fit_enorm_mst | 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.

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