Return a list (or data.frame) of raw item and group level coefficients. Note that while
the output to the console is rounded to three digits, the returned list of objects is not.
Hence, elements from cfs <- coef(mod); cfs[[1]] will contain the non-rounded results (useful
for simulations).
# S4 method for SingleGroupClass
coef(
object,
CI = 0.95,
printSE = FALSE,
rotate = "none",
Target = NULL,
IRTpars = FALSE,
rawug = FALSE,
as.data.frame = FALSE,
simplify = FALSE,
unique = FALSE,
verbose = TRUE,
...
)an object of class SingleGroupClass,
MultipleGroupClass, or MixedClass
the amount of converged used to compute confidence intervals; default is 95 percent confidence intervals
logical; print the standard errors instead of the confidence intervals? When
IRTpars = TRUE then the delta method will be used to compute the associated standard errors
from mirt's default slope-intercept form
see summary method for details. The default rotation is 'none'
a dummy variable matrix indicting a target rotation pattern
logical; convert slope intercept parameters into traditional IRT parameters?
Only applicable to unidimensional models or models with simple structure (i.e., only one non-zero slope).
If a suitable ACOV estimate was computed in the fitted
model, and printSE = FALSE, then suitable CIs will be included based on the delta
method (where applicable)
logical; return the untransformed internal g and u parameters?
If FALSE, g and u's are converted with the original format along with delta standard errors
logical; convert list output to a data.frame instead?
logical; if all items have the same parameter names (indicating they are of the same class) then they are collapsed to a matrix, and a list of length 2 is returned containing a matrix of item parameters and group-level estimates
return the vector of uniquely estimated parameters
logical; allow information to be printed to the console?
additional arguments to be passed
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
# \donttest{
dat <- expand.table(LSAT7)
x <- mirt(dat, 1)
coef(x)
#> $Item.1
#> a1 d g u
#> par 0.988 1.856 0 1
#>
#> $Item.2
#> a1 d g u
#> par 1.081 0.808 0 1
#>
#> $Item.3
#> a1 d g u
#> par 1.706 1.804 0 1
#>
#> $Item.4
#> a1 d g u
#> par 0.765 0.486 0 1
#>
#> $Item.5
#> a1 d g u
#> par 0.736 1.855 0 1
#>
#> $GroupPars
#> MEAN_1 COV_11
#> par 0 1
#>
coef(x, IRTpars = TRUE)
#> $Item.1
#> a b g u
#> par 0.988 -1.879 0 1
#>
#> $Item.2
#> a b g u
#> par 1.081 -0.748 0 1
#>
#> $Item.3
#> a b g u
#> par 1.706 -1.058 0 1
#>
#> $Item.4
#> a b g u
#> par 0.765 -0.635 0 1
#>
#> $Item.5
#> a b g u
#> par 0.736 -2.52 0 1
#>
#> $GroupPars
#> MEAN_1 COV_11
#> par 0 1
#>
coef(x, simplify = TRUE)
#> $items
#> a1 d g u
#> Item.1 0.988 1.856 0 1
#> Item.2 1.081 0.808 0 1
#> Item.3 1.706 1.804 0 1
#> Item.4 0.765 0.486 0 1
#> Item.5 0.736 1.855 0 1
#>
#> $means
#> F1
#> 0
#>
#> $cov
#> F1
#> F1 1
#>
#with computed information matrix
x <- mirt(dat, 1, SE = TRUE)
coef(x)
#> $Item.1
#> a1 d g u
#> par 0.988 1.856 0 1
#> CI_2.5 0.641 1.598 NA NA
#> CI_97.5 1.335 2.114 NA NA
#>
#> $Item.2
#> a1 d g u
#> par 1.081 0.808 0 1
#> CI_2.5 0.750 0.629 NA NA
#> CI_97.5 1.412 0.987 NA NA
#>
#> $Item.3
#> a1 d g u
#> par 1.706 1.804 0 1
#> CI_2.5 1.078 1.404 NA NA
#> CI_97.5 2.334 2.205 NA NA
#>
#> $Item.4
#> a1 d g u
#> par 0.765 0.486 0 1
#> CI_2.5 0.502 0.339 NA NA
#> CI_97.5 1.028 0.633 NA NA
#>
#> $Item.5
#> a1 d g u
#> par 0.736 1.855 0 1
#> CI_2.5 0.440 1.630 NA NA
#> CI_97.5 1.032 2.079 NA NA
#>
#> $GroupPars
#> MEAN_1 COV_11
#> par 0 1
#> CI_2.5 NA NA
#> CI_97.5 NA NA
#>
coef(x, printSE = TRUE)
#> $Item.1
#> a1 d logit(g) logit(u)
#> par 0.988 1.856 -999 999
#> SE 0.177 0.131 NA NA
#>
#> $Item.2
#> a1 d logit(g) logit(u)
#> par 1.081 0.808 -999 999
#> SE 0.169 0.091 NA NA
#>
#> $Item.3
#> a1 d logit(g) logit(u)
#> par 1.706 1.804 -999 999
#> SE 0.320 0.204 NA NA
#>
#> $Item.4
#> a1 d logit(g) logit(u)
#> par 0.765 0.486 -999 999
#> SE 0.134 0.075 NA NA
#>
#> $Item.5
#> a1 d logit(g) logit(u)
#> par 0.736 1.855 -999 999
#> SE 0.151 0.114 NA NA
#>
#> $GroupPars
#> MEAN_1 COV_11
#> par 0 1
#> SE NA NA
#>
coef(x, as.data.frame = TRUE)
#> par CI_2.5 CI_97.5
#> Item.1.a1 0.9879254 0.6405319 1.3353189
#> Item.1.d 1.8560605 1.5983450 2.1137759
#> Item.1.g 0.0000000 NA NA
#> Item.1.u 1.0000000 NA NA
#> Item.2.a1 1.0808847 0.7500334 1.4117360
#> Item.2.d 0.8079786 0.6291264 0.9868309
#> Item.2.g 0.0000000 NA NA
#> Item.2.u 1.0000000 NA NA
#> Item.3.a1 1.7058006 1.0778209 2.3337803
#> Item.3.d 1.8042187 1.4035692 2.2048683
#> Item.3.g 0.0000000 NA NA
#> Item.3.u 1.0000000 NA NA
#> Item.4.a1 0.7651853 0.5022681 1.0281025
#> Item.4.d 0.4859966 0.3391601 0.6328331
#> Item.4.g 0.0000000 NA NA
#> Item.4.u 1.0000000 NA NA
#> Item.5.a1 0.7357980 0.4395386 1.0320574
#> Item.5.d 1.8545127 1.6302516 2.0787739
#> Item.5.g 0.0000000 NA NA
#> Item.5.u 1.0000000 NA NA
#> GroupPars.MEAN_1 0.0000000 NA NA
#> GroupPars.COV_11 1.0000000 NA NA
#two factors
x2 <- mirt(Science, 2)
coef(x2)
#> $Comfort
#> a1 a2 d1 d2 d3
#> par -1.335 0.097 5.211 2.866 -1.603
#>
#> $Work
#> a1 a2 d1 d2 d3
#> par -0.879 1.853 3.704 1.153 -2.904
#>
#> $Future
#> a1 a2 d1 d2 d3
#> par -1.47 1.165 4.663 1.957 -1.736
#>
#> $Benefit
#> a1 a2 d1 d2 d3
#> par -1.722 0 3.989 1.195 -2.044
#>
#> $GroupPars
#> MEAN_1 MEAN_2 COV_11 COV_21 COV_22
#> par 0 0 1 0 1
#>
coef(x2, rotate = 'varimax')
#>
#> Rotation: varimax
#>
#> $Comfort
#> a1 a2 d1 d2 d3
#> par 1.254 0.468 5.211 2.866 -1.603
#>
#> $Work
#> a1 a2 d1 d2 d3
#> par 0.323 2.025 3.704 1.153 -2.904
#>
#> $Future
#> a1 a2 d1 d2 d3
#> par 1.083 1.531 4.663 1.957 -1.736
#>
#> $Benefit
#> a1 a2 d1 d2 d3
#> par 1.653 0.484 3.989 1.195 -2.044
#>
#> $GroupPars
#> MEAN_1 MEAN_2 COV_11 COV_21 COV_22
#> par 0 0 1 0 1
#>
# }