Extract Variance-Covariance Matrix from robcov_vglm Objects
Source:R/robcov_vglm.R
vcov.robcov_vglm.RdReturns the robust (sandwich) variance-covariance matrix.
Usage
# S3 method for class 'robcov_vglm'
vcov(object, ...)Examples
trial <- sim_markov("actt2", n_patients = 40, follow_up_time = 6, seed = 1)
markov_data <- prepare_markov_data(trial, absorbing_state = 8)
fit <- vglm_markov(
ordered(y) ~ time + tx + yprev,
family = VGAM::cumulative(reverse = TRUE, parallel = TRUE),
data = markov_data
)
#> Warning: `id_var` was not supplied to vglm_markov(); stored patient-ID metadata, cluster-robust standard errors for frequentist wrappers, and automatic FWB refits will not be available from the fitted object.
#> Warning: 12 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12
#> Warning: 76 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12
#> Warning: 77 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12
#> Warning: 128 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12
#> Warning: 151 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: 151 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: 151 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: 151 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: fitted values close to 0 or 1
#> Warning: It seems that the nonparallelism assumption has resulted in intersecting linear/additive predictors. Try propodds() or fitting a partial nonproportional odds model or choosing some other link function, etc.
#> Warning: some quantities such as z, residuals, SEs may be inaccurate due to convergence at a half-step
robust_fit <- robcov_vglm(fit, cluster = markov_data$id)
vcov(robust_fit)
#> (Intercept):1 (Intercept):2 (Intercept):3 (Intercept):4
#> (Intercept):1 0.68157900 0.31176163 0.26230659 0.23822018
#> (Intercept):2 0.31176163 4.03879747 4.20659911 3.82953330
#> (Intercept):3 0.26230659 4.20659911 4.40345743 4.01803312
#> (Intercept):4 0.23822018 3.82953330 4.01803312 3.84656546
#> (Intercept):5 0.20657003 3.70333394 3.89686381 3.74445463
#> (Intercept):6 0.15872992 3.59743779 3.79413708 3.65741137
#> time -0.06078981 -0.10888022 -0.10699996 -0.09244885
#> tx -0.10376223 -0.09946213 -0.09274708 -0.08856369
#> yprev2 -0.28303472 0.44322398 0.49716008 0.46347378
#> yprev3 -0.21053880 -1.74917522 -1.82070427 -1.66796499
#> yprev4 0.05278025 -3.52276636 -3.72869292 -3.53221128
#> yprev5 0.02958509 -3.28199059 -3.48008139 -3.36473017
#> yprev6 0.11544661 -3.19410049 -3.39998133 -3.30644674
#> yprev7 0.12770816 -3.10729919 -3.31593107 -3.24189693
#> (Intercept):5 (Intercept):6 time tx yprev2
#> (Intercept):1 0.20657003 0.15872992 -0.060789808 -0.103762227 -0.28303472
#> (Intercept):2 3.70333394 3.59743779 -0.108880224 -0.099462127 0.44322398
#> (Intercept):3 3.89686381 3.79413708 -0.106999957 -0.092747077 0.49716008
#> (Intercept):4 3.74445463 3.65741137 -0.092448846 -0.088563695 0.46347378
#> (Intercept):5 3.93573311 3.91604813 -0.086272674 -0.097674786 0.47124197
#> (Intercept):6 3.91604813 4.26490634 -0.079605250 -0.065155339 0.49632743
#> time -0.08627267 -0.07960525 0.012361390 0.004984025 0.02846527
#> tx -0.09767479 -0.06515534 0.004984025 0.102260603 0.03709325
#> yprev2 0.47124197 0.49632743 0.028465270 0.037093249 0.76673310
#> yprev3 -1.59845770 -1.57411131 0.042766802 -0.015600481 -0.20258334
#> yprev4 -3.45624453 -3.40171875 0.051938106 0.004891656 -0.66394035
#> yprev5 -3.31803555 -3.25262820 0.044041084 0.023766687 -0.54470888
#> yprev6 -3.48921890 -3.70389836 0.033639714 -0.014641639 -0.61150564
#> yprev7 -3.55391896 -3.91529748 0.032375606 -0.039556167 -0.62647700
#> yprev3 yprev4 yprev5 yprev6 yprev7
#> (Intercept):1 -0.21053880 0.052780252 0.02958509 0.11544661 0.12770816
#> (Intercept):2 -1.74917522 -3.522766358 -3.28199059 -3.19410049 -3.10729919
#> (Intercept):3 -1.82070427 -3.728692916 -3.48008139 -3.39998133 -3.31593107
#> (Intercept):4 -1.66796499 -3.532211280 -3.36473017 -3.30644674 -3.24189693
#> (Intercept):5 -1.59845770 -3.456244529 -3.31803555 -3.48921890 -3.55391896
#> (Intercept):6 -1.57411131 -3.401718755 -3.25262820 -3.70389836 -3.91529748
#> time 0.04276680 0.051938106 0.04404108 0.03363971 0.03237561
#> tx -0.01560048 0.004891656 0.02376669 -0.01464164 -0.03955617
#> yprev2 -0.20258334 -0.663940348 -0.54470888 -0.61150564 -0.62647700
#> yprev3 0.86715643 1.574285545 1.47031139 1.45304801 1.43222347
#> yprev4 1.57428554 3.512314884 3.24467574 3.24916527 3.18708095
#> yprev5 1.47031139 3.244675740 3.15027202 3.10885398 3.06109054
#> yprev6 1.45304801 3.249165272 3.10885398 3.57513334 3.57201485
#> yprev7 1.43222347 3.187080950 3.06109054 3.57201485 4.15251783