ltmCat | R Documentation |
This function fits the latent trait model for binary data and populates the fitted values for discrimination and difficulty parameters to an object of class Cat
.
## S4 method for signature 'data.frame'
ltmCat(data, quadraturePoints = 21, ...)
## S4 method for signature 'ltm'
ltmCat(data, quadraturePoints = NULL, ...)
data |
A data frame of manifest variables or an object of class |
quadraturePoints |
A numeric to be passed into the |
... |
arguments to be passed to methods. For more details about the arguments, see |
The data
argument of the function ltmCat
is either a data frame or an object of class ltm
from the ltm
package. If it is a data frame each row represents a respondent and each column represents a question item. If it is an object of the class ltm
, it is output from the ltm
function in the ltm
package.
The quadraturePoints
argument of the function ltmCat
is used only when the data
argument is a data frame. quadraturePoints
is then passed to the ltm
function from the ltm
package when fitting the latent trait model to the data and is used when approximating the value of integrals.
The function ltmCat
returns an object of class Cat
with changes to the following slots:
difficulty
A vector consisting of difficulty parameters for each item.
discrimination
A vector consisting of discrimination parameters for each item.
model
The string "ltm"
, indicating this Cat
object corresponds to a latent trait model.
See Cat-class
for default values of Cat
object slots. See Examples and setters
for example code to change slot values.
In case the Hessian matrix at convergence is not positive definite try to use start.val = "random"
.
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.
Rizopoulos, Dimitris. 2006. “ltm: An R Package for Latent Variable Modeling and Item Response Theory Analyses." Journal of Statistical Software 17(5):1-25.
Cat-class
, npi
, probability
, tpmCat
## Not run:
## Creating Cat object with raw data
data(npi)
ltm_cat1 <- ltmCat(npi, quadraturePoints = 100)
## Creating Cat object with fitted object of class ltm
ltm_fit <- ltm(npi ~ z1, control = list(GHk = 100)) ## from ltm package
class(ltm_fit)
ltm_cat2 <- ltmCat(ltm_fit)
## Note the two Cat objects are identical
identical(ltm_cat1, ltm_cat2)
## End(Not run)
## Creating Cat objects from large datasets is computationally expensive
## Load the Cat object created from the above code
data(ltm_cat)
## Slots that have changed from default values
getModel(ltm_cat)
getDifficulty(ltm_cat)
getDiscrimination(ltm_cat)
## Changing slots from default values
setEstimation(ltm_cat) <- "MLE"
setSelection(ltm_cat) <- "MFI"
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