fit_enorm_mst {dexterMST} | R Documentation |
Fits an Extended NOminal Response Model (ENORM) using conditional maximum likelihood (CML) or a Gibbs sampler for Bayesian estimation; both adapted for MST data
fit_enorm_mst(
db,
predicate = NULL,
fixed_parameters = NULL,
method = c("CML", "Bayes"),
nDraws = 1000
)
db |
an dextermst db handle |
predicate |
logical predicate to select data to include in the analysis, see details |
fixed_parameters |
data.frame with columns 'item_id', 'item_score' and 'beta' |
method |
If CML, the estimation method will be Conditional Maximum Likelihood. If Bayes, a Gibbs sampler will be used to produce a sample from the posterior. |
nDraws |
Number of Gibbs samples when estimation method is Bayes. |
You can use the predicate to include or omit responses from the analysis, e.g. ‘p = fit_enorm_mst(db, item_id != ’some_item' & student_birthdate > '2005-01-01')'
DexterMST will automatically correct the routing rules for the purpose of the current analysis.
There are some caveats though. Predicates that lead to many different designs, e.g. a predicate like
response != 'NA'
(which is perfectly valid but can potentially create
almost as many tests as there are students) might take very long to compute.
Predicates that remove complete modules from a test, e.g. module_nbr !=2
or module_id != 'RU4'
will cause an error and should be avoided.
object of type 'mst_enorm'. Can be cast to a data.frame of item parameters
using function ‘coef' or used in dexter’s ability
functions
Zwitser, R. J. and Maris, G (2015). Conditional statistical inference with multistage testing designs. Psychometrika. Vol. 80, no. 1, 65-84.
Koops, J. and Bechger, T. and Maris, G. (in press); Bayesian inference for multistage and other incomplete designs. In Research for Practical Issues and Solutions in Computerized Multistage Testing. Routledge, London.