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Type 'q()' to quit R. > library(testthat) > library(betaSandwich) > > test_check("betaSandwich") Call: BetaADF(object = object) Standardized regression slopes with MVN standard errors: Call: BetaADF(object = object) Standardized regression slopes with MVN standard errors: Call: BetaHC(object = object, type = "hc0") Standardized regression slopes with HC0 standard errors: Call: BetaHC(object = object, type = "hc0") Standardized regression slopes with HC0 standard errors: Call: BetaHC(object = object, type = "hc1") Standardized regression slopes with HC1 standard errors: Call: BetaHC(object = object, type = "hc1") Standardized regression slopes with HC1 standard errors: Call: BetaHC(object = object, type = "hc2") Standardized regression slopes with HC2 standard errors: Call: BetaHC(object = object, type = "hc2") Standardized regression slopes with HC2 standard errors: Call: BetaHC(object = object, type = "hc3") Standardized regression slopes with HC3 standard errors: Call: BetaHC(object = object, type = "hc3") Standardized regression slopes with HC3 standard errors: Call: BetaHC(object = object, type = "hc4") Standardized regression slopes with HC4 standard errors: Call: BetaHC(object = object, type = "hc4") Standardized regression slopes with HC4 standard errors: Call: BetaHC(object = object, type = "hc4m") Standardized regression slopes with HC4M standard errors: Call: BetaHC(object = object, type = "hc4m") Standardized regression slopes with HC4M standard errors: Call: BetaHC(object = object, type = "hc5") Standardized regression slopes with HC5 standard errors: Call: BetaHC(object = object, type = "hc5") Standardized regression slopes with HC5 standard errors: Call: BetaHC(object = object) Standardized regression slopes with HC3 standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% NARTIC 0.4951 0.0786 6.3025 42 0.0000 0.2172 0.2832 0.3366 0.6537 0.7071 PCTGRT 0.3915 0.0818 4.7831 42 0.0000 0.1019 0.1707 0.2263 0.5567 0.6123 PCTSUPP 0.2632 0.0855 3.0786 42 0.0037 -0.0393 0.0325 0.0907 0.4358 0.4940 99.95% NARTIC 0.7731 PCTGRT 0.6810 PCTSUPP 0.5658 Call: BetaHC(object = object) Standardized regression slopes with HC3 standard errors: Call: BetaN(object = object) Standardized regression slopes with MVN standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% NARTIC 0.4951 0.0759 6.5272 42 0.000 0.2268 0.2905 0.3421 0.6482 0.6998 PCTGRT 0.3915 0.0770 5.0824 42 0.000 0.1190 0.1837 0.2360 0.5469 0.5993 PCTSUPP 0.2632 0.0747 3.5224 42 0.001 -0.0011 0.0616 0.1124 0.4141 0.4649 99.95% NARTIC 0.7635 PCTGRT 0.6640 PCTSUPP 0.5276 Call: BetaN(object = object) Standardized regression slopes with MVN standard errors: Call: BetaADF(object = object) Standardized regression slopes with MVN standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% NARTIC 0.4951 0.0674 7.3490 42 0.0000 0.2568 0.3134 0.3592 0.6311 0.6769 PCTGRT 0.3915 0.0710 5.5164 42 0.0000 0.1404 0.2000 0.2483 0.5347 0.5830 PCTSUPP 0.2632 0.0769 3.4231 42 0.0014 -0.0088 0.0558 0.1081 0.4184 0.4707 99.95% NARTIC 0.7335 PCTGRT 0.6426 PCTSUPP 0.5353 Call: BetaADF(object = object) Standardized regression slopes with MVN standard errors: Call: BetaHC(object = object) Standardized regression slopes with HC3 standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.7622 0.0645 11.8222 44 0 0.5349 0.5886 0.6322 0.8921 0.9357 0.9895 Call: BetaHC(object = object) Standardized regression slopes with HC3 standard errors: Call: BetaN(object = object) Standardized regression slopes with MVN standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.7622 0.0618 12.3341 44 0 0.5443 0.5958 0.6376 0.8867 0.9285 0.98 Call: BetaN(object = object) Standardized regression slopes with MVN standard errors: Call: BetaADF(object = object) Standardized regression slopes with MVN standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% 99.95% NARTIC 0.7622 0.0604 12.625 44 0 0.5493 0.5996 0.6405 0.8838 0.9247 0.975 Call: BetaADF(object = object) Standardized regression slopes with MVN standard errors: Call: BetaN(object = object) Standardized regression slopes with MVN standard errors: Call: BetaN(object = object) Standardized regression slopes with MVN standard errors: Call: DiffBetaSandwich(object = BetaN(object)) Difference between standardized regression coefficients with MVN standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1357 0.7640 0.4449 -0.3428 -0.2458 -0.1623 0.3696 NARTIC-PCTSUPP 0.2319 0.1252 1.8524 0.0640 -0.1800 -0.0906 -0.0135 0.4773 PCTGRT-PCTSUPP 0.1282 0.1227 1.0451 0.2960 -0.2755 -0.1878 -0.1123 0.3688 99.5% 99.95% NARTIC-PCTGRT 0.4531 0.5501 NARTIC-PCTSUPP 0.5544 0.6438 PCTGRT-PCTSUPP 0.4443 0.5320 Call: DiffBetaSandwich(object = BetaN(object)) Difference between standardized regression coefficients with MVN standard errors: Call: DiffBetaSandwich(object = BetaADF(object)) Difference between standardized regression coefficients with MVN standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1212 0.8555 0.3923 -0.2950 -0.2084 -0.1338 0.3411 NARTIC-PCTSUPP 0.2319 0.1181 1.9642 0.0495 -0.1566 -0.0722 0.0005 0.4633 PCTGRT-PCTSUPP 0.1282 0.1215 1.0555 0.2912 -0.2716 -0.1847 -0.1099 0.3664 99.5% 99.95% NARTIC-PCTGRT 0.4158 0.5024 NARTIC-PCTSUPP 0.5360 0.6204 PCTGRT-PCTSUPP 0.4412 0.5281 Call: DiffBetaSandwich(object = BetaADF(object)) Difference between standardized regression coefficients with MVN standard errors: Call: DiffBetaSandwich(object = BetaHC(object, type = "hc0")) Difference between standardized regression coefficients with HC0 standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1201 0.8629 0.3882 -0.2916 -0.2058 -0.1318 0.3391 NARTIC-PCTSUPP 0.2319 0.1169 1.9840 0.0473 -0.1527 -0.0692 0.0028 0.4610 PCTGRT-PCTSUPP 0.1282 0.1201 1.0674 0.2858 -0.2671 -0.1812 -0.1072 0.3637 99.5% 99.95% NARTIC-PCTGRT 0.4131 0.4989 NARTIC-PCTSUPP 0.5330 0.6165 PCTGRT-PCTSUPP 0.4377 0.5236 Call: DiffBetaSandwich(object = BetaHC(object, type = "hc0")) Difference between standardized regression coefficients with HC0 standard errors: Call: DiffBetaSandwich(object = BetaHC(object, type = "hc1")) Difference between standardized regression coefficients with HC1 standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1257 0.8245 0.4097 -0.3100 -0.2202 -0.1427 0.3501 NARTIC-PCTSUPP 0.2319 0.1223 1.8958 0.0580 -0.1706 -0.0832 -0.0078 0.4716 PCTGRT-PCTSUPP 0.1282 0.1257 1.0199 0.3078 -0.2855 -0.1956 -0.1182 0.3747 99.5% 99.95% NARTIC-PCTGRT 0.4275 0.5173 NARTIC-PCTSUPP 0.5470 0.6344 PCTGRT-PCTSUPP 0.4521 0.5420 Call: DiffBetaSandwich(object = BetaHC(object, type = "hc1")) Difference between standardized regression coefficients with HC1 standard errors: Call: DiffBetaSandwich(object = BetaHC(object, type = "hc2")) Difference between standardized regression coefficients with HC2 standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1302 0.7960 0.4260 -0.3248 -0.2318 -0.1516 0.3589 NARTIC-PCTSUPP 0.2319 0.1240 1.8704 0.0614 -0.1761 -0.0875 -0.0111 0.4749 PCTGRT-PCTSUPP 0.1282 0.1284 0.9990 0.3178 -0.2942 -0.2024 -0.1234 0.3798 99.5% 99.95% NARTIC-PCTGRT 0.4391 0.5321 NARTIC-PCTSUPP 0.5513 0.6399 PCTGRT-PCTSUPP 0.4589 0.5506 Call: DiffBetaSandwich(object = BetaHC(object, type = "hc2")) Difference between standardized regression coefficients with HC2 standard errors: Call: DiffBetaSandwich(object = BetaHC(object, type = "hc3")) Difference between standardized regression coefficients with HC3 standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1417 0.7316 0.4644 -0.3626 -0.2613 -0.1741 0.3814 NARTIC-PCTSUPP 0.2319 0.1318 1.7595 0.0785 -0.2018 -0.1076 -0.0264 0.4902 PCTGRT-PCTSUPP 0.1282 0.1375 0.9329 0.3509 -0.3241 -0.2259 -0.1412 0.3977 99.5% 99.95% NARTIC-PCTGRT 0.4686 0.5699 NARTIC-PCTSUPP 0.5714 0.6656 PCTGRT-PCTSUPP 0.4823 0.5806 Call: DiffBetaSandwich(object = BetaHC(object, type = "hc3")) Difference between standardized regression coefficients with HC3 standard errors: Call: DiffBetaSandwich(object = BetaHC(object, type = "hc4")) Difference between standardized regression coefficients with HC4 standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1452 0.7138 0.4753 -0.3742 -0.2704 -0.1809 0.3883 NARTIC-PCTSUPP 0.2319 0.1296 1.7892 0.0736 -0.1946 -0.1020 -0.0221 0.4859 PCTGRT-PCTSUPP 0.1282 0.1361 0.9420 0.3462 -0.3197 -0.2224 -0.1386 0.3951 99.5% 99.95% NARTIC-PCTGRT 0.4777 0.5815 NARTIC-PCTSUPP 0.5658 0.6584 PCTGRT-PCTSUPP 0.4789 0.5762 Call: DiffBetaSandwich(object = BetaHC(object, type = "hc4")) Difference between standardized regression coefficients with HC4 standard errors: Call: DiffBetaSandwich(object = BetaHC(object, type = "hc4m")) Difference between standardized regression coefficients with HC4M standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1465 0.7077 0.4791 -0.3783 -0.2736 -0.1834 0.3907 NARTIC-PCTSUPP 0.2319 0.1338 1.7331 0.0831 -0.2084 -0.1128 -0.0304 0.4941 PCTGRT-PCTSUPP 0.1282 0.1406 0.9123 0.3616 -0.3343 -0.2338 -0.1473 0.4037 99.5% 99.95% NARTIC-PCTGRT 0.4809 0.5856 NARTIC-PCTSUPP 0.5766 0.6722 PCTGRT-PCTSUPP 0.4903 0.5908 Call: DiffBetaSandwich(object = BetaHC(object, type = "hc4m")) Difference between standardized regression coefficients with HC4M standard errors: Call: DiffBetaSandwich(object = BetaHC(object, type = "hc5")) Difference between standardized regression coefficients with HC5 standard errors: est se z p 0.05% 0.5% 2.5% 97.5% NARTIC-PCTGRT 0.1037 0.1312 0.7899 0.4296 -0.3282 -0.2344 -0.1536 0.3609 NARTIC-PCTSUPP 0.2319 0.1227 1.8906 0.0587 -0.1717 -0.0841 -0.0085 0.4723 PCTGRT-PCTSUPP 0.1282 0.1274 1.0067 0.3141 -0.2909 -0.1999 -0.1214 0.3779 99.5% 99.95% NARTIC-PCTGRT 0.4417 0.5355 NARTIC-PCTSUPP 0.5478 0.6355 PCTGRT-PCTSUPP 0.4564 0.5474 Call: DiffBetaSandwich(object = BetaHC(object, type = "hc5")) Difference between standardized regression coefficients with HC5 standard errors: Call: RSqBetaSandwich(object = BetaN(object)) Multiple correlation with MVN standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% rsq 0.8045 4.1345 0.1946 42 0.8467 -13.8224 -10.3507 -7.5393 9.1483 11.9598 adj 0.7906 4.4299 0.1785 42 0.8592 -14.8811 -11.1615 -8.1492 9.7304 12.7426 99.95% rsq 15.4314 adj 16.4622 Call: RSqBetaSandwich(object = BetaN(object)) Multiple correlation with MVN standard errors: Call: RSqBetaSandwich(object = BetaADF(object)) Multiple correlation with MVN standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% rsq 0.8045 3.6172 0.2224 42 0.8251 -11.9923 -8.9550 -6.4953 8.1044 10.5640 adj 0.7906 3.8756 0.2040 42 0.8394 -12.9203 -9.6661 -7.0307 8.6118 11.2472 99.95% rsq 13.6014 adj 14.5015 Call: RSqBetaSandwich(object = BetaADF(object)) Multiple correlation with MVN standard errors: Call: RSqBetaSandwich(object = BetaHC(object)) Multiple correlation with HC3 standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% rsq 0.8045 3.9483 0.2038 42 0.8395 -13.1636 -9.8483 -7.1635 8.7725 11.4573 adj 0.7906 4.2303 0.1869 42 0.8527 -14.1753 -10.6231 -7.7466 9.3277 12.2043 99.95% rsq 14.7726 adj 15.7564 Call: RSqBetaSandwich(object = BetaHC(object)) Multiple correlation with HC3 standard errors: Call: RSqBetaSandwich(object = BetaN(object)) Multiple correlation with MVN standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% rsq 0.5809 8.8646 0.0655 44 0.948 -30.6739 -23.285 -17.2845 18.4463 24.4468 adj 0.5714 9.0661 0.0630 44 0.950 -31.3937 -23.837 -17.7001 18.8428 24.9797 99.95% rsq 31.8357 adj 32.5365 Call: RSqBetaSandwich(object = BetaN(object)) Multiple correlation with MVN standard errors: Call: RSqBetaSandwich(object = BetaADF(object)) Multiple correlation with MVN standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% rsq 0.5809 8.5690 0.0678 44 0.9463 -29.6315 -22.4891 -16.6887 17.8505 23.6509 adj 0.5714 8.7637 0.0652 44 0.9483 -30.3277 -23.0230 -17.0907 18.2335 24.1657 99.95% rsq 30.7933 adj 31.4704 Call: RSqBetaSandwich(object = BetaADF(object)) Multiple correlation with MVN standard errors: Call: RSqBetaSandwich(object = BetaHC(object)) Multiple correlation with HC3 standard errors: est se t df p 0.05% 0.5% 2.5% 97.5% 99.5% rsq 0.5809 8.9208 0.0651 44 0.9484 -30.8720 -23.4363 -17.3978 18.5596 24.5981 adj 0.5714 9.1235 0.0626 44 0.9503 -31.5964 -23.9917 -17.8159 18.9586 25.1344 99.95% rsq 32.0338 adj 32.7391 Call: RSqBetaSandwich(object = BetaHC(object)) Multiple correlation with HC3 standard errors: Call: RSqBetaSandwich(object = BetaN(object)) Multiple correlation with MVN standard errors: Call: RSqBetaSandwich(object = BetaADF(object)) Multiple correlation with MVN standard errors: Call: RSqBetaSandwich(object = BetaHC(object)) Multiple correlation with HC3 standard errors: Call: RSqBetaSandwich(object = BetaN(object)) Multiple correlation with MVN standard errors: Call: RSqBetaSandwich(object = BetaADF(object)) Multiple correlation with MVN standard errors: Call: RSqBetaSandwich(object = BetaHC(object)) Multiple correlation with HC3 standard errors: 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 54 ] > > proc.time() user system elapsed 1.01 0.18 1.14