Output for irt model
Examples
set.seed(12345)
cormatrix<-psych::sim.rasch(nvar=5,n=50000,low=-4,high=4,d=NULL,a=1,mu=0,sd=1)$items
irt_onefactor<-mirt::mirt(cormatrix,1,empiricalhist=TRUE,calcNull=TRUE)
irt_twofactor<-mirt::mirt(cormatrix,2,empiricalhist=TRUE,calcNull=TRUE)
irt_threefactor<-mirt::mirt(cormatrix,3,empiricalhist=TRUE,calcNull=TRUE)
report_irt(model=irt_onefactor,file="one_factor")
#> Q3 summary statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -0.133 -0.055 -0.042 -0.052 -0.026 -0.003
#>
#> V1 V2 V3 V4 V5
#> V1 1.000 -0.042 -0.053 -0.009 -0.003
#> V2 -0.042 1.000 -0.120 -0.056 -0.022
#> V3 -0.053 -0.120 1.000 -0.133 -0.041
#> V4 -0.009 -0.056 -0.133 1.000 -0.042
#> V5 -0.003 -0.022 -0.041 -0.042 1.000
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Data does not contain missing values. Continuing normally
#> Sample size after row-wise response data removal: 50000
#> $model_coefficients
#> a1 d g u
#> V1 1.1023 4.170279 0 1
#> V2 0.9437 1.945285 0 1
#> V3 1.0090 0.006786 0 1
#> V4 1.0210 -2.003479 0 1
#> V5 0.9278 -3.942474 0 1
#>
#> $model_coefficients_oblimin
#> .id a1 d g u x F1
#> V1 items 1.1023 4.170279 0 1 NA NA
#> V2 items 0.9437 1.945285 0 1 NA NA
#> V3 items 1.0090 0.006786 0 1 NA NA
#> V4 items 1.0210 -2.003479 0 1 NA NA
#> V5 items 0.9278 -3.942474 0 1 NA NA
#> 6 means NA NA NA NA 0 NA
#> 7 cov NA NA NA NA NA 1
#>
#> $model_options
#> Options
#> method EM
#> draws 5000
#> calcLL TRUE
#> SE FALSE
#> SE.type Oakes
#> verbose FALSE
#> SEtol 0.001
#> storeEMhistory FALSE
#> calcNull TRUE
#> odentype Gaussian
#> dentype Gaussian
#> zeroExtreme FALSE
#> accelerate Ramsay
#> Norder 2
#> delta 0.00001
#> Etable TRUE
#> plausible.draws 0
#> storeEtable TRUE
#> TOL 0.0001
#> omp_threads 1
#> PLCI FALSE
#> warn TRUE
#> message TRUE
#> technical.symmetric TRUE
#> technical.parallel TRUE
#> technical.omp TRUE
#> MAXQUAD 20000
#> NCYCLES 500
#> BURNIN 150
#> SEMCYCLES 100
#> SEM_from 0
#> SEM_to 0.999
#> KDRAWS 1
#> MHDRAWS 5
#> MHRM_SE_draws 2000
#> internal_constraints TRUE
#> keep_vcov_PD TRUE
#> theta_lim1 -6
#> theta_lim2 6
#> gain1 0.1
#> gain2 0.75
#> NULL.MODEL FALSE
#> USEEM TRUE
#> returnPrepList FALSE
#> Moptim BFGS
#> logLik_if_converged TRUE
#> info_if_converged TRUE
#> full FALSE
#> quadpts 61
#> exploratory FALSE
#>
#> $model_call
#> [1] "NULL"
#>
#> $q3_matrix
#> V1 V2 V3 V4 V5 min max
#> V1 NA NA NA NA NA NA NA
#> V2 -0.042436 NA NA NA NA -0.04244 -0.042436
#> V3 -0.052514 -0.12027 NA NA NA -0.12027 -0.052514
#> V4 -0.008633 -0.05594 -0.13294 NA NA -0.13294 -0.008633
#> V5 -0.003498 -0.02156 -0.04116 -0.042 NA -0.04200 -0.003498
#> min -0.052514 -0.12027 -0.13294 -0.042 Inf -0.13294 -0.052514
#> max -0.003498 -0.02156 -0.04116 -0.042 -Inf -0.04200 -0.003498
#>
#> $exp_residuals
#> V1 V2 V3 V4 V5 freq exp std.res
#> 1 0 0 0 0 0 305 303.35909 0.094212
#> 2 0 0 0 0 1 1 1.59070 -0.468355
#> 3 0 0 0 1 0 10 10.01357 -0.004288
#> 4 0 0 0 1 1 1 0.09944 2.855908
#> 5 0 0 1 0 0 69 75.69118 -0.769095
#> 6 0 0 1 0 1 1 0.74601 0.294068
#> 7 0 0 1 1 0 6 5.00277 0.445850
#> 8 0 1 0 0 0 555 563.76618 -0.369199
#> 9 0 1 0 0 1 5 5.33442 -0.144794
#> 10 0 1 0 1 0 26 35.62707 -1.612888
#> 11 0 1 1 0 0 301 267.25573 2.064128
#> 12 0 1 1 0 1 5 4.74502 0.117056
#> 13 0 1 1 1 0 24 33.75373 -1.678842
#> 14 1 0 0 0 0 4448 4428.45603 0.293688
#> 15 1 0 0 0 1 50 46.26632 0.548914
#> 16 1 0 0 1 0 313 312.08185 0.051973
#> 17 1 0 0 1 1 3 6.16199 -1.273797
#> 18 1 0 1 0 0 2296 2338.08421 -0.870340
#> 19 1 0 1 0 1 61 45.82145 2.242310
#> 20 1 0 1 1 0 343 329.19648 0.760785
#> 21 1 0 1 1 1 8 12.18158 -1.198088
#> 22 1 1 0 0 0 16581 16591.28512 -0.079849
#> 23 1 1 0 0 1 295 312.20151 -0.973529
#> 24 1 1 0 1 0 2253 2233.83298 0.405536
#> 25 1 1 0 1 1 84 79.36676 0.520074
#> 26 1 1 1 0 0 16618 16608.98748 0.069932
#> 27 1 1 1 0 1 588 585.71481 0.094423
#> 28 1 1 1 1 0 4452 4463.69443 -0.175038
#> 29 1 1 1 1 1 298 297.82007 0.010426
#>
#> $item_fit
#> item S_X2 df.S_X2 RMSEA.S_X2 p.S_X2
#> 1 V1 6.544 2 0.007 0.038
#> 2 V2 3.738 2 0.004 0.154
#> 3 V3 0.924 2 0.000 0.630
#> 4 V4 0.252 2 0.000 0.882
#> 5 V5 0.746 2 0.000 0.689
#>
#> $g2_fit
#> G2 p TLI CFI RMSEA df AIC BIC SABIC HQ logLik logPrior SElogLik
#> 1 29.3 0.1069 0.9963 0.997 0.002812 21 178500 178588 178557 178528 -89240 0 0
#>
#> $m2_fit
#> M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR TLI CFI
#> stats 9.61 5 0.087 0.004 0 0.008 0.003 0.997 0.998
#>
report_irt(model=irt_twofactor,file="two_factors")
#> Q3 summary statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.049 0.099 0.125 0.163 0.211 0.335
#>
#> V1 V2 V3 V4 V5
#> V1 1.000 0.096 0.130 0.072 0.049
#> V2 0.096 1.000 0.335 0.226 0.121
#> V3 0.130 0.335 1.000 0.325 0.163
#> V4 0.072 0.226 0.325 1.000 0.111
#> V5 0.049 0.121 0.163 0.111 1.000
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Data does not contain missing values. Continuing normally
#> Sample size after row-wise response data removal: 50000
#> $model_coefficients
#> a1 a2 d g u
#> V1 -1.1018 0.88341 4.499278 0 1
#> V2 -0.9022 0.07262 1.925188 0 1
#> V3 -1.1082 -0.12840 0.006985 0 1
#> V4 -1.0193 0.41052 -2.048156 0 1
#> V5 -0.9164 0.00000 -3.933192 0 1
#>
#> $model_coefficients_oblimin
#> .id a1 a2 d g u x F1 F2
#> V1 items -0.0306 1.43492 4.499278 0 1 NA NA NA
#> V2 items 0.7452 0.20124 1.925188 0 1 NA NA NA
#> V3 items 1.1728 -0.07842 0.006985 0 1 NA NA NA
#> V4 items 0.4531 0.71885 -2.048156 0 1 NA NA NA
#> V5 items 0.8442 0.09403 -3.933192 0 1 NA NA NA
#> 6 means NA NA NA NA NA 0 NA NA
#> 7 means NA NA NA NA NA 0 NA NA
#> 8 cov NA NA NA NA NA NA 1.0000 0.7453
#> 9 cov NA NA NA NA NA NA 0.7453 1.0000
#>
#> $model_options
#> Options
#> method EM
#> draws 5000
#> calcLL TRUE
#> SE FALSE
#> SE.type Oakes
#> verbose FALSE
#> SEtol 0.001
#> storeEMhistory FALSE
#> calcNull TRUE
#> odentype Gaussian
#> dentype Gaussian
#> zeroExtreme FALSE
#> accelerate Ramsay
#> Norder 2
#> delta 0.00001
#> Etable TRUE
#> plausible.draws 0
#> storeEtable TRUE
#> TOL 0.0001
#> omp_threads 1
#> PLCI FALSE
#> warn TRUE
#> message TRUE
#> technical.symmetric TRUE
#> technical.parallel TRUE
#> technical.omp TRUE
#> MAXQUAD 20000
#> NCYCLES 500
#> BURNIN 150
#> SEMCYCLES 100
#> SEM_from 0
#> SEM_to 0.999
#> KDRAWS 1
#> MHDRAWS 5
#> MHRM_SE_draws 2000
#> internal_constraints TRUE
#> keep_vcov_PD TRUE
#> theta_lim1 -6
#> theta_lim2 6
#> gain1 0.1
#> gain2 0.75
#> NULL.MODEL FALSE
#> USEEM TRUE
#> returnPrepList FALSE
#> Moptim BFGS
#> logLik_if_converged TRUE
#> info_if_converged TRUE
#> full FALSE
#> quadpts 31
#> exploratory TRUE
#>
#> $model_call
#> [1] "NULL"
#>
#> $q3_matrix
#> V1 V2 V3 V4 V5 min max
#> V1 NA NA NA NA NA NA NA
#> V2 0.09556 NA NA NA NA 0.09556 0.09556
#> V3 0.12957 0.3347 NA NA NA 0.12957 0.33469
#> V4 0.07209 0.2264 0.3248 NA NA 0.07209 0.32477
#> V5 0.04891 0.1212 0.1634 0.1107 NA 0.04891 0.16340
#> min 0.04891 0.1212 0.1634 0.1107 Inf 0.04891 0.09556
#> max 0.12957 0.3347 0.3248 0.1107 -Inf 0.12957 0.33469
#>
#> $exp_residuals
#> V1 V2 V3 V4 V5 freq exp std.res
#> 1 0 0 0 0 0 305 298.99064 0.34754
#> 2 0 0 0 0 1 1 1.67449 -0.52123
#> 3 0 0 0 1 0 10 7.65641 0.84697
#> 4 0 0 0 1 1 1 0.07827 3.29452
#> 5 0 0 1 0 0 69 79.69486 -1.19801
#> 6 0 0 1 0 1 1 0.87676 0.13162
#> 7 0 0 1 1 0 6 4.04001 0.97513
#> 8 0 1 0 0 0 555 562.31807 -0.30861
#> 9 0 1 0 0 1 5 5.42144 -0.18100
#> 10 0 1 0 1 0 26 26.70787 -0.13697
#> 11 0 1 1 0 0 301 287.83112 0.77621
#> 12 0 1 1 0 1 5 5.43978 -0.18856
#> 13 0 1 1 1 0 24 27.08968 -0.59362
#> 14 1 0 0 0 0 4448 4442.28505 0.08575
#> 15 1 0 0 0 1 50 47.06176 0.42831
#> 16 1 0 0 1 0 313 321.78634 -0.48981
#> 17 1 0 0 1 1 3 6.26901 -1.30562
#> 18 1 0 1 0 0 2296 2310.20859 -0.29561
#> 19 1 0 1 0 1 61 48.15865 1.85043
#> 20 1 0 1 1 0 343 333.24976 0.53411
#> 21 1 0 1 1 1 8 12.72686 -1.32499
#> 22 1 1 0 0 0 16581 16583.16247 -0.01679
#> 23 1 1 0 0 1 295 303.56829 -0.49177
#> 24 1 1 0 1 0 2253 2247.59256 0.11406
#> 25 1 1 0 1 1 84 75.30264 1.00226
#> 26 1 1 1 0 0 16618 16605.04869 0.10051
#> 27 1 1 1 0 1 588 597.06186 -0.37086
#> 28 1 1 1 1 0 4452 4462.52888 -0.15761
#> 29 1 1 1 1 1 298 294.67710 0.19357
#>
#> $item_fit
#> item S_X2 df.S_X2 RMSEA.S_X2 p.S_X2
#> 1 V1 NaN 0 NaN NaN
#> 2 V2 2.846 1 0.006 0.092
#> 3 V3 2.545 1 0.006 0.111
#> 4 V4 0.959 1 0.000 0.327
#> 5 V5 0.536 2 0.000 0.765
#>
#> $g2_fit
#> G2 p TLI CFI RMSEA df AIC BIC SABIC HQ logLik logPrior SElogLik
#> 1 20.35 0.2567 0.9982 0.9988 0.001985 17 178499 178623 178578 178538 -89236 0 0
#>
#> $m2_fit
#> M2 df p RMSEA RMSEA_5 RMSEA_95 SRMSR TLI CFI
#> stats 0.031 1 0.86 0 0 0.006 0.001 1.003 1
#>
report_irt(model=irt_threefactor,file="three_factors")
#> Q3 summary statistics:
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.050 0.106 0.126 0.164 0.214 0.339
#>
#> V1 V2 V3 V4 V5
#> V1 1.000 0.099 0.132 0.104 0.050
#> V2 0.099 1.000 0.295 0.232 0.112
#> V3 0.132 0.295 1.000 0.339 0.162
#> V4 0.104 0.232 0.339 1.000 0.119
#> V5 0.050 0.112 0.162 0.119 1.000
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Data does not contain missing values. Continuing normally
#> Sample size after row-wise response data removal: 50000
#> $model_coefficients
#> a1 a2 a3 d g u
#> V1 -4.1240 4.51446 5.3959 16.235542 0 1
#> V2 -0.9058 0.54128 -0.4518 2.060654 0 1
#> V3 -1.0271 0.03905 -0.1730 0.006835 0 1
#> V4 -0.9995 0.11428 0.0000 -1.995141 0 1
#> V5 -0.9396 0.00000 0.0000 -3.952170 0 1
#>
#> $model_coefficients_oblimin
#> .id a1 a2 a3 d g u x F1 F2 F3
#> V1 items 0.005699 0.01011 8.14833 16.235542 0 1 NA NA NA NA
#> V2 items 0.017266 1.12695 0.01710 2.060654 0 1 NA NA NA NA
#> V3 items 0.979659 0.12596 -0.09645 0.006835 0 1 NA NA NA NA
#> V4 items 0.908478 0.04810 0.11014 -1.995141 0 1 NA NA NA NA
#> V5 items 1.008980 -0.10254 0.02583 -3.952170 0 1 NA NA NA NA
#> 6 means NA NA NA NA NA NA 0 NA NA NA
#> 7 means NA NA NA NA NA NA 0 NA NA NA
#> 8 means NA NA NA NA NA NA 0 NA NA NA
#> 9 cov NA NA NA NA NA NA NA 1.0000 0.8189 0.4831
#> 10 cov NA NA NA NA NA NA NA 0.8189 1.0000 0.3830
#> 11 cov NA NA NA NA NA NA NA 0.4831 0.3830 1.0000
#>
#> $model_options
#> Options
#> method EM
#> draws 5000
#> calcLL TRUE
#> SE FALSE
#> SE.type Oakes
#> verbose FALSE
#> SEtol 0.001
#> storeEMhistory FALSE
#> calcNull TRUE
#> odentype Gaussian
#> dentype Gaussian
#> zeroExtreme FALSE
#> accelerate Ramsay
#> Norder 2
#> delta 0.00001
#> Etable TRUE
#> plausible.draws 0
#> storeEtable TRUE
#> TOL 0.0001
#> omp_threads 1
#> PLCI FALSE
#> warn TRUE
#> message TRUE
#> technical.symmetric TRUE
#> technical.parallel TRUE
#> technical.omp TRUE
#> MAXQUAD 20000
#> NCYCLES 500
#> BURNIN 150
#> SEMCYCLES 100
#> SEM_from 0
#> SEM_to 0.999
#> KDRAWS 1
#> MHDRAWS 5
#> MHRM_SE_draws 2000
#> internal_constraints TRUE
#> keep_vcov_PD TRUE
#> theta_lim1 -6
#> theta_lim2 6
#> gain1 0.1
#> gain2 0.75
#> NULL.MODEL FALSE
#> USEEM TRUE
#> returnPrepList FALSE
#> Moptim BFGS
#> logLik_if_converged TRUE
#> info_if_converged TRUE
#> full FALSE
#> quadpts 15
#> exploratory TRUE
#>
#> $model_call
#> [1] "NULL"
#>
#> $q3_matrix
#> V1 V2 V3 V4 V5 min max
#> V1 NA NA NA NA NA NA NA
#> V2 0.09922 NA NA NA NA 0.09922 0.09922
#> V3 0.13232 0.2949 NA NA NA 0.13232 0.29486
#> V4 0.10438 0.2316 0.3391 NA NA 0.10438 0.33907
#> V5 0.04996 0.1122 0.1619 0.1187 NA 0.04996 0.16186
#> min 0.04996 0.1122 0.1619 0.1187 Inf 0.04996 0.09922
#> max 0.13232 0.2949 0.3391 0.1187 -Inf 0.13232 0.33907
#>
#> $exp_residuals
#> V1 V2 V3 V4 V5 freq exp std.res
#> 1 0 0 0 0 0 305 295.24784 0.567555
#> 2 0 0 0 0 1 1 1.49283 -0.403356
#> 3 0 0 0 1 0 10 8.23627 0.614565
#> 4 0 0 0 1 1 1 0.07122 3.480127
#> 5 0 0 1 0 0 69 82.74123 -1.510653
#> 6 0 0 1 0 1 1 0.75791 0.278074
#> 7 0 0 1 1 0 6 4.25505 0.845920
#> 8 0 1 0 0 0 555 565.97169 -0.461186
#> 9 0 1 0 0 1 5 4.50817 0.231642
#> 10 0 1 0 1 0 26 26.46688 -0.090751
#> 11 0 1 1 0 0 301 289.21140 0.693194
#> 12 0 1 1 0 1 5 4.10673 0.440792
#> 13 0 1 1 1 0 24 24.54760 -0.110525
#> 14 1 0 0 0 0 4448 4445.21656 0.041748
#> 15 1 0 0 0 1 50 48.72538 0.182601
#> 16 1 0 0 1 0 313 326.08182 -0.724444
#> 17 1 0 0 1 1 3 6.69404 -1.427768
#> 18 1 0 1 0 0 2296 2300.88760 -0.101894
#> 19 1 0 1 0 1 61 48.06098 1.866402
#> 20 1 0 1 1 0 343 333.59763 0.514785
#> 21 1 0 1 1 1 8 12.98242 -1.382810
#> 22 1 1 0 0 0 16581 16577.05323 0.030654
#> 23 1 1 0 0 1 295 307.30589 -0.701984
#> 24 1 1 0 1 0 2253 2238.85833 0.298874
#> 25 1 1 0 1 1 84 78.00533 0.678739
#> 26 1 1 1 0 0 16618 16619.21850 -0.009452
#> 27 1 1 1 0 1 588 588.79187 -0.032634
#> 28 1 1 1 1 0 4452 4462.36353 -0.155141
#> 29 1 1 1 1 1 298 297.53892 0.026731
#>
#> $item_fit
#> item S_X2 df.S_X2 RMSEA.S_X2 p.S_X2
#> 1 V1 NaN 0 NaN NaN
#> 2 V2 NaN 0 NaN NaN
#> 3 V3 NaN 0 NaN NaN
#> 4 V4 0.852 1 0 0.356
#> 5 V5 0.345 2 0 0.842
#>
#> $g2_fit
#> G2 p TLI CFI RMSEA df AIC BIC SABIC HQ logLik logPrior SElogLik
#> 1 20.26 0.1222 0.9958 0.9978 0.00299 14 178505 178655 178601 178552 -89236 0 0
#>
#> $m2_fit
#> [1] "Error : Statistic cannot be calculated (too few degrees of freedom)\n"
#> attr(,"class")
#> [1] "try-error"
#> attr(,"condition")
#> <simpleError: Statistic cannot be calculated (too few degrees of freedom)>
#>