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GRShiny
GRM data simulation
Item parameters for graded response model
item_pars <- genIRTpar(nitem = 10, ncat = 3, nfac = 1)
Individual true latent traits
true_theta <- genTheta(nsample = 500, nfac = 1)
GRM data
grm_dt <- genData(eta = true_theta, ipar = item_pars)
GRM data simulation
Generate lavaan syntax
lav_syn <- genLavSyn(dat = grm_dt, nfac = 1)
#>
#> F1 =~ NA*y1+l1*y1+l2*y2+l3*y3+l4*y4+l5*y5+l6*y6+l7*y7+l8*y8+l9*y9+l10*y10
#>
#>
#> F1~~ 1*F1
#> F1~ 0*1
#> y1 | t11*t1;
#> y2 | t21*t1;
#> y3 | t31*t1;
#> y4 | t41*t1;
#> y5 | t51*t1;
#> y6 | t61*t1;
#> y7 | t71*t1;
#> y8 | t81*t1;
#> y9 | t91*t1;
#> y10 | t101*t1;
#> y1 | t12*t2;
#> y2 | t22*t2;
#> y3 | t32*t2;
#> y4 | t42*t2;
#> y5 | t52*t2;
#> y6 | t62*t2;
#> y7 | t72*t2;
#> y8 | t82*t2;
#> y9 | t92*t2;
#> y10 | t102*t2;
Conduct GRM with two different estimators
grm.fit <- runGRM(dat = grm_dt, lav.syntax = lav_syn, estimator = "WL")
Results
parameter estimates
extract_est(grm.fit)
#> lhs op rhs label est se z pvalue
#> 1 F1 =~ y1 l1 0.615 0.043 14.279 0.000
#> 2 F1 =~ y2 l2 0.775 0.035 22.118 0.000
#> 3 F1 =~ y3 l3 0.701 0.038 18.483 0.000
#> 4 F1 =~ y4 l4 0.545 0.048 11.293 0.000
#> 5 F1 =~ y5 l5 0.527 0.046 11.478 0.000
#> 6 F1 =~ y6 l6 0.584 0.044 13.215 0.000
#> 7 F1 =~ y7 l7 0.684 0.040 17.207 0.000
#> 8 F1 =~ y8 l8 0.761 0.035 21.499 0.000
#> 9 F1 =~ y9 l9 0.600 0.045 13.300 0.000
#> 10 F1 =~ y10 l10 0.661 0.040 16.436 0.000
#> 11 y1 | t1 t11 -0.126 0.056 -2.233 0.026
#> 12 y2 | t1 t21 -0.136 0.056 -2.412 0.016
#> 13 y3 | t1 t31 -0.161 0.056 -2.858 0.004
#> 14 y4 | t1 t41 -0.161 0.056 -2.858 0.004
#> 15 y5 | t1 t51 -0.202 0.057 -3.572 0.000
#> 16 y6 | t1 t61 -0.181 0.056 -3.215 0.001
#> 17 y7 | t1 t71 -0.222 0.057 -3.928 0.000
#> 18 y8 | t1 t81 -0.040 0.056 -0.715 0.475
#> 19 y9 | t1 t91 -0.100 0.056 -1.787 0.074
#> 20 y10 | t1 t101 -0.192 0.056 -3.393 0.001
#> 21 y1 | t2 t12 0.166 0.056 2.947 0.003
#> 22 y2 | t2 t22 -0.005 0.056 -0.089 0.929
#> 23 y3 | t2 t32 0.070 0.056 1.251 0.211
#> 24 y4 | t2 t42 0.121 0.056 2.144 0.032
#> 25 y5 | t2 t52 0.264 0.057 4.641 0.000
#> 26 y6 | t2 t62 0.181 0.056 3.215 0.001
#> 27 y7 | t2 t72 0.131 0.056 2.323 0.020
#> 28 y8 | t2 t82 0.105 0.056 1.876 0.061
#> 29 y9 | t2 t92 0.055 0.056 0.983 0.326
#> 30 y10 | t2 t102 0.141 0.056 2.501 0.012
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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