Prints a concise summary of the robust covariance estimation results.
Usage
# S3 method for class 'robcov_vglm'
print(x, ...)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 Warning: 12 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12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 Warning: 76 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12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 Warning: 77 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12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 Warning: 128 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12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 Warning: 151 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12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 Warning: 151 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12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 Warning: 151 diagonal elements of the working weights variable 'wz' have been replaced by 1.819e-12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)
print(robust_fit)
#> Robust (Sandwich) Covariance Estimation for vglm
#> ================================================
#>
#> Call:
#> vglm_markov(formula = ordered(y) ~ time + tx + yprev, family = VGAM::cumulative(reverse = TRUE,
#> parallel = TRUE), data = markov_data)
#>
#> Number of observations: 240
#> Bread: observed
#> HC type: HC0
#> Number of clusters: 40
#>
#> Coefficients:
#> (Intercept):1 (Intercept):2 (Intercept):3 (Intercept):4 (Intercept):5
#> Estimate -0.2507 -21.57 -21.809 -24.289 -28.762
#> Robust SE 0.8256 2.01 2.098 1.961 1.984
#> (Intercept):6 time tx yprev2 yprev3 yprev4 yprev5 yprev6
#> Estimate -33.309 -0.2626 -0.1648 -0.8884 11.8641 23.985 26.516 32.250
#> Robust SE 2.065 0.1112 0.3198 0.8756 0.9312 1.874 1.775 1.891
#> yprev7
#> Estimate 37.219
#> Robust SE 2.038
#>
#> (Use summary() for full coefficient table with z-values and p-values)