| read.mirt {mirt} | R Documentation |
Translate mirt parameters into suitable structure for plink package
Description
This function exports item parameters from the mirt package to the
plink package.
Usage
read.mirt(x, as.irt.pars = TRUE, ...)
Arguments
x |
a single object (or list of objects) returned from |
as.irt.pars |
if |
... |
additional arguments to be passed to |
Author(s)
Phil Chalmers rphilip.chalmers@gmail.com
Examples
## No test:
## unidimensional
library(plink)
data <- expand.table(LSAT7)
(mod1 <- mirt(data, 1))
##
## Call:
## mirt(data = data, model = 1)
##
## Full-information item factor analysis with 1 factor(s).
## Converged within 1e-04 tolerance after 28 EM iterations.
## mirt version: 1.40
## M-step optimizer: BFGS
## EM acceleration: Ramsay
## Number of rectangular quadrature: 61
## Latent density type: Gaussian
##
## Log-likelihood = -2658.805
## Estimated parameters: 10
## AIC = 5337.61
## BIC = 5386.688; SABIC = 5354.927
## G2 (21) = 31.7, p = 0.0628
## RMSEA = 0.023, CFI = NaN, TLI = NaN
plinkpars <- read.mirt(mod1)
plot(plinkpars)
plot(mod1, type = 'trace')
# graded
mod2 <- mirt(Science, 1)
plinkpars <- read.mirt(mod2)
plot(plinkpars)
plot(mod2, type = 'trace')
# gpcm
mod3 <- mirt(Science, 1, itemtype = 'gpcm')
plinkpars <- read.mirt(mod3)
plot(plinkpars)
plot(mod3, type = 'trace')
# nominal
mod4 <- mirt(Science, 1, itemtype = 'nominal')
plinkpars <- read.mirt(mod4)
plot(plinkpars)
plot(mod4, type = 'trace')
## multidimensional
data <- expand.table(LSAT7)
(mod1 <- mirt(data, 2))
##
## Call:
## mirt(data = data, model = 2)
##
## Full-information item factor analysis with 2 factor(s).
## Converged within 1e-04 tolerance after 436 EM iterations.
## mirt version: 1.40
## M-step optimizer: BFGS
## EM acceleration: Ramsay
## Number of rectangular quadrature: 31
## Latent density type: Gaussian
##
## Log-likelihood = -2653.52
## Estimated parameters: 14
## AIC = 5335.039
## BIC = 5403.748; SABIC = 5359.283
## G2 (17) = 21.13, p = 0.2205
## RMSEA = 0.016, CFI = NaN, TLI = NaN
plinkpars <- read.mirt(mod1)
plinkpars
## An object of class "irt.pars"
## Slot "pars":
## a1 a2
## [1,] -2.0069916 0.87012541 2.6477186 0
## [2,] -0.8488470 -0.52216804 0.7876460 0
## [3,] -2.1527152 -1.83632408 2.4827643 0
## [4,] -0.7559215 -0.02804534 0.4847314 0
## [5,] -0.7572922 0.00000000 1.8641045 0
##
## Slot "cat":
## [1] 2 2 2 2 2
##
## Slot "poly.mod":
## An object of class "poly.mod"
## Slot "model":
## [1] "drm"
##
## Slot "items":
## $drm
## [1] 1 2 3 4 5
##
##
##
## Slot "common":
## NULL
##
## Slot "location":
## [1] FALSE
##
## Slot "groups":
## [1] 1
##
## Slot "dimensions":
## [1] 2
plot(plinkpars)
plot(mod1, type = 'trace')
cmod <- mirt.model('
F1 = 1,4,5
F2 = 2-4')
model <- mirt(data, cmod)
plot(read.mirt(model))
itemplot(model, 1)
# graded
mod2 <- mirt(Science, 2)
plinkpars <- read.mirt(mod2)
plinkpars
## An object of class "irt.pars"
## Slot "pars":
## a1 a2 b1 b2 b3
## [1,] -1.3350281 0.09676376 5.210669 2.865549 -1.602826
## [2,] -0.8789508 1.85253465 3.703802 1.153176 -2.904255
## [3,] -1.4696076 1.16485648 4.663467 1.956626 -1.735796
## [4,] -1.7220434 0.00000000 3.988789 1.195247 -2.043998
##
## Slot "cat":
## [1] 4 4 4 4
##
## Slot "poly.mod":
## An object of class "poly.mod"
## Slot "model":
## [1] "grm"
##
## Slot "items":
## $grm
## [1] 1 2 3 4
##
##
##
## Slot "common":
## NULL
##
## Slot "location":
## [1] FALSE
##
## Slot "groups":
## [1] 1
##
## Slot "dimensions":
## [1] 2
plot(plinkpars)
plot(mod2, type = 'trace')
### multiple group equating example
set.seed(1234)
dat <- expand.table(LSAT7)
group <- sample(c('g1', 'g2'), nrow(dat), TRUE)
dat1 <- dat[group == 'g1', ]
dat2 <- dat[group == 'g2', ]
mod1 <- mirt(dat1, 1)
mod2 <- mirt(dat2, 1)
# convert and combine pars
plinkMG <- read.mirt(list(g1=mod1, g2=mod2))
# equivalently:
# mod <- multipleGroup(dat, 1, group)
# plinkMG <- read.mirt(mod)
combine <- matrix(1:5, 5, 2)
comb <- combine.pars(plinkMG, combine, grp.names=unique(group))
out <- plink(comb, rescale="SL")
equate(out)
## Maximum iterations reached for true score: 0
## $tse
## theta g2 g1
## 1 -160.732949 0 0.000000
## 2 -3.233741 1 0.973481
## 3 -1.910192 2 2.023409
## 4 -0.999050 3 3.021848
## 5 0.048359 4 3.973672
## 6 57.337844 5 5.000000
##
## $ose
## $ose$scores
## eap.theta.g2 eap.sd.g2 g2 g1
## 1 -1.896444 0.698843 0 0.0000000
## 2 -1.474472 0.697610 1 0.9893317
## 3 -1.002953 0.716007 2 1.9956600
## 4 -0.455590 0.747730 3 2.9912425
## 5 0.123384 0.782773 4 3.9901784
## 6 0.678747 0.812793 5 4.9949444
##
## $ose$dist
## $ose$dist$g2
## score pop1 pop2 syn
## [1,] 0 0.03273187 0.03273187 0.03273187
## [2,] 1 0.17343742 0.17343742 0.17343742
## [3,] 2 0.48733074 0.48733074 0.48733074
## [4,] 3 0.99873451 0.99873451 0.99873451
## [5,] 4 1.61799716 1.61799716 1.61799716
## [6,] 5 1.56457520 1.56457520 1.56457520
##
## $ose$dist$g1
## score pop1 pop2 syn
## [1,] 0 0.0338755 0.0338755 0.0338755
## [2,] 1 0.1748815 0.1748815 0.1748815
## [3,] 2 0.4863770 0.4863770 0.4863770
## [4,] 3 1.0132130 1.0132130 1.0132130
## [5,] 4 1.6175460 1.6175460 1.6175460
## [6,] 5 1.5489139 1.5489139 1.5489139
equate(out, method = 'OSE')
## $scores
## eap.theta.g2 eap.sd.g2 g2 g1
## 1 -1.896444 0.698843 0 0.0000000
## 2 -1.474472 0.697610 1 0.9893317
## 3 -1.002953 0.716007 2 1.9956600
## 4 -0.455590 0.747730 3 2.9912425
## 5 0.123384 0.782773 4 3.9901784
## 6 0.678747 0.812793 5 4.9949444
##
## $dist
## $dist$g2
## score pop1 pop2 syn
## [1,] 0 0.03273187 0.03273187 0.03273187
## [2,] 1 0.17343742 0.17343742 0.17343742
## [3,] 2 0.48733074 0.48733074 0.48733074
## [4,] 3 0.99873451 0.99873451 0.99873451
## [5,] 4 1.61799716 1.61799716 1.61799716
## [6,] 5 1.56457520 1.56457520 1.56457520
##
## $dist$g1
## score pop1 pop2 syn
## [1,] 0 0.0338755 0.0338755 0.0338755
## [2,] 1 0.1748815 0.1748815 0.1748815
## [3,] 2 0.4863770 0.4863770 0.4863770
## [4,] 3 1.0132130 1.0132130 1.0132130
## [5,] 4 1.6175460 1.6175460 1.6175460
## [6,] 5 1.5489139 1.5489139 1.5489139
## End(No test)
[Package mirt version 1.40 Index]