estimateSE | R Documentation |
Estimates the standard error for a respondent's ability parameter estimate, θ.
estimateSE(catObj)
catObj |
An object of class |
The function estimateSE
estimates the standard error of the ability estimate
given the estimation approach of the Cat
object, specified in estimation
slot of Cat
object.
The expected a posteriori approach is used when estimation
slot is "EAP"
. This method involves integration. See Note for more information.
The modal a posteriori approach is used when estimation
slot is "MAP"
. This method is only available using the normal prior distribution.
The maximum likelihood approach is used when estimation
slot is "MLE"
. When the likelihood
of the ability estimate is undefined,
the MAP or EAP method will be used, determined by what is specified in the estimationDefault
slot in Cat
object.
The weighted maximum likelihood approach is used when estimation
slot is "WLE"
.
Estimating θ requires root finding with the “Brent” method in the GNU Scientific
Library (GSL) with initial search interval of [-5,5]
.
The function estimateSE
returns a numeric for the standard error for θ.
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.
This function uses adaptive quadrature methods from the GNU Scientific
Library (GSL) to approximate single-dimensional
integrals with high accuracy. The bounds of integration are determined by the
lowerBound
and upperBound
slots of the Cat
object.
Haley Acevedo, Ryden Butler, Josh W. Cutler, Matt Malis, Jacob M. Montgomery, Tom Wilkinson, Erin Rossiter, Min Hee Seo, Alex Weil
estimateTheta
## Loading ltm Cat object data(ltm_cat) ## Store example answers setAnswers(ltm_cat) <- c(1,0,1,0,1, rep(NA, 35)) ## Set different estimation procedures and calculate ability estimate and its standard error setEstimation(ltm_cat) <- "EAP" estimateTheta(ltm_cat) estimateSE(ltm_cat) setEstimation(ltm_cat) <- "MAP" estimateTheta(ltm_cat) estimateSE(ltm_cat) setEstimation(ltm_cat) <- "MLE" estimateTheta(ltm_cat) estimateSE(ltm_cat) setEstimation(ltm_cat) <- "WLE" estimateTheta(ltm_cat) estimateSE(ltm_cat)
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