d1LL | R Documentation |
Calculates either the first derivative of the log-likelihood or the first derivative of the log-posterior evaluated at point θ.
d1LL(catObj, theta, use_prior)
catObj |
An object of class |
theta |
A numeric or an integer indicating the value for θ_j |
use_prior |
A logical indicating whether to calculate based on the log-likelihood or log-posterior |
When the usePrior
argument is FALSE
, the function d1LL
evaluates the first derivative of the log-likelihood at point θ.
When the usePrior
argument is TRUE
, the function d1LL
evaluates the first derivative of the log-posterior at point θ.
If the argument use_prior
is TRUE
, the function d1LL
must use the the normal prior distribution.
The function d1LL
returns a numeric of the derivative of the log-likelihood (or log-posterior) given a respondent's answer profile.
This function is to allow users to access the internal functions of the package. During item selection, all calculations are done in compiled C++
code.
Haley Acevedo, Ryden Butler, Josh W. Cutler, Matt Malis, Jacob M. Montgomery, Tom Wilkinson, Erin Rossiter, Min Hee Seo, Alex Weil
Baker, Frank B. and Seock-Ho Kim. 2004. Item Response Theory: Parameter Estimation Techniques. New York: Marcel Dekker.
Choi, Seung W. and Richard J. Swartz. 2009. “Comparison of CAT Item Selection Criteria for Polytomous Items." Applied Psychological Measurement 33(6):419-440.
Muraki, Eiji. 1992. “A generalized partial credit model: Application of an EM algorithm." ETS Research Report Series 1992(1):1-30.
van der Linden, Wim J. 1998. “Bayesian Item Selection Criteria for Adaptive Testing." Psychometrika 63(2):201-216.
Cat-class
, prior
## Loading ltm Cat object data(ltm_cat) ## Store example answers setAnswers(ltm_cat) <- c(1,0,1,0,1, rep(NA, 35)) ## d1LL for Cat object of the ltm model d1LL(ltm_cat, theta = 1, use_prior = FALSE)
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