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Type 'q()' to quit R. > library(testthat) > library(betaMC) > > test_check("betaMC") Call: BetaMC(object = mc) Standardized regression slopes type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.4951 0.0549 5 0.4814 0.4818 0.4832 0.6107 0.6140 0.6147 PCTGRT 0.3915 0.0766 5 0.3453 0.3460 0.3488 0.5310 0.5424 0.5450 PCTSUPP 0.2632 0.0440 5 0.1311 0.1316 0.1336 0.2273 0.2274 0.2275 Call: BetaMC(object = mc) Standardized regression slopes type = "mvn" Call: BetaMC(object = mc) Standardized regression slopes type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.7622 0.0729 5 0.6586 0.661 0.6716 0.8502 0.8545 0.8555 Call: BetaMC(object = mc) Standardized regression slopes type = "mvn" Call: BetaMC(object = mc) Standardized regression slopes type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.4951 0.0821 5 0.4733 0.4740 0.4773 0.6793 0.6879 0.6898 PCTGRT 0.3915 0.0936 5 0.1733 0.1757 0.1866 0.4161 0.4198 0.4206 PCTSUPP 0.2632 0.0509 5 0.1624 0.1628 0.1646 0.2860 0.2913 0.2925 Call: BetaMC(object = mc) Standardized regression slopes type = "mvn" Call: BetaMC(object = mc) Standardized regression slopes type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.7622 0.0662 5 0.6361 0.6385 0.6491 0.7956 0.7965 0.7967 Call: BetaMC(object = mc) Standardized regression slopes type = "mvn" Call: DeltaRSqMC(object = mc) Improvement in R-squared type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.1859 0.0695 5 0.1164 0.1164 0.1166 0.2682 0.2790 0.2814 PCTGRT 0.1177 0.0100 5 0.0887 0.0889 0.0900 0.1122 0.1122 0.1123 PCTSUPP 0.0569 0.0498 5 0.0143 0.0146 0.0162 0.1228 0.1230 0.1230 Call: DeltaRSqMC(object = mc) Improvement in R-squared type = "mvn" Call: DeltaRSqMC(object = mc) Improvement in R-squared type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.1859 0.0220 5 0.1377 0.1382 0.1402 0.1918 0.1923 0.1924 PCTGRT 0.1177 0.0270 5 0.0520 0.0526 0.0554 0.1213 0.1223 0.1226 PCTSUPP 0.0569 0.0362 5 0.0161 0.0162 0.0168 0.0975 0.0993 0.0996 Call: DeltaRSqMC(object = mc) Improvement in R-squared type = "mvn" Call: DiffBetaMC(object = mc) Differences of standardized regression slopes type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC-PCTGRT 0.1037 0.1338 5 -0.1372 -0.1369 -0.1354 0.1682 0.1862 0.1903 NARTIC-PCTSUPP 0.2319 0.1096 5 0.0928 0.0932 0.0948 0.3465 0.3530 0.3545 PCTGRT-PCTSUPP 0.1282 0.1096 5 0.1471 0.1474 0.1487 0.3996 0.4081 0.4100 Call: DiffBetaMC(object = mc) Differences of standardized regression slopes type = "mvn" Call: DiffBetaMC(object = mc) Differences of standardized regression slopes type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC-PCTGRT 0.1037 0.0978 5 -0.0687 -0.0672 -0.0605 0.1843 0.1960 0.1987 NARTIC-PCTSUPP 0.2319 0.0885 5 0.0463 0.0489 0.0603 0.2816 0.2869 0.2882 PCTGRT-PCTSUPP 0.1282 0.0882 5 0.0274 0.0285 0.0335 0.2491 0.2556 0.2571 Call: DiffBetaMC(object = mc) Differences of standardized regression slopes type = "mvn" MCMI(object = object, mi = mi, R = R, type = "mvn", fixed_x = TRUE) MCMI(object = object, mi = mi, R = R, type = "adf", fixed_x = TRUE) MCMI(object = object, mi = mi, R = R, type = "hc0", fixed_x = TRUE) MCMI(object = object, mi = mi, R = R, type = "hc1", fixed_x = TRUE) MCMI(object = object, mi = mi, R = R, type = "hc2", fixed_x = TRUE) MCMI(object = object, mi = mi, R = R, type = "hc3", fixed_x = TRUE) MCMI(object = object, mi = mi, R = R, type = "hc4", fixed_x = TRUE) MCMI(object = object, mi = mi, R = R, type = "hc4m", fixed_x = TRUE) MCMI(object = object, mi = mi, R = R, type = "hc5", fixed_x = TRUE) MC(object = object, R = R, type = "mvn", fixed_x = TRUE) MC(object = object, R = R, type = "adf", fixed_x = TRUE) MC(object = object, R = R, type = "hc0", fixed_x = TRUE) MC(object = object, R = R, type = "hc1", fixed_x = TRUE) MC(object = object, R = R, type = "hc2", fixed_x = TRUE) MC(object = object, R = R, type = "hc3", fixed_x = TRUE) MC(object = object, R = R, type = "hc4", fixed_x = TRUE) MC(object = object, R = R, type = "hc4m", fixed_x = TRUE) MC(object = object, R = R, type = "hc5", fixed_x = TRUE) Call: MC(object = object, R = 5L, decomposition = "chol", fixed_x = TRUE) The first set of simulated parameter estimates and model-implied covariance matrix. $coef [1] 0.5030514 0.5632128 $sigmasq [1] 0.565457 $vechsigmacapx [1] 1.000000e+00 -2.950685e-16 1.000000e+00 $sigmacapx [,1] [,2] [1,] 1.000000e+00 -2.950685e-16 [2,] -2.950685e-16 1.000000e+00 $sigmaysq [1] 1.135726 $sigmayx [1] 0.5030514 0.5632128 $sigmacap [,1] [,2] [,3] [1,] 1.1357264 5.030514e-01 5.632128e-01 [2,] 0.5030514 1.000000e+00 -2.950685e-16 [3,] 0.5632128 -2.950685e-16 1.000000e+00 $pd [1] TRUE Call: MC(object = object, R = 5L, decomposition = "svd", fixed_x = TRUE) The first set of simulated parameter estimates and model-implied covariance matrix. $coef [1] 0.5650920 0.5101143 $sigmasq [1] 0.5131815 $vechsigmacapx [1] 1.000000e+00 -2.950685e-16 1.000000e+00 $sigmacapx [,1] [,2] [1,] 1.000000e+00 -2.950685e-16 [2,] -2.950685e-16 1.000000e+00 $sigmaysq [1] 1.092727 $sigmayx [1] 0.5650920 0.5101143 $sigmacap [,1] [,2] [,3] [1,] 1.0927271 5.650920e-01 5.101143e-01 [2,] 0.5650920 1.000000e+00 -2.950685e-16 [3,] 0.5101143 -2.950685e-16 1.000000e+00 $pd [1] TRUE MCMI(object = object, mi = mi, R = R, type = "mvn") MCMI(object = object, mi = mi, R = R, type = "adf") MCMI(object = object, mi = mi, R = R, type = "hc0") MCMI(object = object, mi = mi, R = R, type = "hc1") MCMI(object = object, mi = mi, R = R, type = "hc2") MCMI(object = object, mi = mi, R = R, type = "hc3") MCMI(object = object, mi = mi, R = R, type = "hc4") MCMI(object = object, mi = mi, R = R, type = "hc4m") MCMI(object = object, mi = mi, R = R, type = "hc5") MC(object = object, R = R, type = "mvn") MC(object = object, R = R, type = "adf") MC(object = object, R = R, type = "hc0") MC(object = object, R = R, type = "hc1") MC(object = object, R = R, type = "hc2") MC(object = object, R = R, type = "hc3") MC(object = object, R = R, type = "hc4") MC(object = object, R = R, type = "hc4m") MC(object = object, R = R, type = "hc5") Call: MC(object = object, R = 5L, decomposition = "chol") The first set of simulated parameter estimates and model-implied covariance matrix. $coef [1] 0.4716786 0.4818254 $sigmasq [1] 0.4979227 $vechsigmacapx [1] 1.00973789 0.03790098 1.03016649 $sigmacapx [,1] [,2] [1,] 1.00973789 0.03790098 [2,] 0.03790098 1.03016649 $sigmaysq [1] 0.9789562 $sigmayx [1] 0.4945334 0.5142374 $sigmacap [,1] [,2] [,3] [1,] 0.9789562 0.49453344 0.51423741 [2,] 0.4945334 1.00973789 0.03790098 [3,] 0.5142374 0.03790098 1.03016649 $pd [1] TRUE Call: MC(object = object, R = 5L, decomposition = "svd") The first set of simulated parameter estimates and model-implied covariance matrix. $coef [1] 0.5032395 0.4749796 $sigmasq [1] 0.545411 $vechsigmacapx [1] 0.96601972 -0.01260221 0.99277832 $sigmacapx [,1] [,2] [1,] 0.96601972 -0.01260221 [2,] -0.01260221 0.99277832 $sigmaysq [1] 1.008007 $sigmayx [1] 0.4801534 0.4652075 $sigmacap [,1] [,2] [,3] [1,] 1.0080071 0.48015344 0.46520748 [2,] 0.4801534 0.96601972 -0.01260221 [3,] 0.4652075 -0.01260221 0.99277832 $pd [1] TRUE Call: PCorMC(object = mc) Squared partial correlations type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.4874 0.1363 5 0.2134 0.2159 0.2273 0.5727 0.5834 0.5858 PCTGRT 0.3757 0.0544 5 0.2703 0.2703 0.2703 0.3742 0.3749 0.3750 PCTSUPP 0.2254 0.0644 5 0.0558 0.0573 0.0643 0.2198 0.2222 0.2227 Call: PCorMC(object = mc) Squared partial correlations type = "mvn" Call: PCorMC(object = mc) Squared partial correlations type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.4874 0.0627 5 0.4037 0.4056 0.4138 0.5652 0.5671 0.5676 PCTGRT 0.3757 0.0762 5 0.2286 0.2288 0.2297 0.4042 0.4089 0.4099 PCTSUPP 0.2254 0.0990 5 0.1159 0.1183 0.1292 0.3777 0.3835 0.3848 Call: PCorMC(object = mc) Squared partial correlations type = "mvn" Call: RSqMC(object = mc) R-squared and adjusted R-squared type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% rsq 0.8045 0.0339 5 0.7688 0.7689 0.7696 0.8422 0.8431 0.8433 adj 0.7906 0.0364 5 0.7523 0.7524 0.7531 0.8309 0.8319 0.8321 Call: RSqMC(object = mc) R-squared and adjusted R-squared type = "mvn" Call: RSqMC(object = mc) R-squared and adjusted R-squared type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% rsq 0.5809 0.0375 5 0.5327 0.5331 0.5347 0.6231 0.6256 0.6261 adj 0.5714 0.0384 5 0.5221 0.5225 0.5242 0.6145 0.6171 0.6176 Call: RSqMC(object = mc) R-squared and adjusted R-squared type = "mvn" Call: RSqMC(object = mc) R-squared and adjusted R-squared type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% rsq 0.8045 0.0617 5 0.7386 0.7387 0.7388 0.8800 0.8851 0.8863 adj 0.7906 0.0661 5 0.7200 0.7200 0.7202 0.8714 0.8769 0.8782 Call: RSqMC(object = mc) R-squared and adjusted R-squared type = "mvn" Call: RSqMC(object = mc) R-squared and adjusted R-squared type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% rsq 0.5809 0.0833 5 0.3983 0.3985 0.3996 0.5925 0.5986 0.6000 adj 0.5714 0.0852 5 0.3846 0.3849 0.3860 0.5833 0.5895 0.5909 Call: RSqMC(object = mc) R-squared and adjusted R-squared type = "mvn" Call: SCorMC(object = mc) Semipartial correlations type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.4312 0.0537 5 0.3688 0.3692 0.3712 0.4988 0.5022 0.5030 PCTGRT 0.3430 0.0802 5 0.1841 0.1848 0.1878 0.3695 0.3716 0.3721 PCTSUPP 0.2385 0.0454 5 0.1739 0.1748 0.1789 0.2942 0.2980 0.2989 Call: SCorMC(object = mc) Semipartial correlations type = "mvn" Call: SCorMC(object = mc) Semipartial correlations type = "mvn" est se R 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.4312 0.0828 5 0.2157 0.2180 0.2285 0.4096 0.4097 0.4098 PCTGRT 0.3430 0.0516 5 0.3013 0.3015 0.3023 0.4220 0.4259 0.4267 PCTSUPP 0.2385 0.0418 5 0.1865 0.1868 0.1883 0.2809 0.2822 0.2825 Call: SCorMC(object = mc) Semipartial correlations type = "mvn" beta1 beta2 beta3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmaysq 909.1981 257.2976 276.0367 1 0.007091036 0.03637752 0.01896371 sigmayx1 3507.1691 471.2058 510.5430 0 0.084208291 0.21599726 0.11260003 sigmayx2 471.2058 333.2295 150.9121 0 0.000000000 0.08420829 0.00000000 sigmayx3 510.5430 150.9121 554.4386 0 0.000000000 0.00000000 0.08420829 sigmax1x1 0.0000 0.0000 0.0000 0 1.000000000 0.00000000 0.00000000 sigmax2x1 0.0000 0.0000 0.0000 0 0.000000000 1.00000000 0.00000000 sigmax3x1 0.0000 0.0000 0.0000 0 0.000000000 0.00000000 1.00000000 sigmax2x2 0.0000 0.0000 0.0000 0 0.000000000 0.00000000 0.00000000 sigmax3x2 0.0000 0.0000 0.0000 0 0.000000000 0.00000000 0.00000000 sigmax3x3 0.0000 0.0000 0.0000 0 0.000000000 0.00000000 0.00000000 sigmax2x2 sigmax3x2 sigmax3x3 sigmaysq 0.04665482 0.0486426 0.01267877 sigmayx1 0.00000000 0.0000000 0.00000000 sigmayx2 0.21599726 0.1126000 0.00000000 sigmayx3 0.00000000 0.2159973 0.11260003 sigmax1x1 0.00000000 0.0000000 0.00000000 sigmax2x1 0.00000000 0.0000000 0.00000000 sigmax3x1 0.00000000 0.0000000 0.00000000 sigmax2x2 1.00000000 0.0000000 0.00000000 sigmax3x2 0.00000000 1.0000000 0.00000000 sigmax3x3 0.00000000 0.0000000 1.00000000 beta1 beta2 beta3 sigmasq sigmaysq 909.1981 257.2976 276.0367 1 sigmayx1 3507.1691 471.2058 510.5430 0 sigmayx2 471.2058 333.2295 150.9121 0 sigmayx3 510.5430 150.9121 554.4386 0 sigmax1x1 0.0000 0.0000 0.0000 0 sigmax2x1 0.0000 0.0000 0.0000 0 sigmax3x1 0.0000 0.0000 0.0000 0 sigmax2x2 0.0000 0.0000 0.0000 0 sigmax3x2 0.0000 0.0000 0.0000 0 sigmax3x3 0.0000 0.0000 0.0000 0 beta1 beta2 beta3 rsq sigmax1x1 sigmax2x1 sigmaysq 909.1981 257.2976 276.0367 -126.0843 0.007091036 0.03637752 sigmayx1 3507.1691 471.2058 510.5430 0.0000 0.084208291 0.21599726 sigmayx2 471.2058 333.2295 150.9121 0.0000 0.000000000 0.08420829 sigmayx3 510.5430 150.9121 554.4386 0.0000 0.000000000 0.00000000 sigmax1x1 0.0000 0.0000 0.0000 0.0000 1.000000000 0.00000000 sigmax2x1 0.0000 0.0000 0.0000 0.0000 0.000000000 1.00000000 sigmax3x1 0.0000 0.0000 0.0000 0.0000 0.000000000 0.00000000 sigmax2x2 0.0000 0.0000 0.0000 0.0000 0.000000000 0.00000000 sigmax3x2 0.0000 0.0000 0.0000 0.0000 0.000000000 0.00000000 sigmax3x3 0.0000 0.0000 0.0000 0.0000 0.000000000 0.00000000 sigmax3x1 sigmax2x2 sigmax3x2 sigmax3x3 sigmaysq 0.01896371 0.04665482 0.0486426 0.01267877 sigmayx1 0.11260003 0.00000000 0.0000000 0.00000000 sigmayx2 0.00000000 0.21599726 0.1126000 0.00000000 sigmayx3 0.08420829 0.00000000 0.2159973 0.11260003 sigmax1x1 0.00000000 0.00000000 0.0000000 0.00000000 sigmax2x1 0.00000000 0.00000000 0.0000000 0.00000000 sigmax3x1 1.00000000 0.00000000 0.0000000 0.00000000 sigmax2x2 0.00000000 1.00000000 0.0000000 0.00000000 sigmax3x2 0.00000000 0.00000000 1.0000000 0.00000000 sigmax3x3 0.00000000 0.00000000 0.0000000 1.00000000 beta1 beta2 beta3 rsq sigmaysq 909.1981 257.2976 276.0367 -126.0843 sigmayx1 3507.1691 471.2058 510.5430 0.0000 sigmayx2 471.2058 333.2295 150.9121 0.0000 sigmayx3 510.5430 150.9121 554.4386 0.0000 sigmax1x1 0.0000 0.0000 0.0000 0.0000 sigmax2x1 0.0000 0.0000 0.0000 0.0000 sigmax3x1 0.0000 0.0000 0.0000 0.0000 sigmax2x2 0.0000 0.0000 0.0000 0.0000 sigmax3x2 0.0000 0.0000 0.0000 0.0000 sigmax3x3 0.0000 0.0000 0.0000 0.0000 [ FAIL 0 | WARN 0 | SKIP 0 | PASS 91 ] > > proc.time() user system elapsed 6.14 0.54 7.12