Given an internal mirt object estimate the bootstrapped standard errors. It may
be beneficial to run the computations using multi-core architecture (e.g., the parallel
package). Parameters are organized from the freely estimated values in mod2values(x)
(equality constraints will also be returned in the bootstrapped estimates).
boot.mirt(x, R = 100, boot.fun = NULL, technical = NULL, ...)Arguments
- x
an estimated model object
- R
number of draws to use (passed to the
boot()function)- boot.fun
a user-defined function used to extract the information from the bootstrap fitted models. Must be of the form
boot.fun(x), wherexis the bootstrap fitted model under investigation, and the return must be a numeric vector. If omitted a default function will be defined internally that returns the estimated parameters from themodobject, resulting in bootstrapped parameter estimate results- technical
technical arguments passed to estimation engine. See
mirtfor details- ...
additional arguments to be passed on to
boot(...)and mirt's estimation engine
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
Examples
# \donttest{
# standard
mod <- mirt(Science, 1)
booted <- boot.mirt(mod, R=20)
plot(booted)
booted
#>
#> ORDINARY NONPARAMETRIC BOOTSTRAP
#>
#>
#> Call:
#> boot.mirt(x = mod, R = 20)
#>
#>
#> Bootstrap Statistics :
#> original bias std. error
#> t1* 1.0417547 0.057453085 0.1853378
#> t2* 4.8641542 0.117409179 0.3287895
#> t3* 2.6399417 0.028935069 0.2211631
#> t4* -1.4660135 -0.043036416 0.1675969
#> t5* 1.2259618 0.016325124 0.2007617
#> t6* 2.9240027 0.061362253 0.2932473
#> t7* 0.9011651 0.015719572 0.1713621
#> t8* -2.2665647 -0.022291010 0.2486331
#> t9* 2.2933717 0.046506742 0.5424316
#> t10* 5.2339928 0.122452084 1.0286184
#> t11* 2.2137728 -0.006343118 0.4155776
#> t12* -1.9637062 -0.060291592 0.3678867
#> t13* 1.0949151 0.034361158 0.2454328
#> t14* 3.3479196 0.028974417 0.3012493
#> t15* 0.9916289 -0.033491971 0.1514729
#> t16* -1.6882599 -0.030078767 0.1980832
#run in parallel using snow back-end using all available cores
mod <- mirt(Science, 1)
booted <- boot.mirt(mod, parallel = 'snow', ncpus = parallel::detectCores())
booted
#>
#> ORDINARY NONPARAMETRIC BOOTSTRAP
#>
#>
#> Call:
#> boot.mirt(x = mod, parallel = "snow", ncpus = parallel::detectCores())
#>
#>
#> Bootstrap Statistics :
#> original bias std. error
#> t1* 1.0417547 0.043813843 0.2412273
#> t2* 4.8641542 0.195389942 0.5566439
#> t3* 2.6399417 0.050127490 0.2706744
#> t4* -1.4660135 -0.035475773 0.1679960
#> t5* 1.2259618 -0.003908781 0.1978620
#> t6* 2.9240027 0.048165349 0.2248971
#> t7* 0.9011651 0.001902018 0.1495929
#> t8* -2.2665647 -0.016643178 0.2090448
#> t9* 2.2933717 0.055942237 0.6631287
#> t10* 5.2339928 0.160157494 1.0606722
#> t11* 2.2137728 0.012249989 0.4194951
#> t12* -1.9637062 -0.076833875 0.4121066
#> t13* 1.0949151 0.068956104 0.2449640
#> t14* 3.3479196 0.095617953 0.3075003
#> t15* 0.9916289 0.031321499 0.1567300
#> t16* -1.6882599 -0.066492471 0.2023222
####
# bootstrapped CIs for standardized factor loadings
boot.fun <- function(mod){
so <- summary(mod, verbose=FALSE)
as.vector(so$rotF)
}
# test to see if it works before running
boot.fun(mod)
#> [1] 0.5220496 0.5844686 0.8030199 0.5410276
# run
booted.loads <- boot.mirt(mod, boot.fun=boot.fun)
booted.loads
#>
#> ORDINARY NONPARAMETRIC BOOTSTRAP
#>
#>
#> Call:
#> boot.mirt(x = mod, boot.fun = boot.fun)
#>
#>
#> Bootstrap Statistics :
#> original bias std. error
#> t1* 0.5220496 0.002344347 0.08867253
#> t2* 0.5844686 -0.003216162 0.06017769
#> t3* 0.8030199 0.007693533 0.07162299
#> t4* 0.5410276 0.003008830 0.07553746
# }