IrmGPCM

java.lang.Object
- com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel
- - com.itemanalysis.psychometrics.irt.model.IrmGPCM

All Implemented Interfaces:

ItemResponseModel
```
public class IrmGPCM
extends AbstractItemResponseModel
```
This version of the Generalized Partial Credit Model (GPCM) uses a discrimination parameter and two or more step parameters. For an item with m categories, there are m step parameters with the first step parameter fixed to zero. For k = 1,..., m, Let Zk = sum_{v=1}^k {D*a*(theta-b_v)}, then the probability of a response of k is given by, exp(Zk)/(sum_{c=1}^m {exp(Zc)}). This form of the GPCM is used in Brad Hanson's ICL program and jMetrik. Since 8/17/2014: This class allows the computation of the probability of a response using item parameter values that are not stored in the object. You must use an array of item parameters that has a specific order to the values. The order is iparam[0] = discrimination, iparam[1] = step1 (always fixed to zero), iparam[2] = step 2, iparam[3] = step 3, ... iparam[m+1] = step m.

Field Summary

Fields
Modifier and Type	Field and Description
`protected double`	`D`
`protected double[]`	`proposalStep`
`protected double[]`	`step`
`protected ItemParamPrior[]`	`stepPrior`
`protected double[]`	`stepStdError`

Fields inherited from class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel
groupId, isFixed, maxCategory, maxWeight, minCategory, minWeight, name, ncat, ncatM1, scoreWeight

Constructor Summary

Constructors
Constructor and Description

IrmGPCM(double discrimination, double[] step, double D)
Default constructor

Constructors
Constructor and Description
`IrmGPCM(double discrimination, double[] step, double D)` Default constructor

Method Summary

All Methods Instance Methods Concrete 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 ll, 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)` Not implemented.
`double`	`derivTheta(double theta)` First derivative of item response model with respect to theta.
`double`	`expectedValue(double theta)` Computes the expected value using parameters stored in the object.
`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.
`double`	`getGuessing()` Gets the pseudo-guessing (i.e.
`double`	`getGuessingStdError()` Gets the guessing parameter estimate standard error.
`double[]`	`getItemParameterArray()`
`int`	`getNumberOfEstimatedParameters()` Does not count the first step because it is fixed to zero
`int`	`getNumberOfParameters()` Counts the first step which is always 0
`double`	`getProposalDifficulty()` A proposal difficulty value is obtained during each iteration of the estimation routine.
`double`	`getScalingConstant()`
`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 k, double D)` Gradient of item response model with respect to (wrt) item parameters.
`double[]`	`gradient(double theta, int category)` Gradient of item response model with respect to (wrt) item parameters.
`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.
`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 category, double D)` Computes the probability of responding in category k using item parameters passed to the method using the iparam argument.
`double`	`probability(double theta, int category)` Computes probability of a response using parameters stored in the object.
`void`	`scale(double intercept, double slope)` Computes a linear transformation of item parameters.
`void`	`setDifficulty(double difficulty)` Set difficulty parameter to an existing value.
`void`	`setDifficultyPrior(ItemParamPrior difficultyPrior)`
`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`	`setGuessing(double guessing)` Set lower asymptote parameter to an existing value.
`void`	`setGuessingPrior(ItemParamPrior guessingPrior)`
`void`	`setGuessingStdError(double stdError)` The guessing parameter standard error may be computed external to the class.
`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`	`setSlipping(double slipping)` Set upper asymptote parameter to an existing value.
`void`	`setSlippingPrior(ItemParamPrior slippingPrior)`
`void`	`setSlippingStdError(double slipping)` 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 k)`
`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.
`java.lang.String`	`toString()` Displays the item parameter values and standard errors.
`double`	`tSharpExpectedValue(double theta, double intercept, double slope)` Computes the expected value using parameters stored in the object.
`double`	`tSharpProbability(double theta, int category, double intercept, double slope)` Returns the probability of a response with a linear transformation of the parameters.
`double`	`tStarExpectedValue(double theta, double intercept, double slope)` Computes the expected value using parameters stored in the object.
`double`	`tStarProbability(double theta, int category, double intercept, double slope)` Returns the probability of a response with a linear transformation of the parameters.

Methods inherited from class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel
defaultScoreWeights, getGroupId, getItemFitStatistic, getItemScoring, getMaxScoreWeight, getMinScoreWeight, getName, getNcat, getScoreWeights, isFixed, setFixed, setGroupId, setItemFitStatistic, setItemScoring, setName, setScoreWeights

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - D
```
protected double D
```
  - step
```
protected double[] step
```
  - proposalStep
```
protected double[] proposalStep
```
  - stepStdError
```
protected double[] stepStdError
```
  - stepPrior
```
protected ItemParamPrior[] stepPrior
```
- Constructor Detail
  - IrmGPCM
```
public IrmGPCM(double discrimination,
               double[] step,
               double D)
```
    Default constructor
    
    Parameters:
    
    discrimination - item discrimination parameters
    
    step - an array of m step parameters. The first step parameter should be fixed to 0.
    
    D - scaling constant tha is either 1 or 1.7 (or 1.712)
- Method Detail
  - probability
```
public double probability(double theta,
                          double[] iparam,
                          int category,
                          double D)
```
    Computes the probability of responding in category k using item parameters passed to the method using the iparam argument. It does NOT use the item parameters stored in the object.
    
    Parameters:
    
    theta - person ability parameter
    
    iparam - an array of all item parameters. The order is [0] discrimination parameter, [1:length] array of step parameters.
    
    category - response category for which probability is sought.
    
    D - scaling constant tha is either 1 or 1.7
    
    Returns:
    
    probability of a response
  - probability
```
public double probability(double theta,
                          int category)
```
    Computes probability of a response using parameters stored in the object.
    
    Parameters:
    
    theta - person ability parameter
    
    category - response category for which probability is sought.
    
    Returns:
    
    probability of a response
  - expectedValue
```
public double expectedValue(double theta)
```
    Computes the expected value using parameters stored in the object.
    
    Parameters:
    
    theta - person ability value
    
    Returns:
    
    expected item response
  - cumulativeProbability
```
public double cumulativeProbability(double theta,
                                    int category)
```
    Not implemented. Only implemented for the graded response model.
    
    Parameters:
    
    theta - a person ability value
    
    category - response category
    
    Returns:
  - gradient
```
public double[] gradient(double theta,
                         int category)
```
    Gradient of item response model with respect to (wrt) item parameters. The response categories are indexed k = 0, 1, 2, ..., m. This method computes the gradientAt using values using item parameter values stored in the object.
    
    Parameters:
    
    theta - person ability value
    
    category - category for which the gradientAt is sought.
    
    Returns:
    
    gradientAt of response model wrt item parameters
  - gradient
```
public double[] gradient(double theta,
                         double[] iparam,
                         int k,
                         double D)
```
    Gradient of item response model with respect to (wrt) item parameters. The response categories are indexed k = 0, 1, 2, ..., m. This method computes the gradientAt using values passed in the iparam argument. It does NOT use the stored item parameter values.
    
    Parameters:
    
    theta - person ability value
    
    iparam - array of item parameters. The order is iparam[0] = discrimination, iparam[1] = step1 (fixed to zero), iparam[2] = step 2, iparam[3] = step 3, ..., iparam[m+1] = step m.
    
    k - zero based index of the response category i.e. k = 0, 1, 2, ..., m.
    
    D - scaling constant that is either 1 or 1.7
    
    Returns:
    
    gradientAt of response model wrt item parameters
  - derivTheta
```
public double derivTheta(double theta)
```
    First derivative of item response model with respect to theta.
    
    Parameters:
    
    theta - a person ability value.
    
    Returns:
    
    first derivative
  - itemInformationAt
```
public double itemInformationAt(double theta)
```
    Description copied from interface: ItemResponseModel
    
    Computes the item information function at theta.
    
    Parameters:
    
    theta - person ability value.
    
    Returns:
    
    item information at theta.
  - nonZeroPrior
```
public double[] nonZeroPrior(double[] param)
```
    Description copied from interface: ItemResponseModel
    
    If the prior density for a parameter is zero, adjust parameter to the nearest non zero value.
  - setDiscriminationPrior
```
public void setDiscriminationPrior(ItemParamPrior prior)
```
  - setStepPriorAt
```
public void setStepPriorAt(ItemParamPrior prior,
                           int k)
```
  - setDifficultyPrior
```
public void setDifficultyPrior(ItemParamPrior difficultyPrior)
```
  - setGuessingPrior
```
public void setGuessingPrior(ItemParamPrior guessingPrior)
```
  - setSlippingPrior
```
public void setSlippingPrior(ItemParamPrior slippingPrior)
```
  - addPriorsToLogLikelihood
```
public double addPriorsToLogLikelihood(double ll,
                                       double[] iparam)
```
    Description copied from interface: ItemResponseModel
    
    Adds prior probabilities to the loglikelihood. This method is used in {@link com.itemanalysis.psychometrics.irt.estimation.MarginalMaximumLikelihoodEstimation
  - addPriorsToLogLikelihoodGradient
```
public double[] addPriorsToLogLikelihoodGradient(double[] loglikegrad,
                                                 double[] iparam)
```
    Description copied from interface: ItemResponseModel
    
    Adds log-prior probabilities to the item loglikelihood. This method is used in MarginalMaximumLikelihoodEstimation.
  - incrementMeanSigma
```
public void incrementMeanSigma(org.apache.commons.math3.stat.descriptive.moment.Mean mean,
                               org.apache.commons.math3.stat.descriptive.moment.StandardDeviation sd)
```
    Description copied from interface: ItemResponseModel
    
    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
```
public void incrementMeanMean(org.apache.commons.math3.stat.descriptive.moment.Mean meanDiscrimination,
                              org.apache.commons.math3.stat.descriptive.moment.Mean meanDifficulty)
```
    Description copied from interface: ItemResponseModel
    
    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.
  - scale
```
public void scale(double intercept,
                  double slope)
```
    Computes a linear transformation of item parameters.
    
    Parameters:
    
    intercept - intercept transformation coefficient.
    
    slope - slope transformation coefficient.
  - tStarProbability
```
public double tStarProbability(double theta,
                               int category,
                               double intercept,
                               double slope)
```
    Returns the probability of a response with a linear transformation of the parameters. This transformation is such that Form X (New Form) is transformed to the scale of Form Y (Old Form). It implements the backwards (New to Old) transformation as described in Kim and Kolen.
    
    Parameters:
    
    theta - examinee proficiency parameter
    
    category - item response
    
    intercept - intercept coefficient of linear transformation
    
    slope - slope (i.e. scale) parameter of the linear transformation
    
    Returns:
    
    probability of a response at values of linearly transformed item parameters
  - tStarExpectedValue
```
public double tStarExpectedValue(double theta,
                                 double intercept,
                                 double slope)
```
    Computes the expected value using parameters stored in the object. This expected value uses a linear transformation of the Form X (New Form) values to the Form Y (Old Form) scale.
    
    Parameters:
    
    theta - person ability value.
    
    intercept - intercept linking coefficient.
    
    slope - slope linking coefficient.
    
    Returns:
    
    expected value of a response
  - tSharpProbability
```
public double tSharpProbability(double theta,
                                int category,
                                double intercept,
                                double slope)
```
    Returns the probability of a response with a linear transformation of the parameters. This transformation is such that Form Y (Old Form) is transformed to the scale of Form X (New Form). It implements the forward (Old to New) transformation as described in Kim and Kolen.
    
    Parameters:
    
    theta - examinee proficiency value
    
    category - item response
    
    intercept - linking coefficient for intercept
    
    slope - linking coefficient for slope
    
    Returns:
    
    probability of a response at values of linearly transformed item parameters
  - tSharpExpectedValue
```
public double tSharpExpectedValue(double theta,
                                  double intercept,
                                  double slope)
```
    Computes the expected value using parameters stored in the object. This expected value uses a linear transformation of the Form Y (Old Form) values to the Form X (New Form) scale.
    
    Parameters:
    
    theta - examinee proficiency value
    
    intercept - linking coefficient for intercept
    
    slope - linking coefficient for slope
    
    Returns:
  - getItemParameterArray
```
public double[] getItemParameterArray()
```
  - setStandardErrors
```
public void setStandardErrors(double[] x)
```
  - getType
```
public IrmType getType()
```
    Description copied from interface: ItemResponseModel
    
    Gets the type of item response model.
    
    Returns:
    
    type of item response model.
  - getNumberOfParameters
```
public int getNumberOfParameters()
```
    Counts the first step which is always 0
    
    Returns:
  - getNumberOfEstimatedParameters
```
public int getNumberOfEstimatedParameters()
```
    Does not count the first step because it is fixed to zero
    
    Returns:
  - getScalingConstant
```
public double getScalingConstant()
```
  - getDifficulty
```
public double getDifficulty()
```
    Description copied from interface: ItemResponseModel
    
    Gets the item difficulty parameter.
    
    Returns:
    
    item difficulty.
  - setDifficulty
```
public void setDifficulty(double difficulty)
```
    Description copied from interface: ItemResponseModel
    
    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 ItemResponseModel.setProposalDifficulty(double).
  - getProposalDifficulty
```
public double getProposalDifficulty()
```
    Description copied from interface: ItemResponseModel
    
    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
```
public void setProposalDifficulty(double difficulty)
```
    Description copied from interface: ItemResponseModel
    
    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
```
public double getDifficultyStdError()
```
    Description copied from interface: ItemResponseModel
    
    Gets the item difficulty standard error.
    
    Returns:
    
    item difficulty standard error.
  - setDifficultyStdError
```
public void setDifficultyStdError(double stdError)
```
    Description copied from interface: ItemResponseModel
    
    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.
  - getDiscrimination
```
public double getDiscrimination()
```
    Description copied from interface: ItemResponseModel
    
    Gets item discrimination.
    
    Returns:
    
    item discrimination.
  - setDiscrimination
```
public void setDiscrimination(double discrimination)
```
    Description copied from interface: ItemResponseModel
    
    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 ItemResponseModel.setProposalDiscrimination(double).
  - setProposalDiscrimination
```
public void setProposalDiscrimination(double discrimination)
```
    Description copied from interface: ItemResponseModel
    
    Set the proposed discrimination estimate.
    
    Parameters:
    
    discrimination - proposed item discrimination value.
  - getDiscriminationStdError
```
public double getDiscriminationStdError()
```
    Description copied from interface: ItemResponseModel
    
    Gets the standard error for the item discrimination estimate.
    
    Returns:
    
    item discrimination standard error.
  - setDiscriminationStdError
```
public void setDiscriminationStdError(double stdError)
```
    Description copied from interface: ItemResponseModel
    
    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
```
public double getGuessing()
```
    Description copied from interface: ItemResponseModel
    
    Gets the pseudo-guessing (i.e. lower asymptote) parameter.
    
    Returns:
    
    guessing parameter.
  - setGuessing
```
public void setGuessing(double guessing)
```
    Description copied from interface: ItemResponseModel
    
    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 ItemResponseModel.setProposalGuessing(double).
  - setProposalGuessing
```
public void setProposalGuessing(double guessing)
```
    Description copied from interface: ItemResponseModel
    
    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
```
public double getGuessingStdError()
```
    Description copied from interface: ItemResponseModel
    
    Gets the guessing parameter estimate standard error.
    
    Returns:
    
    guessing parameter estimate standard error.
  - setGuessingStdError
```
public void setGuessingStdError(double stdError)
```
    Description copied from interface: ItemResponseModel
    
    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.
  - setSlipping
```
public void setSlipping(double slipping)
```
    Description copied from interface: ItemResponseModel
    
    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 ItemResponseModel.setProposalSlipping(double).
  - setProposalSlipping
```
public void setProposalSlipping(double slipping)
```
    Description copied from interface: ItemResponseModel
    
    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.
  - setSlippingStdError
```
public void setSlippingStdError(double slipping)
```
    Description copied from interface: ItemResponseModel
    
    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:
    
    slipping - standard error for the slipping parameter estimate.
  - getSlipping
```
public double getSlipping()
```
    Description copied from interface: ItemResponseModel
    
    Gets the slipping (i.e. upper asymptote) parameter.
    
    Returns:
    
    slipping parameter.
  - getSlippingStdError
```
public double getSlippingStdError()
```
    Description copied from interface: ItemResponseModel
    
    Gets the slipping parameter estimate standard error.
    
    Returns:
    
    slipping parameter estimate standard error.
  - getStepParameters
```
public double[] getStepParameters()
```
    Description copied from interface: ItemResponseModel
    
    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.
  - setStepParameters
```
public void setStepParameters(double[] step)
```
  - setProposalStepParameters
```
public void setProposalStepParameters(double[] step)
```
  - getStepStdError
```
public double[] getStepStdError()
```
    Description copied from interface: ItemResponseModel
    
    Gets that standard errors for each step parameter estimate.
    
    Returns:
    
    an array of step standard errors.
  - setStepStdError
```
public void setStepStdError(double[] stdError)
```
    Description copied from interface: ItemResponseModel
    
    Sets the standard error for the step parameter estimates.
    
    Parameters:
    
    stdError - an array of standard errors for the step parameters.
  - getThresholdParameters
```
public double[] getThresholdParameters()
```
    Description copied from interface: ItemResponseModel
    
    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.
  - setThresholdParameters
```
public void setThresholdParameters(double[] thresholds)
```
    Description copied from interface: ItemResponseModel
    
    Sets the threshold parameters to particular values. These values will be updated during estimateion unless the item is fixed in ItemResponseModel.setFixed(boolean).
    
    Parameters:
    
    thresholds - array of threshold parameters.
  - setProposalThresholds
```
public void setProposalThresholds(double[] thresholds)
```
    Description copied from interface: ItemResponseModel
    
    Sets the proposed threshold parameters estimates to particular values. These values will be updated during estimation unless the item is fixed in ItemResponseModel.setFixed(boolean).
    
    Parameters:
    
    thresholds - array of proposed threshold parameter estimates.
  - getThresholdStdError
```
public double[] getThresholdStdError()
```
    Description copied from interface: ItemResponseModel
    
    Gets the array of standard errors fort eh threshold parameter estimates.
    
    Returns:
    
    array of standard errors.
  - setThresholdStdError
```
public void setThresholdStdError(double[] stdError)
```
    Description copied from interface: ItemResponseModel
    
    Set the threshold standard errors.
    
    Parameters:
    
    stdError - an array of standard errors for the threshold paramter estimates.
  - acceptAllProposalValues
```
public double acceptAllProposalValues()
```
    Description copied from interface: ItemResponseModel
    
    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.
  - toString
```
public java.lang.String toString()
```
    Displays the item parameter values and standard errors.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    String representation of item parameter values and standard errors.

Class IrmGPCM

Field Summary

Fields inherited from class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel

Constructor Summary

Method Summary

Methods inherited from class com.itemanalysis.psychometrics.irt.model.AbstractItemResponseModel

Methods inherited from class java.lang.Object

Field Detail

D

step

proposalStep

stepStdError

stepPrior

Constructor Detail

IrmGPCM

Method Detail

probability

probability

expectedValue

cumulativeProbability

gradient

gradient

derivTheta

itemInformationAt

nonZeroPrior

setDiscriminationPrior

setStepPriorAt

setDifficultyPrior

setGuessingPrior

setSlippingPrior

addPriorsToLogLikelihood

addPriorsToLogLikelihoodGradient

incrementMeanSigma

incrementMeanMean

scale

tStarProbability

tStarExpectedValue

tSharpProbability

tSharpExpectedValue

getItemParameterArray

setStandardErrors

getType

getNumberOfParameters

getNumberOfEstimatedParameters

getScalingConstant

getDifficulty

setDifficulty

getProposalDifficulty

setProposalDifficulty

getDifficultyStdError

setDifficultyStdError

getDiscrimination

setDiscrimination

setProposalDiscrimination

getDiscriminationStdError

setDiscriminationStdError

getGuessing

setGuessing

setProposalGuessing

getGuessingStdError

setGuessingStdError

setSlipping

setProposalSlipping

setSlippingStdError

getSlipping

getSlippingStdError

getStepParameters

setStepParameters

setProposalStepParameters

getStepStdError

setStepStdError

getThresholdParameters

setThresholdParameters

setProposalThresholds

getThresholdStdError

setThresholdStdError

acceptAllProposalValues

toString