Based on the given model, checking whether user speciflied input variables are correct. If the input variables are acceptable, this function will format them and then return them as a list
. Otherwise, this function will return a error message to indicate which variables are unacceptable.
Model |
A character to declare the type of items to be modeled. The parameter labels follow conventional use, can be:
'3PL' - Three parameter logistic (3PL) model proposed by Birnbaum(1968):
P(x = 1|\theta, a, b, c) = c + (1 - c) / (1 + exp(-D * a * (\theta - b)))
where x=1 is the correct response, theta is examinne's ability; a, b and c are the item discrimination, difficulty and guessing parameter, respectively; D is the scaling constant 1.702.
'4PL' - Four parameter logistic (4PL) model proposed by Barton & Lord's(1981). Transfer the unslipping (upper asymptote) parameter d to slipping parameter s by set s=1-d:
P(x = 1|\theta, a, b, c, s) = c + (1 - s - c) / (1 + exp(-D * a * (\theta - b)))
where x=1 is the correct response; theta is examinne's ability. a, b, c and s are the item discrimination, difficulty guessing and slipping parameter, respectively; D is the scaling constant 1.702.
'1PLG' - One parameter logsitc guessing (1PLG) model proposed by San Martín et al.(2006). Let invlogit(x)=1 / (1 + exp(-x)):
P(x = 1|\theta, \beta, \gamma) = invlogit(\theta - \beta) + (1 - invlogit(\theta - \beta)) * invlogit(\gamma)
where x=1 is the correct response, theta is examinne's ability; beta and gamma are the item difficulty and guessing parameter, respectively.
'1PLAG' - One parameter logsitc ability-based guessing (1PLAG) model proposed by San Martín et al.(2006). Let invlogit(x)=1 / (1 + exp(-x)):
P(x = 1|\theta, \alpha, \beta, \gamma) = invlogit(\theta - \beta) + (1 - invlogit(\theta - \beta)) * invlogit(\alpha * \theta + \gamma)
where x=1 is the correct response, theta is examinne's ability; alpha is the weight of the ability in the guessing component; beta and gamma are the item difficulty and guessing parameter, respectively.
These parameter labels are capitalized in program for emphasis.
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data |
A matrix or data.frame consists of dichotomous data (1 for correct and 0 for wrong response), with missing data coded as in Missing (by default, Missing=-9). Each row of data represents a examinne' responses, and each column represents an item.
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PriorA |
The user specified logarithmic normal distribution prior for item discrimation (a) parameters in the 3PL and 4PL models. Can be:
A numeric with two hyperparameters mean and variance of logarithmic normal distribution for all a parameters. By default, PriorA=c(0,0.25), which means a log normal prior of mean=0 and variance=0.25 will be used for all item discrimation parameters.
A NA , refers to no priors will be used, so maximum likelihood estimates for item discrimation parameter will be obtained.
A matrix with two columns, and each row of matrix consists of two hyperparameters of log normal prior (mean and variance) for single item a parameter.
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PriorB |
The user specified normal distribution prior for item difficulty (b) parameters in the 3PL and 4PL models. Can be:
A numeric with two hyperparameters mean and variance of normal distribution for all b parameters. By default, PriorB=c(0,4), which means a normal prior of mean=0 and variance=4 will be used for all item difficulty parameters.
A NA , refers to no priors will be used, so maximum likelihood estimates for item difficulty parameter will be obtained.
A matrix with two columns, and each row of matrix consists of two hyperparameters of normal prior (mean and variance) for single item b parameter.
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PriorC |
The user specified Beta(x,y) distribution prior for item guessing (c) parameters in the 3PL and 4PL models. Can be:
A numeric with two hyperparameters x and y of Beta distribution for all c parameters. By default, PriorC=c(4,16), which means a Beta prior of mean=4/(4+16)=0.2 and variance=0.008 will be used for all item guessing parameters.
A NA , refers to no priors will be used, so maximum likelihood estimates for item guessing parameter will be obtained.
A matrix with two columns, and each row of matrix consists of two hyperparameters of Beta prior (x and y) for single item c parameter.
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PriorS |
The user specified Beta(x,y) distribution prior for item slipping (s) parameters in the 4PL model. Can be:
A numeric with two hyperparameters x and y of Beta distribution for all s parameters. By default, PriorS=c(4,16), which means a Beta prior of mean=4/(4+16)=0.2 and variance=0.008 will be used for all item slipping parameters.
A NA , refers to no priors will be used, so maximum likelihood estimates for item slipping parameter will be obtained.
A matrix with two columns, and each row of matrix consists of two hyperparameters of Beta prior (x and y) for single item s parameter.
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PriorAlpha |
The user specified normal distribution prior for the logarithmic weight of the ability in the guessing component (ln(alpha)) parameter in the 1PLAG model. Can be:
A numeric with two hyperparameters normal distribution for all log(alpha) parameters. By default, PriorAlpha=c(-1.9,1), which means a Normal prior of mean=-1.9 and variance=1 will be used for the logarithmic weight of the ability.
A NA , refers to no priors will be used, so maximum likelihood estimates for the weight of the ability will be obtained.
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PriorBeta |
The user specified normal distribution prior for item difficulty (beta) parameters in the 1PLAG and 1PLG model. Can be:
A numeric with two hyperparameters mean and variance of normal distribution for all beta parameters. By default, PriorBeta=c(0,4), which means a normal prior of mean=0 and variance=4 will be used for all item difficulty parameters.
A NA , refers to no priors will be used, so maximum likelihood estimates for item difficulty parameter will be obtained.
A matrix with two columns, and each row of matrix consists of two hyperparameters of normal prior (mean and variance) for single item beta parameter.
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PriorGamma |
The user specified normal distribution prior for item guessing (gamma) parameters in the 1PLAG and 1PLG model. Can be:
A numeric with two hyperparameters mean and variance of normal distribution for all gamma parameters. By default, PriorGamma=c(-1.39,0.25), which means a normal prior of mean=-1.39 and variance=0.25 will be used for all item guessing parameters.
A NA , refers to no priors will be used, so maximum likelihood estimates for item guessing parameter will be obtained.
A matrix with two columns, and each row of matrix consists of two hyperparameters of normal prior (mean and variance) for single item gamma parameter.
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InitialA |
The user specified starting values for item discrimation (a) parameters in the 3PL and 4PL models. Can be:
A NA (default), refers to no specified starting values for a parameter.
A single number (numeric ), refers to set this number to be the starting values of a for all items.
A numeric consists of starting values for each a parameter.
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InitialB |
The user specified starting values for item difficulty (b) parameters in the 3PL and 4PL models. Can be:
A NA (default), refers to no specified starting values for b parameter.
A single number (numeric ), refers to set this number to be the starting values of b for all items.
A numeric consists of starting values for each b parameter.
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InitialC |
The user specified starting values for item guessing (c) parameters in the 3PL and 4PL models. Can be:
A NA (default), refers to no specified starting values for c parameter.
A single number (numeric ), refers to set this number to be the starting values of c for all items.
A numeric consists of starting values for each c parameter.
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InitialS |
The user specified starting values for item slipping (s) parameters in the 4PL model. Can be:
A NA (default), refers to no specified starting values for s parameter.
A single number (numeric ), refers to set this number to be the starting values of s for all items.
A numeric consists of starting values for each s parameter.
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InitialAlpha |
The user specified starting value for the weight of the ability in the guessing component (alpha) parameters in the 1PLAG model. Can be:
A NA (default), refers to no specified starting values for alpha parameter.
A single number (numeric ), refers to set this number to be the starting value of alpha.
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InitialBeta |
The user specified starting values for item difficulty (beta) parameters in the 1PLAG and 1PLG models. Can be:
A NA (default), refers to no specified starting values for beta parameter.
A single number (numeric ), refers to set this number to be the starting values of beta for all items.
A numeric consists of starting values for each beta parameter.
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InitialGamma |
The user specified starting values for item guessing (gamma) parameters in the 1PLAG and 1PLG models. Can be:
A NA (default), refers to no specified starting values for gamma parameter.
A single number (numeric ), refers to set this number to be the starting values of gamma for all items.
A numeric consists of starting values for each gamma parameter.
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Tol |
A single number (numeric ), refers to convergence threshold for E-step cycles; defaults are 0.0001.
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max.ECycle |
A single integer , refers to maximum number of E-step cycles; defaults are 2000L.
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max.MCycle |
A single integer , refers to maximum number of M-step cycles; defaults are 100L.
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n.Quadpts |
A single integer , refers to number of quadrature points per dimension (must be larger than 5); defaults are 31L.
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n.decimal |
A single integer , refers to number of decimal places when outputs results.
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Theta.lim |
A numeric with two number, refers to the range of integration grid for each dimension; default is c(-6, 6).
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Missing |
A single number (numeric ) to indicate which elements are missing; default is -9. The Missing cannot be 0 or 1.
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ParConstraint |
A logical value to indicate whether estimates parametes in a reasonable range; default is FALSE. If ParConstraint=TRUE: a in [0.001, 6], b in [-6, 6], c in [0.0001, 0.5], s in [0.0001, c], alpha in [0, 0.707], beta in [-6, 6], gamma in [-7, 0].
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BiasSE |
A logical value to determine whether directly estimating SEs from inversed Hession matrix rather than USEM method, default is FALSE.
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Barton, M. A., & Lord, F. M. (1981). An upper asymptote for the three-parameter logistic item response model. ETS Research Report Series, 1981(1), 1-8. doi:10.1002/j.2333-8504.1981.tb01255.x
Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores (pp. 395-479). MA: Adison-Wesley.
San Martín, E., Del Pino, G., & De Boeck, P. (2006). IRT models for ability-based guessing. Applied Psychological Measurement, 30(3), 183-203. doi:10.1177/0146621605282773