Simulated Multifaceted Data

Description

Simulated data from multiple facets.

Usage

 data(data.sim.mfr)
 data(data.sim.facets)

Format

The format of data.sim.mfr is:
num [1:100, 1:5] 3 2 1 1 0 1 0 1 0 0 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:100] "V1" "V1.1" "V1.2" "V1.3" ...
..$ : NULL

The format of data.sim.facets is:
'data.frame': 100 obs. of 3 variables:
$ rater : num 1 2 3 4 5 1 2 3 4 5 ...
$ topic : num 3 1 3 1 3 2 3 2 2 1 ...
$ female: num 2 2 1 2 1 1 2 1 2 1 ...

Source

Simulated

Examples

#######
# sim multi faceted Rasch model
data(data.sim.mfr)
data(data.sim.facets)

  # 1: A-matrix test_rater
  test_1_items <- .A.matrix( data.sim.mfr, formulaA = ~rater, 
            facets = data.sim.facets, constraint = "items" )
  test_1_cases <- .A.matrix( data.sim.mfr, formulaA = ~rater, 
            facets = data.sim.facets, constraint = "cases" )
  
  # 2: test_item+rater
  test_2_cases <- .A.matrix( data.sim.mfr, formulaA = ~item+rater, 
            facets = data.sim.facets, constraint = "cases" )
  
  # 3: test_item+rater+topic+ratertopic
  test_3_items <- .A.matrix( data.sim.mfr, formulaA = ~item+rater*topic, 
            facets = data.sim.facets, constraint = "items" )
  # conquest uses a different way of ordering the rows
  # these are the first few rows of the conquest design matrix
  # test_3_items$A[grep("item1([[:print:]])*topic1", rownames(test_3_items)),] 
  
  # 4: test_item+step
  test_4_cases <- .A.matrix( data.sim.mfr, formulaA = ~item+step, 
            facets = data.sim.facets, constraint = "cases" )

  # 5: test_item+item:step
  test_5_cases <- .A.matrix( data.sim.mfr, formulaA = ~item+item:step, 
            facets = data.sim.facets, constraint = "cases" )
  test_5_cases$A[, grep("item1", colnames(test_5_cases)) ]

  # 5+x: more
  #  => 6: is this even well defined in the conquest-design output
  #          (see test_item+topicstep_cases.cqc / .des)
  #        regardless of the meaning of such a formula; 
  #        currently .A.matrix throws a warning
  # test_6_cases <- .A.matrix( data.sim.mfr, formulaA = ~item+topic:step, 
  #                 facets = data.sim.facets, constraint = "cases" )
  test_7_cases <- .A.matrix( data.sim.mfr, formulaA = ~item+topic+topic:step, 
            facets = data.sim.facets, constraint = "cases" )

## Not run: 
  # => 8: same as with 6
  test_8_cases <- .A.matrix( data.sim.mfr, formulaA = ~item+rater+item:rater:step, 
            facets = data.sim.facets, constraint = "cases" )
## [1] "Can't proceed the estimation: Lower-order term is missing."            
  test_9_cases <- .A.matrix( data.sim.mfr, formulaA = ~item+step+rater+item:step+item:rater, 
            facets = data.sim.facets, constraint = "cases" )
  test_10_cases <- .A.matrix( data.sim.mfr, formulaA = ~item+female+item:female, 
            facets = data.sim.facets, constraint = "cases" )

  ### All Design matrices
  test_1_cases <- designMatrices.mfr( data.sim.mfr, formulaA = ~rater, 
            facets = data.sim.facets, constraint = "cases" )
  test_4_cases <- designMatrices.mfr( data.sim.mfr, formulaA = ~item+item:step, 
            facets = data.sim.facets, constraint = "cases" )
  
  ### TAM
  test_4_cases <- tam.mml.mfr( data.sim.mfr, formulaA = ~item+item:step )
  test_tam <- tam.mml( data.sim.mfr )

  test_1_cases <- tam.mml.mfr( data.sim.mfr, formulaA = ~rater, 
            facets = data.sim.facets, constraint = "cases" )
  test_2_cases <- tam.mml.mfr( data.sim.mfr, formulaA = ~item+rater, 
            facets = data.sim.facets, constraint = "cases" )
## End(Not run)

Results