Estimate the parameters of the Rasch model and the Interaction model

fit_domains(dataSrc, item_property, predicate = NULL)

Arguments

dataSrc

a connection to a dexter database or a data.frame with columns: person_id, item_id, item_score

item_property

The item property defining the domains (subtests)

predicate

An optional expression to subset data, if NULL all data is used

Value

An object of class imp holding results for the Rasch model and the interaction model.

Details

We have generalised the interaction model for items having more than two (potentially, a largish number) of response categories. This function represents scores on subtests as super-items and analyses these as normal items.

Examples



db = start_new_project(verbAggrRules, ":memory:")
add_booklet(db, verbAggrData, "agg")
#> no column `person_id` provided, automatically generating unique person id's
#> $items
#>  [1] "S1DoCurse"   "S1DoScold"   "S1DoShout"   "S1WantCurse" "S1WantScold"
#>  [6] "S1WantShout" "S2DoCurse"   "S2DoScold"   "S2DoShout"   "S2WantCurse"
#> [11] "S2WantScold" "S2WantShout" "S3DoCurse"   "S3DoScold"   "S3DoShout"  
#> [16] "S3WantCurse" "S3WantScold" "S3WantShout" "S4DoCurse"   "S4DoScold"  
#> [21] "S4DoShout"   "S4WantCurse" "S4WantScold" "S4WantShout"
#> 
#> $person_properties
#> character(0)
#> 
#> $columns_ignored
#> [1] "gender" "anger" 
#> 
add_item_properties(db, verbAggrProperties)
#> 4 item properties for 24 items added or updated
mSit = fit_domains(db, item_property= "situation")
plot(mSit)





close_project(db)