IrmPCM

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

All Implemented Interfaces:

ItemResponseModel
```
public class IrmPCM
extends AbstractItemResponseModel
```
This version of the Partial Credit Model (GPCM) uses a difficulty parameter (b), and one or more threshold parameters. For an item with M categories, there are M-1 threshold parameters (t). For k = 2,..., M. Let Zk = sum_{v=2}^k {(theta-b-t_v)} if k>1 and Zk = 0 if k==1. The probability of a response of k is given by, exp(Zk)/(1+sum_{k=2}^M {exp(Zk)}). This form of the PCM is used in WINSTEPS and jMetrik Rasch estimation procedures. Note that it differs from IrmGPCM2 in that IrmPCM does not include a discrimination parameter (i.e. a=1). It also differs in computation of the step parameters. This class uses b_v = (b+t_v), whereas IrmGPCM2 uses b_v = (b-t_v). The partial credit model as used by PARSCALE can be obtained with IrmGPCM2

Field Summary
- 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

IrmPCM(double difficulty, double[] threshold, double D)
Default constructor

Constructors
Constructor and Description
`IrmPCM(double difficulty, double[] threshold, 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)` Partial derivative 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()` 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`	`getProposalDiscrimination()`
`double[]`	`getProposalThresholds()`
`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)` Computes the gradientAt (vector of first partial derivatives) with respect to the item parameters.
`double[]`	`gradient(double theta, int k)` Computes the gradientAt of teh item response model with respect to the 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 a response using item parameter values passed in iparam.
`double`	`probability(double theta, int category)` Computes probability of a response using parameters stored in the object.
`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 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`	`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()`
`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()`
`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()`
`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 the expected value using parameters stored in the object
`double`	`tStarProbability(double theta, int response, double intercept, double slope)` Returns the probability of a response with a linear transformatin 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, setScoreWeights

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

- Constructor Detail
  - IrmPCM
```
public IrmPCM(double difficulty,
              double[] threshold,
              double D)
```
    Default constructor
    
    Parameters:
    
    difficulty - item difficulty parameter
    
    threshold - an array of m-1 threshold parameters for an m category item
    
    D - a scaling constant that is either 1.0, 1.7, or 1.712
- Method Detail
  - setName
```
public void setName(VariableName name)
```
    Description copied from interface: ItemResponseModel
    
    Sets the name of the item.
    
    Specified by:
    
    setName in interface ItemResponseModel
    
    Overrides:
    
    setName in class AbstractItemResponseModel
    
    Parameters:
    
    name - an item name
  - probability
```
public double probability(double theta,
                          double[] iparam,
                          int category,
                          double D)
```
    Description copied from interface: ItemResponseModel
    
    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.
    
    category - an item response category.
    
    D - a sclaing constant that 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 proficiency value
    
    category - category for which the probability of a response is sought.
    
    Returns:
    
    probability of responding in category
  - expectedValue
```
public double expectedValue(double theta)
```
    computes the expected value using parameters stored in the object
    
    Parameters:
    
    theta -
    
    Returns:
  - 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,
                         double[] iparam,
                         int k,
                         double D)
```
    Description copied from interface: ItemResponseModel
    
    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.
    
    k - response category
    
    D - scaling constant that is either 1 or 1.7
    
    Returns:
    
    an array of first partial derivatives (i.e. the gradientAt).
  - gradient
```
public double[] gradient(double theta,
                         int k)
```
    Computes the gradientAt of teh item response model with respect to the item parameters
    
    Parameters:
    
    theta - person ability value
    
    Returns:
    
    gradientAt
  - 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.
  - derivTheta
```
public double derivTheta(double theta)
```
    Partial derivative with respect to theta.
    
    Parameters:
    
    theta - person proficiency value
    
    Returns:
    
    partial derivative at theta
  - 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.
  - 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)
```
    Description copied from interface: ItemResponseModel
    
    Performs a linear transformation of item parameters and standard errors.
    
    Parameters:
    
    intercept - intercept transformation coefficient.
    
    slope - slope transformation coefficient.
  - getNumberOfParameters
```
public int getNumberOfParameters()
```
    Description copied from interface: ItemResponseModel
    
    Gets the number of item parameters in the response model.
    
    Returns:
    
    number of item parameters.
  - getNumberOfEstimatedParameters
```
public int getNumberOfEstimatedParameters()
```
    Description copied from interface: ItemResponseModel
    
    Number of estimated parameters does not count any values fixed during estimation.
    
    Returns:
  - getScalingConstant
```
public double getScalingConstant()
```
  - tStarProbability
```
public double tStarProbability(double theta,
                               int response,
                               double intercept,
                               double slope)
```
    Returns the probability of a response with a linear transformatin 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
    
    response - 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
    
    Parameters:
    
    theta -
    
    intercept - intercept linking coefficient.
    
    slope - slope linking coefficient.
    
    Returns:
  - tSharpProbability
```
public double tSharpProbability(double theta,
                                int response,
                                double intercept,
                                double slope)
```
    Description copied from interface: ItemResponseModel
    
    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.
  - tSharpExpectedValue
```
public double tSharpExpectedValue(double theta,
                                  double intercept,
                                  double slope)
```
    Description copied from interface: ItemResponseModel
    
    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.
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object
  - getType
```
public IrmType getType()
```
    Description copied from interface: ItemResponseModel
    
    Gets the type of item response model.
    
    Returns:
    
    type of item response model.
  - getItemParameterArray
```
public double[] getItemParameterArray()
```
  - setStandardErrors
```
public void setStandardErrors(double[] x)
```
  - 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)
```
  - 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).
  - getProposalDiscrimination
```
public double getProposalDiscrimination()
```
  - 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.
  - 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.
  - getProposalThresholds
```
public double[] getProposalThresholds()
```
  - 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.
  - setStepParameters
```
public void setStepParameters(double[] step)
```
  - setProposalStepParameters
```
public void setProposalStepParameters(double[] step)
```
  - 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()
```
  - setProposalStepParameters
```
public void setProposalStepParameters()
```
  - 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.
  - 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.

Class IrmPCM

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

Constructor Detail

IrmPCM

Method Detail

setName

probability

probability

expectedValue

cumulativeProbability

gradient

gradient

addPriorsToLogLikelihood

addPriorsToLogLikelihoodGradient

derivTheta

itemInformationAt

incrementMeanSigma

incrementMeanMean

scale

getNumberOfParameters

getNumberOfEstimatedParameters

getScalingConstant

tStarProbability

tStarExpectedValue

tSharpProbability

tSharpExpectedValue

toString

getType

getItemParameterArray

setStandardErrors

nonZeroPrior

setDiscriminationPrior

setStepPriorAt

setDifficultyPrior

setGuessingPrior

setSlippingPrior

getDifficulty

setDifficulty

getProposalDifficulty

setProposalDifficulty

getDifficultyStdError

setDifficultyStdError

getDiscrimination

setDiscrimination

getProposalDiscrimination

setProposalDiscrimination

getDiscriminationStdError

setDiscriminationStdError

getGuessing

setGuessing

setProposalGuessing

getGuessingStdError

setGuessingStdError

setSlipping

setProposalSlipping

setSlippingStdError

getSlipping

getSlippingStdError

getThresholdParameters

setThresholdParameters

getProposalThresholds

setProposalThresholds

getThresholdStdError

setThresholdStdError

setStepParameters

setProposalStepParameters

getStepParameters

setStepParameters

setProposalStepParameters

getStepStdError

setStepStdError

acceptAllProposalValues