Given an estimated model from any of mirt's model fitting functions this function will convert
the model parameters into the design data frame of starting values and other parameter
characteristics (similar to using the pars = 'values' for obtaining starting values).
References
Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06
Author
Phil Chalmers rphilip.chalmers@gmail.com
Examples
# \donttest{
dat <- expand.table(LSAT7)
mod <- mirt(dat, "F=1-5
CONSTRAIN=(1-5, a1)")
values <- mod2values(mod)
values
#> group item class name parnum value lbound ubound est const nconst
#> 1 all Item.1 dich a1 1 1.011 -Inf Inf TRUE 1 none
#> 2 all Item.1 dich d 2 1.868 -Inf Inf TRUE none none
#> 3 all Item.1 dich g 3 0.000 0 1 FALSE none none
#> 4 all Item.1 dich u 4 1.000 0 1 FALSE none none
#> 5 all Item.2 dich a1 5 1.011 -Inf Inf TRUE 1 none
#> 6 all Item.2 dich d 6 0.791 -Inf Inf TRUE none none
#> 7 all Item.2 dich g 7 0.000 0 1 FALSE none none
#> 8 all Item.2 dich u 8 1.000 0 1 FALSE none none
#> 9 all Item.3 dich a1 9 1.011 -Inf Inf TRUE 1 none
#> 10 all Item.3 dich d 10 1.461 -Inf Inf TRUE none none
#> 11 all Item.3 dich g 11 0.000 0 1 FALSE none none
#> 12 all Item.3 dich u 12 1.000 0 1 FALSE none none
#> 13 all Item.4 dich a1 13 1.011 -Inf Inf TRUE 1 none
#> 14 all Item.4 dich d 14 0.521 -Inf Inf TRUE none none
#> 15 all Item.4 dich g 15 0.000 0 1 FALSE none none
#> 16 all Item.4 dich u 16 1.000 0 1 FALSE none none
#> 17 all Item.5 dich a1 17 1.011 -Inf Inf TRUE 1 none
#> 18 all Item.5 dich d 18 1.993 -Inf Inf TRUE none none
#> 19 all Item.5 dich g 19 0.000 0 1 FALSE none none
#> 20 all Item.5 dich u 20 1.000 0 1 FALSE none none
#> 21 all GROUP GroupPars MEAN_1 21 0.000 -Inf Inf FALSE none none
#> 22 all GROUP GroupPars COV_11 22 1.000 0 Inf FALSE none none
#> prior.type prior_1 prior_2
#> 1 none NaN NaN
#> 2 none NaN NaN
#> 3 none NaN NaN
#> 4 none NaN NaN
#> 5 none NaN NaN
#> 6 none NaN NaN
#> 7 none NaN NaN
#> 8 none NaN NaN
#> 9 none NaN NaN
#> 10 none NaN NaN
#> 11 none NaN NaN
#> 12 none NaN NaN
#> 13 none NaN NaN
#> 14 none NaN NaN
#> 15 none NaN NaN
#> 16 none NaN NaN
#> 17 none NaN NaN
#> 18 none NaN NaN
#> 19 none NaN NaN
#> 20 none NaN NaN
#> 21 none NaN NaN
#> 22 none NaN NaN
# use the converted values as starting values in a new model, and reduce TOL
mod2 <- mirt(dat, 1, pars = values, TOL=1e-5)
coef(mod2, simplify=TRUE)
#> $items
#> a1 d g u
#> Item.1 1.011 1.868 0 1
#> Item.2 1.011 0.791 0 1
#> Item.3 1.011 1.461 0 1
#> Item.4 1.011 0.521 0 1
#> Item.5 1.011 1.993 0 1
#>
#> $means
#> F1
#> 0
#>
#> $cov
#> F1
#> F1 1
#>
# use parameters on different dataset
mod3 <- mirt(expand.table(LSAT6), pars=values)
coef(mod3, simplify=TRUE)
#> $items
#> a1 d g u
#> Item_1 0.755 2.730 0 1
#> Item_2 0.755 0.999 0 1
#> Item_3 0.755 0.240 0 1
#> Item_4 0.755 1.307 0 1
#> Item_5 0.755 2.100 0 1
#>
#> $means
#> F1
#> 0
#>
#> $cov
#> F1
#> F1 1
#>
# supports differing itemtypes on second model
sv <- mirt(Science, itemtype=c('graded', rep('gpcm', 3)), pars='values')
mod3 <- mirt(Science, pars = sv) # itemtype omitted
coef(mod3, simplify=TRUE)$items
#> a1 d1 d2 d3 ak0 ak1 ak2 ak3 d0
#> Comfort 1.0278666 4.842467 2.629427 -1.4610757 NA NA NA NA NA
#> Work 0.8622915 1.735970 2.608453 0.8615093 0 1 2 3 0
#> Future 2.1388465 4.498623 6.612162 4.8108956 0 1 2 3 0
#> Benefit 0.7208997 2.096130 2.894803 1.7189648 0 1 2 3 0
extract.mirt(mod3, 'itemtype')
#> [1] "graded" "gpcm" "gpcm" "gpcm"
# }