
    Parameters:
    
    meanDiscrimination - item discrimination mean.
    
    meanDifficulty - item difficulty mean.
  - getType
```
IrmType getType()
```
    Gets the type of item response model.
    
    Returns:
    
    type of item response model.

Interface ItemResponseModel

Method Summary

Method Detail

probability

probability

expectedValue

cumulativeProbability

setDiscriminationPrior

setDifficultyPrior

setGuessingPrior

setSlippingPrior

setStepPriorAt

setItemScoring

nonZeroPrior

getItemScoring

scale

addPriorsToLogLikelihood

gradient

gradient

addPriorsToLogLikelihoodGradient

getNumberOfParameters

getNumberOfEstimatedParameters

getItemParameterArray

setStandardErrors

getNcat

getScalingConstant

isFixed

setFixed

getDifficulty

setDifficulty

getProposalDifficulty

setProposalDifficulty

getDifficultyStdError

setDifficultyStdError

acceptAllProposalValues

getDiscrimination

setDiscrimination

setProposalDiscrimination

getDiscriminationStdError

setDiscriminationStdError

getGuessing

setGuessing

setProposalGuessing

getGuessingStdError

setGuessingStdError

getSlipping

setSlipping

setProposalSlipping

getSlippingStdError

setSlippingStdError

setStepParameters

setProposalStepParameters

getStepParameters

getThresholdParameters

getStepStdError

setStepStdError

setThresholdParameters

setProposalThresholds

getThresholdStdError

setThresholdStdError

setScoreWeights

setName

getName

setGroupId

getGroupId

getMinScoreWeight

getMaxScoreWeight

getScoreWeights

setItemFitStatistic

getItemFitStatistic

derivTheta

itemInformationAt

tStarProbability

tSharpProbability

tStarExpectedValue

tSharpExpectedValue

incrementMeanSigma

incrementMeanMean

getType