| fixedCalib {mirt} | R Documentation |
Fixed-item calibration method
Description
Implements the set of fixed-item calibration methods described by Kim (2006). The initial
calibrated model must be fitted via mirt, is currently limited to
unidimensional models only, and should only be utilized when the new set of responses
are obtained from a population with similar distributional characteristics in the latent traits.
For more flexible calibration of items, including a fixed-item calibration variant involving
anchor items for equating, see multipleGroup.
Usage
fixedCalib(
data,
model = 1,
old_mod,
PAU = "MWU",
NEMC = "MEM",
technical = list(),
...
)
Arguments
data |
new data to be used for calibration. Note that to be consistent
with the |
model |
type of model to fit for the complete dataset (not that for the fixed items
in |
old_mod |
a model of class SingleGroupClass fitted using |
PAU |
prior ability update (PAU) approach. Supports none ( |
NEMC |
number of EM cycles (NEMC) to use for the to-be-estimated parameters.
Supports one ( |
technical |
list of technical estimation arguments
(see |
... |
additional arguments to pass to |
References
Kim, S. (2006). A comparative study of IRT fixed parameter calibration methods. Journal of Educational Measurement, 4(43), 355-381.
See Also
Examples
## No test:
# single factor
set.seed(12345)
J <- 50
a <- matrix(abs(rnorm(J,1,.3)), ncol=1)
d <- matrix(rnorm(J,0,.7),ncol=1)
itemtype <- rep('2PL', nrow(a))
# calibration data theta ~ N(0,1)
N <- 3000
dataset1 <- simdata(a, d, N = N, itemtype=itemtype)
# new data (again, theta ~ N(0,1))
dataset2 <- simdata(a, d, N = 1000, itemtype=itemtype)
# last 40% of experimental items not given to calibration group
# (unobserved; hence removed)
dataset1 <- dataset1[,-c(J:(J*.6))]
head(dataset1)
## Item_1 Item_2 Item_3 Item_4 Item_5 Item_6 Item_7 Item_8 Item_9 Item_10
## [1,] 0 0 0 0 0 0 1 1 1 0
## [2,] 0 0 0 1 0 1 0 0 0 0
## [3,] 0 1 0 1 1 1 1 1 1 1
## [4,] 0 1 1 0 1 0 0 0 1 0
## [5,] 1 0 1 1 1 0 1 0 1 0
## [6,] 1 1 0 1 0 1 0 1 1 0
## Item_11 Item_12 Item_13 Item_14 Item_15 Item_16 Item_17 Item_18 Item_19
## [1,] 1 1 1 1 1 0 0 0 1
## [2,] 0 0 1 0 0 0 1 1 0
## [3,] 1 1 0 1 0 1 0 0 0
## [4,] 0 0 1 1 0 0 1 1 0
## [5,] 0 0 0 1 0 0 1 1 1
## [6,] 1 0 1 1 0 0 1 0 1
## Item_20 Item_21 Item_22 Item_23 Item_24 Item_25 Item_26 Item_27 Item_28
## [1,] 0 0 0 0 1 0 0 1 0
## [2,] 0 1 0 0 1 0 0 1 1
## [3,] 0 1 0 1 1 1 1 0 0
## [4,] 0 0 0 0 0 1 0 1 0
## [5,] 0 1 1 1 1 1 1 1 1
## [6,] 0 0 0 1 1 1 0 0 0
## Item_29
## [1,] 1
## [2,] 1
## [3,] 1
## [4,] 1
## [5,] 1
## [6,] 1
#--------------------------------------
# calibrated model from dataset1 only
mod <- mirt(dataset1, model = 1)
coef(mod, simplify=TRUE)
## $items
## a1 d g u
## Item_1 1.196 -0.366 0 1
## Item_2 1.218 1.345 0 1
## Item_3 1.029 -0.017 0 1
## Item_4 0.817 0.230 0 1
## Item_5 1.148 -0.462 0 1
## Item_6 0.473 0.157 0 1
## Item_7 1.172 0.491 0 1
## Item_8 0.882 0.599 0 1
## Item_9 0.732 1.367 0 1
## Item_10 0.737 -1.703 0 1
## Item_11 0.988 0.057 0 1
## Item_12 1.621 -0.888 0 1
## Item_13 1.211 0.362 0 1
## Item_14 1.261 1.101 0 1
## Item_15 0.748 -0.319 0 1
## Item_16 1.171 -1.247 0 1
## Item_17 0.742 0.637 0 1
## Item_18 0.925 1.128 0 1
## Item_19 1.477 0.395 0 1
## Item_20 1.037 -0.993 0 1
## Item_21 1.136 0.068 0 1
## Item_22 1.402 -0.513 0 1
## Item_23 0.679 -0.684 0 1
## Item_24 0.483 1.690 0 1
## Item_25 0.501 1.048 0 1
## Item_26 1.604 0.636 0 1
## Item_27 0.833 0.559 0 1
## Item_28 1.100 -0.493 0 1
## Item_29 1.200 0.354 0 1
##
## $means
## F1
## 0
##
## $cov
## F1
## F1 1
# No Prior Weights Updating and One EM Cycle (NWU-OEM)
NWU_OEM <- fixedCalib(dataset2, model=1, old_mod=mod, PAU='NWU', NEMC='OEM')
coef(NWU_OEM, simplify=TRUE)
## $items
## a1 d g u
## Item_1 1.196 -0.366 0 1
## Item_2 1.218 1.345 0 1
## Item_3 1.029 -0.017 0 1
## Item_4 0.817 0.230 0 1
## Item_5 1.148 -0.462 0 1
## Item_6 0.473 0.157 0 1
## Item_7 1.172 0.491 0 1
## Item_8 0.882 0.599 0 1
## Item_9 0.732 1.367 0 1
## Item_10 0.737 -1.703 0 1
## Item_11 0.988 0.057 0 1
## Item_12 1.621 -0.888 0 1
## Item_13 1.211 0.362 0 1
## Item_14 1.261 1.101 0 1
## Item_15 0.748 -0.319 0 1
## Item_16 1.171 -1.247 0 1
## Item_17 0.742 0.637 0 1
## Item_18 0.925 1.128 0 1
## Item_19 1.477 0.395 0 1
## Item_20 1.037 -0.993 0 1
## Item_21 1.136 0.068 0 1
## Item_22 1.402 -0.513 0 1
## Item_23 0.679 -0.684 0 1
## Item_24 0.483 1.690 0 1
## Item_25 0.501 1.048 0 1
## Item_26 1.604 0.636 0 1
## Item_27 0.833 0.559 0 1
## Item_28 1.100 -0.493 0 1
## Item_29 1.200 0.354 0 1
## Item_30 0.750 1.329 0 1
## Item_31 1.093 1.235 0 1
## Item_32 1.380 1.740 0 1
## Item_33 1.112 0.441 0 1
## Item_34 1.164 2.443 0 1
## Item_35 1.054 1.361 0 1
## Item_36 0.863 1.818 0 1
## Item_37 0.717 0.865 0 1
## Item_38 0.379 0.044 0 1
## Item_39 1.180 1.336 0 1
## Item_40 0.713 0.967 0 1
## Item_41 0.865 0.150 0 1
## Item_42 0.205 -0.581 0 1
## Item_43 0.587 1.914 0 1
## Item_44 0.977 0.509 0 1
## Item_45 0.944 -0.151 0 1
## Item_46 1.239 1.288 0 1
## Item_47 0.547 0.176 0 1
## Item_48 0.811 2.059 0 1
## Item_49 0.841 0.169 0 1
## Item_50 0.631 -0.103 0 1
##
## $means
## F1
## 0
##
## $cov
## F1
## F1 1
data.frame(coef(NWU_OEM, simplify=TRUE)$items[,c('a1','d')],
pop_a1=a, pop_d=d)
## a1 d pop_a1 pop_d
## Item_1 1.1961890 -0.36582599 1.1756586 -0.37827025
## Item_2 1.2180200 1.34513036 1.2128398 1.36338487
## Item_3 1.0287076 -0.01749165 0.9672090 0.03751319
## Item_4 0.8169838 0.22955350 0.8639508 0.24616399
## Item_5 1.1477420 -0.46213650 1.1817662 -0.46968358
## Item_6 0.4734409 0.15748181 0.4546132 0.19456759
## Item_7 1.1723767 0.49084966 1.1890296 0.48381989
## Item_8 0.8823964 0.59861143 0.9171448 0.57665673
## Item_9 0.7320060 1.36669674 0.9147521 1.50154551
## Item_10 0.7374958 -1.70288028 0.7242034 -1.64286078
## Item_11 0.9877230 0.05684034 0.9651257 0.10471439
## Item_12 1.6208445 -0.88830542 1.5451936 -0.93977204
## Item_13 1.2113938 0.36247319 1.1111884 0.38731215
## Item_14 1.2608594 1.10068854 1.1560649 1.11297399
## Item_15 0.7483149 -0.31912299 0.7748404 -0.41081572
## Item_16 1.1708992 -1.24683338 1.2450700 -1.28266411
## Item_17 0.7422645 0.63674114 0.7340927 0.62169760
## Item_18 0.9248296 1.12848731 0.9005267 1.11544193
## Item_19 1.4767582 0.39539515 1.3362138 0.36179827
## Item_20 1.0368814 -0.99330414 1.0896171 -0.90697018
## Item_21 1.1357543 0.06752098 1.2338866 0.03823090
## Item_22 1.4021119 -0.51282038 1.4367355 -0.54925456
## Item_23 0.6786602 -0.68438097 0.8067015 -0.73454697
## Item_24 0.4828002 1.68992778 0.5340588 1.63135837
## Item_25 0.5011412 1.04812815 0.5206871 0.98189377
## Item_26 1.6039924 0.63625385 1.5415293 0.65982060
## Item_27 0.8328685 0.55938549 0.8555058 0.57838080
## Item_28 1.1001393 -0.49299690 1.1861139 -0.56807834
## Item_29 1.1996287 0.35417420 1.1836370 0.33337380
## Item_30 0.7503191 1.32941502 0.9513067 0.71488089
## Item_31 1.0934852 1.23450251 1.2435620 0.45176815
## Item_32 1.3797225 1.73968930 1.6590501 0.73020049
## Item_33 1.1122063 0.44125253 1.6147571 -0.21305838
## Item_34 1.1635528 2.44296521 1.4897337 1.73397764
## Item_35 1.0542788 1.36075678 1.0762814 0.67985447
## Item_36 0.8625460 1.81754474 1.1473565 1.30696943
## Item_37 0.7169594 0.86486730 0.9027740 0.47042973
## Item_38 0.3789943 0.04394555 0.5013849 -0.21556737
## Item_39 1.1801729 1.33626718 1.5303202 0.37556660
## Item_40 0.7130625 0.96707202 1.0077403 0.57740905
## Item_41 0.8654027 0.14975466 1.3385533 -0.67473104
## Item_42 0.2054520 -0.58087717 0.2858926 -0.59855776
## Item_43 0.5870526 1.91384494 0.6819203 1.32086286
## Item_44 0.9773758 0.50918361 1.2811422 -0.27427356
## Item_45 0.9438821 -0.15062731 1.2563355 -0.68644306
## Item_46 1.2394331 1.28838231 1.4382188 0.48113247
## Item_47 0.5466966 0.17590264 0.5760704 -0.35353046
## Item_48 0.8112318 2.05870733 1.1702210 1.51040387
## Item_49 0.8414788 0.16883149 1.1749563 -0.41985829
## Item_50 0.6307197 -0.10310709 0.6079603 -0.48618269
plot(NWU_OEM, type = 'empiricalhist')
# No Prior Weights Updating and Multiple EM Cycles (NWU-MEM)
NWU_MEM <- fixedCalib(dataset2, model = 1, old_mod = mod, PAU = 'NWU')
coef(NWU_MEM, simplify=TRUE)
## $items
## a1 d g u
## Item_1 1.196 -0.366 0 1
## Item_2 1.218 1.345 0 1
## Item_3 1.029 -0.017 0 1
## Item_4 0.817 0.230 0 1
## Item_5 1.148 -0.462 0 1
## Item_6 0.473 0.157 0 1
## Item_7 1.172 0.491 0 1
## Item_8 0.882 0.599 0 1
## Item_9 0.732 1.367 0 1
## Item_10 0.737 -1.703 0 1
## Item_11 0.988 0.057 0 1
## Item_12 1.621 -0.888 0 1
## Item_13 1.211 0.362 0 1
## Item_14 1.261 1.101 0 1
## Item_15 0.748 -0.319 0 1
## Item_16 1.171 -1.247 0 1
## Item_17 0.742 0.637 0 1
## Item_18 0.925 1.128 0 1
## Item_19 1.477 0.395 0 1
## Item_20 1.037 -0.993 0 1
## Item_21 1.136 0.068 0 1
## Item_22 1.402 -0.513 0 1
## Item_23 0.679 -0.684 0 1
## Item_24 0.483 1.690 0 1
## Item_25 0.501 1.048 0 1
## Item_26 1.604 0.636 0 1
## Item_27 0.833 0.559 0 1
## Item_28 1.100 -0.493 0 1
## Item_29 1.200 0.354 0 1
## Item_30 0.976 0.738 0 1
## Item_31 1.355 0.436 0 1
## Item_32 1.896 0.751 0 1
## Item_33 1.483 -0.136 0 1
## Item_34 1.566 1.615 0 1
## Item_35 1.108 0.689 0 1
## Item_36 1.027 1.297 0 1
## Item_37 0.950 0.485 0 1
## Item_38 0.636 -0.358 0 1
## Item_39 1.561 0.369 0 1
## Item_40 1.002 0.572 0 1
## Item_41 1.293 -0.537 0 1
## Item_42 0.237 -0.569 0 1
## Item_43 0.759 1.528 0 1
## Item_44 1.267 -0.198 0 1
## Item_45 1.179 -0.719 0 1
## Item_46 1.484 0.418 0 1
## Item_47 0.517 -0.350 0 1
## Item_48 1.204 1.560 0 1
## Item_49 1.178 -0.381 0 1
## Item_50 0.611 -0.485 0 1
##
## $means
## F1
## 0
##
## $cov
## F1
## F1 1
data.frame(coef(NWU_MEM, simplify=TRUE)$items[,c('a1','d')],
pop_a1=a, pop_d=d)
## a1 d pop_a1 pop_d
## Item_1 1.1961890 -0.36582599 1.1756586 -0.37827025
## Item_2 1.2180200 1.34513036 1.2128398 1.36338487
## Item_3 1.0287076 -0.01749165 0.9672090 0.03751319
## Item_4 0.8169838 0.22955350 0.8639508 0.24616399
## Item_5 1.1477420 -0.46213650 1.1817662 -0.46968358
## Item_6 0.4734409 0.15748181 0.4546132 0.19456759
## Item_7 1.1723767 0.49084966 1.1890296 0.48381989
## Item_8 0.8823964 0.59861143 0.9171448 0.57665673
## Item_9 0.7320060 1.36669674 0.9147521 1.50154551
## Item_10 0.7374958 -1.70288028 0.7242034 -1.64286078
## Item_11 0.9877230 0.05684034 0.9651257 0.10471439
## Item_12 1.6208445 -0.88830542 1.5451936 -0.93977204
## Item_13 1.2113938 0.36247319 1.1111884 0.38731215
## Item_14 1.2608594 1.10068854 1.1560649 1.11297399
## Item_15 0.7483149 -0.31912299 0.7748404 -0.41081572
## Item_16 1.1708992 -1.24683338 1.2450700 -1.28266411
## Item_17 0.7422645 0.63674114 0.7340927 0.62169760
## Item_18 0.9248296 1.12848731 0.9005267 1.11544193
## Item_19 1.4767582 0.39539515 1.3362138 0.36179827
## Item_20 1.0368814 -0.99330414 1.0896171 -0.90697018
## Item_21 1.1357543 0.06752098 1.2338866 0.03823090
## Item_22 1.4021119 -0.51282038 1.4367355 -0.54925456
## Item_23 0.6786602 -0.68438097 0.8067015 -0.73454697
## Item_24 0.4828002 1.68992778 0.5340588 1.63135837
## Item_25 0.5011412 1.04812815 0.5206871 0.98189377
## Item_26 1.6039924 0.63625385 1.5415293 0.65982060
## Item_27 0.8328685 0.55938549 0.8555058 0.57838080
## Item_28 1.1001393 -0.49299690 1.1861139 -0.56807834
## Item_29 1.1996287 0.35417420 1.1836370 0.33337380
## Item_30 0.9762030 0.73762603 0.9513067 0.71488089
## Item_31 1.3546278 0.43608060 1.2435620 0.45176815
## Item_32 1.8963614 0.75072559 1.6590501 0.73020049
## Item_33 1.4833684 -0.13615393 1.6147571 -0.21305838
## Item_34 1.5661380 1.61503162 1.4897337 1.73397764
## Item_35 1.1080231 0.68889209 1.0762814 0.67985447
## Item_36 1.0265916 1.29656964 1.1473565 1.30696943
## Item_37 0.9495227 0.48504809 0.9027740 0.47042973
## Item_38 0.6357917 -0.35821345 0.5013849 -0.21556737
## Item_39 1.5609002 0.36934080 1.5303202 0.37556660
## Item_40 1.0017413 0.57214795 1.0077403 0.57740905
## Item_41 1.2934463 -0.53663846 1.3385533 -0.67473104
## Item_42 0.2365342 -0.56867576 0.2858926 -0.59855776
## Item_43 0.7585749 1.52822491 0.6819203 1.32086286
## Item_44 1.2665593 -0.19834384 1.2811422 -0.27427356
## Item_45 1.1786531 -0.71904132 1.2563355 -0.68644306
## Item_46 1.4839823 0.41813815 1.4382188 0.48113247
## Item_47 0.5165255 -0.35006720 0.5760704 -0.35353046
## Item_48 1.2040324 1.55971466 1.1702210 1.51040387
## Item_49 1.1779999 -0.38145049 1.1749563 -0.41985829
## Item_50 0.6105721 -0.48525352 0.6079603 -0.48618269
plot(NWU_MEM, type = 'empiricalhist')
# One Prior Weights Updating and One EM Cycle (OWU-OEM)
OWU_OEM <- fixedCalib(dataset2, model=1, old_mod=mod, PAU='OWU', NEMC="OEM")
coef(OWU_OEM, simplify=TRUE)
## $items
## a1 d g u
## Item_1 1.196 -0.366 0 1
## Item_2 1.218 1.345 0 1
## Item_3 1.029 -0.017 0 1
## Item_4 0.817 0.230 0 1
## Item_5 1.148 -0.462 0 1
## Item_6 0.473 0.157 0 1
## Item_7 1.172 0.491 0 1
## Item_8 0.882 0.599 0 1
## Item_9 0.732 1.367 0 1
## Item_10 0.737 -1.703 0 1
## Item_11 0.988 0.057 0 1
## Item_12 1.621 -0.888 0 1
## Item_13 1.211 0.362 0 1
## Item_14 1.261 1.101 0 1
## Item_15 0.748 -0.319 0 1
## Item_16 1.171 -1.247 0 1
## Item_17 0.742 0.637 0 1
## Item_18 0.925 1.128 0 1
## Item_19 1.477 0.395 0 1
## Item_20 1.037 -0.993 0 1
## Item_21 1.136 0.068 0 1
## Item_22 1.402 -0.513 0 1
## Item_23 0.679 -0.684 0 1
## Item_24 0.483 1.690 0 1
## Item_25 0.501 1.048 0 1
## Item_26 1.604 0.636 0 1
## Item_27 0.833 0.559 0 1
## Item_28 1.100 -0.493 0 1
## Item_29 1.200 0.354 0 1
## Item_30 0.734 1.342 0 1
## Item_31 1.081 1.235 0 1
## Item_32 1.354 1.740 0 1
## Item_33 1.087 0.449 0 1
## Item_34 1.180 2.469 0 1
## Item_35 1.080 1.352 0 1
## Item_36 0.867 1.826 0 1
## Item_37 0.736 0.845 0 1
## Item_38 0.407 0.018 0 1
## Item_39 1.192 1.330 0 1
## Item_40 0.694 0.970 0 1
## Item_41 0.876 0.141 0 1
## Item_42 0.207 -0.593 0 1
## Item_43 0.608 1.915 0 1
## Item_44 0.948 0.525 0 1
## Item_45 0.918 -0.150 0 1
## Item_46 1.246 1.287 0 1
## Item_47 0.541 0.194 0 1
## Item_48 0.824 2.052 0 1
## Item_49 0.881 0.150 0 1
## Item_50 0.643 -0.109 0 1
##
## $means
## F1
## 0
##
## $cov
## F1
## F1 1
data.frame(coef(OWU_OEM, simplify=TRUE)$items[,c('a1','d')], pop_a1=a, pop_d=d)
## a1 d pop_a1 pop_d
## Item_1 1.1961890 -0.36582599 1.1756586 -0.37827025
## Item_2 1.2180200 1.34513036 1.2128398 1.36338487
## Item_3 1.0287076 -0.01749165 0.9672090 0.03751319
## Item_4 0.8169838 0.22955350 0.8639508 0.24616399
## Item_5 1.1477420 -0.46213650 1.1817662 -0.46968358
## Item_6 0.4734409 0.15748181 0.4546132 0.19456759
## Item_7 1.1723767 0.49084966 1.1890296 0.48381989
## Item_8 0.8823964 0.59861143 0.9171448 0.57665673
## Item_9 0.7320060 1.36669674 0.9147521 1.50154551
## Item_10 0.7374958 -1.70288028 0.7242034 -1.64286078
## Item_11 0.9877230 0.05684034 0.9651257 0.10471439
## Item_12 1.6208445 -0.88830542 1.5451936 -0.93977204
## Item_13 1.2113938 0.36247319 1.1111884 0.38731215
## Item_14 1.2608594 1.10068854 1.1560649 1.11297399
## Item_15 0.7483149 -0.31912299 0.7748404 -0.41081572
## Item_16 1.1708992 -1.24683338 1.2450700 -1.28266411
## Item_17 0.7422645 0.63674114 0.7340927 0.62169760
## Item_18 0.9248296 1.12848731 0.9005267 1.11544193
## Item_19 1.4767582 0.39539515 1.3362138 0.36179827
## Item_20 1.0368814 -0.99330414 1.0896171 -0.90697018
## Item_21 1.1357543 0.06752098 1.2338866 0.03823090
## Item_22 1.4021119 -0.51282038 1.4367355 -0.54925456
## Item_23 0.6786602 -0.68438097 0.8067015 -0.73454697
## Item_24 0.4828002 1.68992778 0.5340588 1.63135837
## Item_25 0.5011412 1.04812815 0.5206871 0.98189377
## Item_26 1.6039924 0.63625385 1.5415293 0.65982060
## Item_27 0.8328685 0.55938549 0.8555058 0.57838080
## Item_28 1.1001393 -0.49299690 1.1861139 -0.56807834
## Item_29 1.1996287 0.35417420 1.1836370 0.33337380
## Item_30 0.7338596 1.34171621 0.9513067 0.71488089
## Item_31 1.0810934 1.23518942 1.2435620 0.45176815
## Item_32 1.3536031 1.74007904 1.6590501 0.73020049
## Item_33 1.0871425 0.44941021 1.6147571 -0.21305838
## Item_34 1.1800858 2.46871234 1.4897337 1.73397764
## Item_35 1.0803263 1.35175673 1.0762814 0.67985447
## Item_36 0.8666080 1.82586462 1.1473565 1.30696943
## Item_37 0.7358795 0.84485396 0.9027740 0.47042973
## Item_38 0.4070019 0.01833533 0.5013849 -0.21556737
## Item_39 1.1921382 1.33027311 1.5303202 0.37556660
## Item_40 0.6941284 0.97027712 1.0077403 0.57740905
## Item_41 0.8759719 0.14117791 1.3385533 -0.67473104
## Item_42 0.2070009 -0.59318726 0.2858926 -0.59855776
## Item_43 0.6082669 1.91531663 0.6819203 1.32086286
## Item_44 0.9481219 0.52535227 1.2811422 -0.27427356
## Item_45 0.9181546 -0.15002032 1.2563355 -0.68644306
## Item_46 1.2461867 1.28672335 1.4382188 0.48113247
## Item_47 0.5414343 0.19387196 0.5760704 -0.35353046
## Item_48 0.8238261 2.05156006 1.1702210 1.51040387
## Item_49 0.8809591 0.15027203 1.1749563 -0.41985829
## Item_50 0.6425456 -0.10887582 0.6079603 -0.48618269
plot(OWU_OEM, type = 'empiricalhist')
# One Prior Weights Updating and Multiple EM Cycles (OWU-MEM)
OWU_MEM <- fixedCalib(dataset2, model = 1, old_mod = mod, PAU = 'OWU')
coef(OWU_MEM, simplify=TRUE)
## $items
## a1 d g u
## Item_1 1.196 -0.366 0 1
## Item_2 1.218 1.345 0 1
## Item_3 1.029 -0.017 0 1
## Item_4 0.817 0.230 0 1
## Item_5 1.148 -0.462 0 1
## Item_6 0.473 0.157 0 1
## Item_7 1.172 0.491 0 1
## Item_8 0.882 0.599 0 1
## Item_9 0.732 1.367 0 1
## Item_10 0.737 -1.703 0 1
## Item_11 0.988 0.057 0 1
## Item_12 1.621 -0.888 0 1
## Item_13 1.211 0.362 0 1
## Item_14 1.261 1.101 0 1
## Item_15 0.748 -0.319 0 1
## Item_16 1.171 -1.247 0 1
## Item_17 0.742 0.637 0 1
## Item_18 0.925 1.128 0 1
## Item_19 1.477 0.395 0 1
## Item_20 1.037 -0.993 0 1
## Item_21 1.136 0.068 0 1
## Item_22 1.402 -0.513 0 1
## Item_23 0.679 -0.684 0 1
## Item_24 0.483 1.690 0 1
## Item_25 0.501 1.048 0 1
## Item_26 1.604 0.636 0 1
## Item_27 0.833 0.559 0 1
## Item_28 1.100 -0.493 0 1
## Item_29 1.200 0.354 0 1
## Item_30 0.975 0.738 0 1
## Item_31 1.353 0.436 0 1
## Item_32 1.892 0.749 0 1
## Item_33 1.482 -0.137 0 1
## Item_34 1.563 1.614 0 1
## Item_35 1.107 0.689 0 1
## Item_36 1.026 1.297 0 1
## Item_37 0.949 0.485 0 1
## Item_38 0.637 -0.358 0 1
## Item_39 1.559 0.369 0 1
## Item_40 1.001 0.572 0 1
## Item_41 1.294 -0.537 0 1
## Item_42 0.237 -0.569 0 1
## Item_43 0.758 1.528 0 1
## Item_44 1.266 -0.199 0 1
## Item_45 1.179 -0.719 0 1
## Item_46 1.482 0.418 0 1
## Item_47 0.517 -0.350 0 1
## Item_48 1.203 1.560 0 1
## Item_49 1.178 -0.382 0 1
## Item_50 0.611 -0.485 0 1
##
## $means
## F1
## 0
##
## $cov
## F1
## F1 1
data.frame(coef(OWU_MEM, simplify=TRUE)$items[,c('a1','d')],
pop_a1=a, pop_d=d)
## a1 d pop_a1 pop_d
## Item_1 1.1961890 -0.36582599 1.1756586 -0.37827025
## Item_2 1.2180200 1.34513036 1.2128398 1.36338487
## Item_3 1.0287076 -0.01749165 0.9672090 0.03751319
## Item_4 0.8169838 0.22955350 0.8639508 0.24616399
## Item_5 1.1477420 -0.46213650 1.1817662 -0.46968358
## Item_6 0.4734409 0.15748181 0.4546132 0.19456759
## Item_7 1.1723767 0.49084966 1.1890296 0.48381989
## Item_8 0.8823964 0.59861143 0.9171448 0.57665673
## Item_9 0.7320060 1.36669674 0.9147521 1.50154551
## Item_10 0.7374958 -1.70288028 0.7242034 -1.64286078
## Item_11 0.9877230 0.05684034 0.9651257 0.10471439
## Item_12 1.6208445 -0.88830542 1.5451936 -0.93977204
## Item_13 1.2113938 0.36247319 1.1111884 0.38731215
## Item_14 1.2608594 1.10068854 1.1560649 1.11297399
## Item_15 0.7483149 -0.31912299 0.7748404 -0.41081572
## Item_16 1.1708992 -1.24683338 1.2450700 -1.28266411
## Item_17 0.7422645 0.63674114 0.7340927 0.62169760
## Item_18 0.9248296 1.12848731 0.9005267 1.11544193
## Item_19 1.4767582 0.39539515 1.3362138 0.36179827
## Item_20 1.0368814 -0.99330414 1.0896171 -0.90697018
## Item_21 1.1357543 0.06752098 1.2338866 0.03823090
## Item_22 1.4021119 -0.51282038 1.4367355 -0.54925456
## Item_23 0.6786602 -0.68438097 0.8067015 -0.73454697
## Item_24 0.4828002 1.68992778 0.5340588 1.63135837
## Item_25 0.5011412 1.04812815 0.5206871 0.98189377
## Item_26 1.6039924 0.63625385 1.5415293 0.65982060
## Item_27 0.8328685 0.55938549 0.8555058 0.57838080
## Item_28 1.1001393 -0.49299690 1.1861139 -0.56807834
## Item_29 1.1996287 0.35417420 1.1836370 0.33337380
## Item_30 0.9754835 0.73759996 0.9513067 0.71488089
## Item_31 1.3529960 0.43565857 1.2435620 0.45176815
## Item_32 1.8920655 0.74941926 1.6590501 0.73020049
## Item_33 1.4823957 -0.13676561 1.6147571 -0.21305838
## Item_34 1.5632963 1.61443582 1.4897337 1.73397764
## Item_35 1.1070549 0.68876441 1.0762814 0.67985447
## Item_36 1.0257494 1.29658002 1.1473565 1.30696943
## Item_37 0.9487642 0.48500521 0.9027740 0.47042973
## Item_38 0.6365371 -0.35814281 0.5013849 -0.21556737
## Item_39 1.5587391 0.36866684 1.5303202 0.37556660
## Item_40 1.0011701 0.57211468 1.0077403 0.57740905
## Item_41 1.2936486 -0.53716279 1.3385533 -0.67473104
## Item_42 0.2372635 -0.56862885 0.2858926 -0.59855776
## Item_43 0.7581178 1.52829512 0.6819203 1.32086286
## Item_44 1.2655570 -0.19867816 1.2811422 -0.27427356
## Item_45 1.1789916 -0.71942555 1.2563355 -0.68644306
## Item_46 1.4820218 0.41756426 1.4382188 0.48113247
## Item_47 0.5174459 -0.34998852 0.5760704 -0.35353046
## Item_48 1.2029294 1.55973508 1.1702210 1.51040387
## Item_49 1.1780664 -0.38178278 1.1749563 -0.41985829
## Item_50 0.6113326 -0.48518034 0.6079603 -0.48618269
plot(OWU_MEM, type = 'empiricalhist')
# Multiple Prior Weights Updating and Multiple EM Cycles (MWU-MEM)
MWU_MEM <- fixedCalib(dataset2, model = 1, old_mod = mod)
coef(MWU_MEM, simplify=TRUE)
## $items
## a1 d g u
## Item_1 1.196 -0.366 0 1
## Item_2 1.218 1.345 0 1
## Item_3 1.029 -0.017 0 1
## Item_4 0.817 0.230 0 1
## Item_5 1.148 -0.462 0 1
## Item_6 0.473 0.157 0 1
## Item_7 1.172 0.491 0 1
## Item_8 0.882 0.599 0 1
## Item_9 0.732 1.367 0 1
## Item_10 0.737 -1.703 0 1
## Item_11 0.988 0.057 0 1
## Item_12 1.621 -0.888 0 1
## Item_13 1.211 0.362 0 1
## Item_14 1.261 1.101 0 1
## Item_15 0.748 -0.319 0 1
## Item_16 1.171 -1.247 0 1
## Item_17 0.742 0.637 0 1
## Item_18 0.925 1.128 0 1
## Item_19 1.477 0.395 0 1
## Item_20 1.037 -0.993 0 1
## Item_21 1.136 0.068 0 1
## Item_22 1.402 -0.513 0 1
## Item_23 0.679 -0.684 0 1
## Item_24 0.483 1.690 0 1
## Item_25 0.501 1.048 0 1
## Item_26 1.604 0.636 0 1
## Item_27 0.833 0.559 0 1
## Item_28 1.100 -0.493 0 1
## Item_29 1.200 0.354 0 1
## Item_30 0.980 0.736 0 1
## Item_31 1.358 0.433 0 1
## Item_32 1.906 0.748 0 1
## Item_33 1.487 -0.139 0 1
## Item_34 1.574 1.613 0 1
## Item_35 1.112 0.687 0 1
## Item_36 1.035 1.296 0 1
## Item_37 0.953 0.483 0 1
## Item_38 0.642 -0.360 0 1
## Item_39 1.568 0.367 0 1
## Item_40 1.007 0.570 0 1
## Item_41 1.299 -0.539 0 1
## Item_42 0.240 -0.569 0 1
## Item_43 0.764 1.527 0 1
## Item_44 1.270 -0.201 0 1
## Item_45 1.180 -0.721 0 1
## Item_46 1.490 0.416 0 1
## Item_47 0.522 -0.351 0 1
## Item_48 1.211 1.558 0 1
## Item_49 1.183 -0.384 0 1
## Item_50 0.615 -0.486 0 1
##
## $means
## F1
## 0
##
## $cov
## F1
## F1 1
data.frame(coef(MWU_MEM, simplify=TRUE)$items[,c('a1','d')],
pop_a1=a, pop_d=d)
## a1 d pop_a1 pop_d
## Item_1 1.1961890 -0.36582599 1.1756586 -0.37827025
## Item_2 1.2180200 1.34513036 1.2128398 1.36338487
## Item_3 1.0287076 -0.01749165 0.9672090 0.03751319
## Item_4 0.8169838 0.22955350 0.8639508 0.24616399
## Item_5 1.1477420 -0.46213650 1.1817662 -0.46968358
## Item_6 0.4734409 0.15748181 0.4546132 0.19456759
## Item_7 1.1723767 0.49084966 1.1890296 0.48381989
## Item_8 0.8823964 0.59861143 0.9171448 0.57665673
## Item_9 0.7320060 1.36669674 0.9147521 1.50154551
## Item_10 0.7374958 -1.70288028 0.7242034 -1.64286078
## Item_11 0.9877230 0.05684034 0.9651257 0.10471439
## Item_12 1.6208445 -0.88830542 1.5451936 -0.93977204
## Item_13 1.2113938 0.36247319 1.1111884 0.38731215
## Item_14 1.2608594 1.10068854 1.1560649 1.11297399
## Item_15 0.7483149 -0.31912299 0.7748404 -0.41081572
## Item_16 1.1708992 -1.24683338 1.2450700 -1.28266411
## Item_17 0.7422645 0.63674114 0.7340927 0.62169760
## Item_18 0.9248296 1.12848731 0.9005267 1.11544193
## Item_19 1.4767582 0.39539515 1.3362138 0.36179827
## Item_20 1.0368814 -0.99330414 1.0896171 -0.90697018
## Item_21 1.1357543 0.06752098 1.2338866 0.03823090
## Item_22 1.4021119 -0.51282038 1.4367355 -0.54925456
## Item_23 0.6786602 -0.68438097 0.8067015 -0.73454697
## Item_24 0.4828002 1.68992778 0.5340588 1.63135837
## Item_25 0.5011412 1.04812815 0.5206871 0.98189377
## Item_26 1.6039924 0.63625385 1.5415293 0.65982060
## Item_27 0.8328685 0.55938549 0.8555058 0.57838080
## Item_28 1.1001393 -0.49299690 1.1861139 -0.56807834
## Item_29 1.1996287 0.35417420 1.1836370 0.33337380
## Item_30 0.9799188 0.73574557 0.9513067 0.71488089
## Item_31 1.3578293 0.43339629 1.2435620 0.45176815
## Item_32 1.9059056 0.74796781 1.6590501 0.73020049
## Item_33 1.4871958 -0.13873748 1.6147571 -0.21305838
## Item_34 1.5741957 1.61315138 1.4897337 1.73397764
## Item_35 1.1115597 0.68668459 1.0762814 0.67985447
## Item_36 1.0346509 1.29592913 1.1473565 1.30696943
## Item_37 0.9527569 0.48319191 0.9027740 0.47042973
## Item_38 0.6415651 -0.35957581 0.5013849 -0.21556737
## Item_39 1.5678452 0.36674297 1.5303202 0.37556660
## Item_40 1.0068017 0.57042111 1.0077403 0.57740905
## Item_41 1.2990962 -0.53939359 1.3385533 -0.67473104
## Item_42 0.2396837 -0.56916091 0.2858926 -0.59855776
## Item_43 0.7643385 1.52742055 0.6819203 1.32086286
## Item_44 1.2701110 -0.20068022 1.2811422 -0.27427356
## Item_45 1.1801122 -0.72077719 1.2563355 -0.68644306
## Item_46 1.4901493 0.41558227 1.4382188 0.48113247
## Item_47 0.5222886 -0.35120291 0.5760704 -0.35353046
## Item_48 1.2109577 1.55840487 1.1702210 1.51040387
## Item_49 1.1826291 -0.38379663 1.1749563 -0.41985829
## Item_50 0.6153935 -0.48648805 0.6079603 -0.48618269
plot(MWU_MEM, type = 'empiricalhist')
# factor scores distribution check
fs <- fscores(MWU_MEM)
hist(fs)
c(mean_calib=mean(fs[1:N, ]), sd_calib=sd(fs[1:N, ]))
## mean_calib sd_calib
## -0.0001434687 0.9215504597
c(mean_exper=mean(fs[-c(1:N), ]), sd_exper=sd(fs[-c(1:N), ]))
## mean_exper sd_exper
## 0.03381096 0.93303246
############################
## Item length constraint example for each participant in the experimental
## items group. In this example, all participants were forced to have a test
## length of J=30, though the item pool had J=50 total items.
# new experimental data (relatively extreme, theta ~ N(.5,1.5))
dataset2 <- simdata(a, d, N = 1000, itemtype=itemtype,
mu=.5, sigma=matrix(1.5))
# Add missing values to each participant in new dataset where individuals
# were randomly administered 10 experimental items, subject to the constraint
# that each participant received a test with J=30 items.
dataset2 <- t(apply(dataset2, 1, function(x){
NA_precalib <- sample(1:30, 10)
NA_experimental <- sample(31:50, 10)
x[c(NA_precalib, NA_experimental)] <- NA
x
}))
head(dataset2)
## Item_1 Item_2 Item_3 Item_4 Item_5 Item_6 Item_7 Item_8 Item_9 Item_10
## [1,] NA 1 0 0 NA NA 0 NA 1 0
## [2,] NA NA NA 1 1 1 NA 0 1 NA
## [3,] NA NA 0 1 1 1 1 NA 1 0
## [4,] NA 1 1 NA 0 NA 1 1 1 1
## [5,] 1 1 1 1 NA NA 0 1 NA NA
## [6,] 1 0 0 1 1 1 NA NA NA 0
## Item_11 Item_12 Item_13 Item_14 Item_15 Item_16 Item_17 Item_18 Item_19
## [1,] 1 0 NA 0 NA 0 0 1 0
## [2,] 1 1 1 NA 0 0 1 NA 1
## [3,] 1 1 0 1 1 1 NA NA NA
## [4,] NA NA 1 NA 0 0 1 0 0
## [5,] 1 NA 0 1 1 0 1 1 NA
## [6,] 0 1 1 1 0 0 NA 1 NA
## Item_20 Item_21 Item_22 Item_23 Item_24 Item_25 Item_26 Item_27 Item_28
## [1,] 0 0 NA 0 1 NA 0 1 0
## [2,] NA 0 1 NA 1 1 1 1 0
## [3,] 1 1 NA 0 NA NA 1 NA 1
## [4,] 1 1 1 NA NA 1 1 1 1
## [5,] 1 NA 1 NA 1 NA NA 0 1
## [6,] 1 1 0 0 0 NA NA NA 0
## Item_29 Item_30 Item_31 Item_32 Item_33 Item_34 Item_35 Item_36 Item_37
## [1,] NA NA 0 1 1 NA 0 NA 0
## [2,] NA 1 1 NA NA 1 NA 1 1
## [3,] 1 1 1 NA NA NA NA 1 NA
## [4,] NA NA NA NA NA NA 0 NA 1
## [5,] 1 1 NA 1 NA 1 1 NA NA
## [6,] NA NA NA 1 NA NA 1 1 0
## Item_38 Item_39 Item_40 Item_41 Item_42 Item_43 Item_44 Item_45 Item_46
## [1,] 0 NA NA NA NA 1 0 NA 0
## [2,] NA NA NA 1 NA 1 1 1 1
## [3,] 1 NA NA NA 1 0 1 NA NA
## [4,] 0 1 NA NA NA NA 1 1 0
## [5,] NA 1 1 NA NA 1 NA NA 1
## [6,] 1 NA NA NA NA 1 NA NA 1
## Item_47 Item_48 Item_49 Item_50
## [1,] NA NA 0 NA
## [2,] NA 1 NA NA
## [3,] 0 0 1 1
## [4,] NA 1 0 1
## [5,] NA 1 1 1
## [6,] 0 1 NA 1
# check that all individuals had 30 items
all(rowSums(!is.na(dataset2)) == 30)
## [1] TRUE
# Multiple Prior Weights Updating and Multiple EM Cycles (MWU-MEM)
MWU_MEM <- fixedCalib(dataset2, model = 1, old_mod = mod)
coef(MWU_MEM, simplify=TRUE)
## $items
## a1 d g u
## Item_1 1.196 -0.366 0 1
## Item_2 1.218 1.345 0 1
## Item_3 1.029 -0.017 0 1
## Item_4 0.817 0.230 0 1
## Item_5 1.148 -0.462 0 1
## Item_6 0.473 0.157 0 1
## Item_7 1.172 0.491 0 1
## Item_8 0.882 0.599 0 1
## Item_9 0.732 1.367 0 1
## Item_10 0.737 -1.703 0 1
## Item_11 0.988 0.057 0 1
## Item_12 1.621 -0.888 0 1
## Item_13 1.211 0.362 0 1
## Item_14 1.261 1.101 0 1
## Item_15 0.748 -0.319 0 1
## Item_16 1.171 -1.247 0 1
## Item_17 0.742 0.637 0 1
## Item_18 0.925 1.128 0 1
## Item_19 1.477 0.395 0 1
## Item_20 1.037 -0.993 0 1
## Item_21 1.136 0.068 0 1
## Item_22 1.402 -0.513 0 1
## Item_23 0.679 -0.684 0 1
## Item_24 0.483 1.690 0 1
## Item_25 0.501 1.048 0 1
## Item_26 1.604 0.636 0 1
## Item_27 0.833 0.559 0 1
## Item_28 1.100 -0.493 0 1
## Item_29 1.200 0.354 0 1
## Item_30 1.073 1.010 0 1
## Item_31 1.415 0.728 0 1
## Item_32 2.096 0.852 0 1
## Item_33 1.620 0.024 0 1
## Item_34 1.241 1.730 0 1
## Item_35 1.126 0.867 0 1
## Item_36 1.211 1.154 0 1
## Item_37 1.021 0.621 0 1
## Item_38 0.654 -0.203 0 1
## Item_39 1.332 0.684 0 1
## Item_40 0.938 0.677 0 1
## Item_41 1.831 -0.565 0 1
## Item_42 0.386 -0.631 0 1
## Item_43 0.691 1.108 0 1
## Item_44 1.231 -0.054 0 1
## Item_45 1.103 -0.460 0 1
## Item_46 1.400 0.693 0 1
## Item_47 0.532 -0.290 0 1
## Item_48 1.121 1.508 0 1
## Item_49 1.157 -0.318 0 1
## Item_50 0.691 -0.432 0 1
##
## $means
## F1
## 0
##
## $cov
## F1
## F1 1
data.frame(coef(MWU_MEM, simplify=TRUE)$items[,c('a1','d')],
pop_a1=a, pop_d=d)
## a1 d pop_a1 pop_d
## Item_1 1.1961890 -0.36582599 1.1756586 -0.37827025
## Item_2 1.2180200 1.34513036 1.2128398 1.36338487
## Item_3 1.0287076 -0.01749165 0.9672090 0.03751319
## Item_4 0.8169838 0.22955350 0.8639508 0.24616399
## Item_5 1.1477420 -0.46213650 1.1817662 -0.46968358
## Item_6 0.4734409 0.15748181 0.4546132 0.19456759
## Item_7 1.1723767 0.49084966 1.1890296 0.48381989
## Item_8 0.8823964 0.59861143 0.9171448 0.57665673
## Item_9 0.7320060 1.36669674 0.9147521 1.50154551
## Item_10 0.7374958 -1.70288028 0.7242034 -1.64286078
## Item_11 0.9877230 0.05684034 0.9651257 0.10471439
## Item_12 1.6208445 -0.88830542 1.5451936 -0.93977204
## Item_13 1.2113938 0.36247319 1.1111884 0.38731215
## Item_14 1.2608594 1.10068854 1.1560649 1.11297399
## Item_15 0.7483149 -0.31912299 0.7748404 -0.41081572
## Item_16 1.1708992 -1.24683338 1.2450700 -1.28266411
## Item_17 0.7422645 0.63674114 0.7340927 0.62169760
## Item_18 0.9248296 1.12848731 0.9005267 1.11544193
## Item_19 1.4767582 0.39539515 1.3362138 0.36179827
## Item_20 1.0368814 -0.99330414 1.0896171 -0.90697018
## Item_21 1.1357543 0.06752098 1.2338866 0.03823090
## Item_22 1.4021119 -0.51282038 1.4367355 -0.54925456
## Item_23 0.6786602 -0.68438097 0.8067015 -0.73454697
## Item_24 0.4828002 1.68992778 0.5340588 1.63135837
## Item_25 0.5011412 1.04812815 0.5206871 0.98189377
## Item_26 1.6039924 0.63625385 1.5415293 0.65982060
## Item_27 0.8328685 0.55938549 0.8555058 0.57838080
## Item_28 1.1001393 -0.49299690 1.1861139 -0.56807834
## Item_29 1.1996287 0.35417420 1.1836370 0.33337380
## Item_30 1.0728327 1.00970460 0.9513067 0.71488089
## Item_31 1.4152227 0.72783419 1.2435620 0.45176815
## Item_32 2.0962138 0.85156001 1.6590501 0.73020049
## Item_33 1.6204379 0.02406782 1.6147571 -0.21305838
## Item_34 1.2411306 1.73012149 1.4897337 1.73397764
## Item_35 1.1259698 0.86699898 1.0762814 0.67985447
## Item_36 1.2110533 1.15414948 1.1473565 1.30696943
## Item_37 1.0209382 0.62085332 0.9027740 0.47042973
## Item_38 0.6536874 -0.20338064 0.5013849 -0.21556737
## Item_39 1.3317226 0.68396203 1.5303202 0.37556660
## Item_40 0.9379259 0.67723720 1.0077403 0.57740905
## Item_41 1.8312041 -0.56543279 1.3385533 -0.67473104
## Item_42 0.3860292 -0.63097116 0.2858926 -0.59855776
## Item_43 0.6905627 1.10809614 0.6819203 1.32086286
## Item_44 1.2311295 -0.05444516 1.2811422 -0.27427356
## Item_45 1.1033591 -0.46005338 1.2563355 -0.68644306
## Item_46 1.4001941 0.69281395 1.4382188 0.48113247
## Item_47 0.5317801 -0.29008785 0.5760704 -0.35353046
## Item_48 1.1206327 1.50830171 1.1702210 1.51040387
## Item_49 1.1573237 -0.31832472 1.1749563 -0.41985829
## Item_50 0.6909833 -0.43221941 0.6079603 -0.48618269
plot(MWU_MEM, type = 'empiricalhist')
## factor scores check
fs <- fscores(MWU_MEM)
hist(fs)
c(mean_calib=mean(fs[1:N, ]), sd_calib=sd(fs[1:N, ]))
## mean_calib sd_calib
## -0.0001434687 0.9215504597
## shrinkage, but generally different from calibrated sample
c(mean_exper=mean(fs[-c(1:N), ]), sd_exper=sd(fs[-c(1:N), ]))
## mean_exper sd_exper
## 0.3533213 1.0351346
## End(No test)
[Package mirt version 1.40 Index]