Learn R Programming

immer (version 1.5-13)

immer-package: tools:::Rd_package_title("immer")

Description

tools:::Rd_package_description("immer")

Arguments

Author

tools:::Rd_package_author("immer")

Maintainer: tools:::Rd_package_maintainer("immer")

Details

The immer package has following features:

  • Estimation of the hierarchical rater model (Patz et al., 2002) with immer_hrm and simulation of it with immer_hrm_simulate.

  • The linear logistic partial credit model as an extension to the linear logistic test model (LLTM) for dichotomous data can be estimated with conditional maximum likelihood (Andersen, 1995) using immer_cml.

  • The linear logistic partial credit model can be estimated with composite conditional maximum likelihood (Varin, Reid & Firth, 2011) using the immer_ccml function.

  • The linear logistic partial credit model can be estimated with a bias-corrected joint maximum likelihood method (Bertoli-Barsotti, Lando & Punzo, 2014) using the immer_jml function.

  • Wrapper function immer_FACETS to the commercial program FACETS (Linacre, 1999) for analyzing multi-faceted Rasch models.

  • ...

References

Andersen, E. B. (1995). Polytomous Rasch models and their estimation. In G. H. Fischer & I. W. Molenaar (Eds.). Rasch Models (pp. 39-52). New York: Springer.

Bertoli-Barsotti, L., Lando, T., & Punzo, A. (2014). Estimating a Rasch Model via fuzzy empirical probability functions. In D. Vicari, A. Okada, G. Ragozini & C. Weihs (Eds.). Analysis and Modeling of Complex Data in Behavioral and Social Sciences, Springer.

Linacre, J. M. (1999). FACETS (Version 3.17)[Computer software]. Chicago: MESA.

Patz, R. J., Junker, B. W., Johnson, M. S., & Mariano, L. T. (2002). The hierarchical rater model for rated test items and its application to large-scale educational assessment data. Journal of Educational and Behavioral Statistics, 27(4), 341-384.

Robitzsch, A., & Steinfeld, J. (2018). Item response models for human ratings: Overview, estimation methods, and implementation in R. Psychological Test and Assessment Modeling, 60(1), 101-139.

Varin, C., Reid, N., & Firth, D. (2011). An overview of composite likelihood methods. Statistica Sinica, 21, 5-42.

Wang, W. C., Su, C. M., & Qiu, X. L. (2014). Item response models for local dependence among multiple ratings. Journal of Educational Measurement, 51(3), 260-280.

See Also

For estimating the Rasch multi-facets model with marginal maximum likelihood see also the TAM::tam.mml.mfr and sirt::rm.facets functions.

For estimating the hierarchical rater model based on signal detection theory see sirt::rm.sdt.

For conditional maximum likelihood estimation of linear logistic partial credit models see the eRm (e.g. eRm::LPCM) and the psychotools (e.g. psychotools::pcmodel) packages.

Examples

Run this code
  ##  
  ##  
  ##  immer 0.0-16 (2015-06-27)
  ##  
  ##   
  ##      #@#################################################################@   
  ##     :#@##################################################################`  
  ##     #####################################################################+  
  ##     #####################################################################@  
  ##    .##########@###########################################################` 
  ##    +######@+;'@##########################################################@: 
  ##    ######'     +@########################################################## 
  ##    #####+ ##'  `##########################################################@ 
  ##    ####@ ####  +@#@##################################################@##### 
  ##   .#####.#####@######@#########@#@######@@####@##@############@######@#####`
  ##   :####+:###@:,@##@,;##@+@##@+'###@;.'##@##@##@''@#####@@'+@###,;##@;######.
  ##   ;####@ @##   ##@  ,;    :,    .#    ':    :.    ,@@#'     ';   ,+   @####.
  ##   '#####`:##   #@   : ''    ++   @@;  ` +;    +'   #@' `+@   #@  .    #####,
  ##   +######, #   : ,   @#@   +#@  `##,   @#@   @##  `#@  `##   @#`  +,  @####:
  ##   +#######@@  :+@`  ;##'  `##@  .@#   +##:  `###  .@.  @#+   #:  .##@@#####:
  ##   '########:  ##@   @#@,  ;##;  ;#@   @##`  +##,  +#       `@#`  ##########,
  ##   ;########   @##   ###`  @##`  @#+  `###   @##   ##   ,;'@##@   @#########,
  ##   :#######@  `##+  ,##@   @#@   @#,  '##@   ##@   @@  `#######  `##########.
  ##   .#######+  ,##.  @##+  `##@  .##`  @##;  ,##@  `##   ###; #;  :#@,+###### 
  ##    #######'    #   @##:  ,##;  ;##   @##`  +###   `@:     `;#.  @@:  ###### 
  ##    #######@,`,##.`,###,..###,  #@#..,##@,..####:` +#@'  ``#+#` `@#+ :#####@ 
  ##    #########################:   ,###################@#####@###@#@#########@ 
  ##    '########################@+;+##########################################: 
  ##    ,######################################################################` 
  ##     ##@##################################################################@  

Run the code above in your browser using DataLab