R Under development (unstable) (2026-07-12 r90242 ucrt) -- "Unsuffered Consequences" Copyright (C) 2026 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(bayesics) > > test_check("bayesics") --- Bayes factor in favor of the full vs. null model: 4.81e+55; =>Level of evidence: Decisive --- Summary of factor level means --- # A tibble: 10 x 5 Variable `Post Mean` Lower Upper `Prob Dir` 1 Mean : x1 : a -0.911 -1.11 -0.716 1.000 2 Mean : x1 : b -1.09 -1.31 -0.882 1 3 Mean : x1 : c -0.915 -1.09 -0.737 1 4 Mean : x1 : d 0.905 0.707 1.10 1.000 5 Mean : x1 : e 1.05 0.854 1.25 1 6 Var : x1 : a 0.992 0.750 1.31 NA 7 Var : x1 : b 1.17 0.884 1.54 NA 8 Var : x1 : c 0.829 0.627 1.09 NA 9 Var : x1 : d 1.03 0.776 1.36 NA 10 Var : x1 : e 1.01 0.764 1.33 NA --- Summary of pairwise differences --- # A tibble: 10 x 9 Comparison `Post Mean` Lower Upper `Prob Dir` `ROPE (0.1)` EPR 1 a-b 0.182 -0.103 0.465 0.894 0.270 0.549 2 a-c 0.00419 -0.261 0.268 0.506 0.525 0.501 3 a-d -1.82 -2.09 -1.54 1 0 0.101 4 a-e -1.96 -2.24 -1.69 1 0 0.0836 5 b-c -0.178 -0.452 0.0985 0.900 0.261 0.450 6 b-d -2.00 -2.28 -1.71 1 0 0.0893 7 b-e -2.14 -2.43 -1.85 1 0 0.0740 8 c-d -1.82 -2.09 -1.55 1 0 0.0913 9 c-e -1.97 -2.23 -1.70 1 0 0.0744 10 d-e -0.145 -0.428 0.135 0.844 0.340 0.460 # i 2 more variables: `EPR Lower` , `EPR Upper` *Note: EPR (Exceedence in Pairs Rate) for a Comparison of g-h = Pr(Y_(gi) > Y_(hi)|parameters) # A tibble: 15 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 (Intercept) -1.08 -1.24 -0.929 1 NA (NA,NA) 2 x1 1.02 0.926 1.12 1 0 (-0.089,0.08~ 3 x2 0.0130 -0.0373 0.105 0.283 0.947 (-0.088,0.08~ 4 x3b -0.0166 -0.239 0.156 0.231 0.996 (-0.351,0.35~ 5 x3c 0.0388 -0.0923 0.298 0.291 0.991 (-0.351,0.35~ 6 x3d 2.01 1.76 2.25 1 0 (-0.351,0.35~ 7 x3e 2.14 1.89 2.38 1 0 (-0.351,0.35~ 8 x4 -0.0000604 -0.0697 0.0696 0.177 0.973 (-0.083,0.08~ 9 x5 -0.00599 -0.0917 0.0568 0.221 0.966 (-0.088,0.08~ 10 x6 -0.000371 -0.0731 0.0703 0.176 0.974 (-0.087,0.08~ 11 x7 0.0132 -0.0350 0.107 0.282 0.948 (-0.089,0.08~ 12 x8 0.00545 -0.0581 0.0867 0.224 0.970 (-0.088,0.08~ 13 x9 0.0571 0 0.192 0.503 0.648 (-0.089,0.08~ 14 x10 -0.0121 -0.105 0.0387 0.281 0.948 (-0.083,0.08~ 15 Residual variance 1.05 0.936 1.28 NA NA (NA,NA) Cell sizes were too small for large sample approximation. Instead, setting uniform prior on Pr(exposure|outcome) and making exact finite sample inference. Cell sizes were too small for large sample approximation. Instead, setting uniform prior on Pr(exposure|outcome) and making exact finite sample inference. Cell sizes were too small for large sample approximation. Instead, setting uniform prior on Pr(exposure|outcome) and making exact finite sample inference. Cell sizes were too small for large sample approximation. Instead, setting uniform prior on Pr(exposure|outcome) and making exact finite sample inference. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. A uniform prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. A uniform prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. A uniform prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. A uniform prior will be used. Prior shape parameters were not supplied. Beta(2,3.9) prior will be used. Prior shape parameters were not supplied. Beta(3.9,2) prior will be used. The g hyperparameter in Zellner's g prior is not specified. It will be set automatically to n. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. ---------- Values given in terms of odds ratios ---------- # A tibble: 6 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 x1 2.22 1.26 3.93 0.997 0.00180 (0.972,1.029) 2 x2 0.838 0.473 1.49 0.727 0.0694 (0.97,1.031) 3 x3b 0.429 0.0508 3.62 0.781 0.0638 (0.889,1.125) 4 x3c 0.911 0.126 6.58 0.537 0.0925 (0.889,1.125) 5 x3d 8.49 1.54 46.8 0.993 0.00535 (0.889,1.125) 6 x3e 21.5 3.91 118. 1.000 0.000223 (0.889,1.125) Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. ---------- Test for heteroscedasticity in 1-way ANOVA models. Bayes factor in favor of homoscedasticity = 592366584.582148 Level of evidence: Decisive in favor of homoscedasticity ---------- ---------- Test for heteroscedasticity in 1-way ANOVA models. Bayes factor in favor of homoscedasticity = 0.028467538606378 Level of evidence: Strong in favor of heteroscedasticity ---------- The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) # A tibble: 8 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 (Intercept) -0.996 -1.20 -0.790 1 NA (NA,NA) 2 x1 0.943 0.849 1.04 1 1.85e-56 (-0.085,0.08~ 3 x2 0.0897 -0.00309 0.182 0.971 4.56e- 1 (-0.084,0.08~ 4 x3b 0.0428 -0.249 0.335 0.613 9.71e- 1 (-0.337,0.33~ 5 x3c -0.0155 -0.307 0.276 0.542 9.76e- 1 (-0.337,0.33~ 6 x3d 1.90 1.61 2.19 1 8.75e-24 (-0.337,0.33~ 7 x3e 1.94 1.65 2.24 1 5.61e-25 (-0.337,0.33~ 8 Residual varia~ 1.10 0.978 1.25 NA NA (NA,NA) The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) By default, the fraction of data "used" is max(ncol(X) + 1,log(n)) / n. ---------- The fractional Bayes factor equaled 1.76e+60. Interpretation: Decisive (in favor of the first model) ---------- The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) The mu hyperparameter in the normal prior is not specified. It will be set automatically to 0. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The V hyperparameter in the normal prior is not specified. It will be set automatically to 4/25Diag(s^2_{X_j}) # A tibble: 8 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 (Intercept) -0.996 -1.20 -0.790 1 NA (NA,NA) 2 x1 0.943 0.849 1.04 1 1.85e-56 (-0.085,0.08~ 3 x2 0.0897 -0.00309 0.182 0.971 4.56e- 1 (-0.084,0.08~ 4 x3b 0.0428 -0.249 0.335 0.613 9.71e- 1 (-0.337,0.33~ 5 x3c -0.0155 -0.307 0.276 0.542 9.76e- 1 (-0.337,0.33~ 6 x3d 1.90 1.61 2.19 1 8.75e-24 (-0.337,0.33~ 7 x3e 1.94 1.65 2.24 1 5.61e-25 (-0.337,0.33~ 8 Residual varia~ 1.10 0.978 1.25 NA NA (NA,NA) Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. The g hyperparameter in Zellner's g prior is not specified. It will be set automatically to n. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The g hyperparameter in Zellner's g prior is not specified. It will be set automatically to n. Finished with 500 preliminary posterior draws. # A tibble: 8 x 5 Estimand Estimate Lower Upper `Prob Dir` 1 ACME (Control) 1.08 0.486 1.80 1 2 ACME (Treatment) 1.90 0.952 3.30 1 3 ADE (Control) 0.865 0.642 1.15 1 4 ADE (Treatment) 1.68 1.28 2.23 1 5 Total Effect 2.76 1.91 4.05 1 6 ACME (Average) 1.49 0.741 2.53 1 7 ADE (Average) 1.27 1.02 1.57 1 8 Prop. Mediated (Average) 0.529 0.373 0.633 NA The g hyperparameter in Zellner's g prior is not specified. It will be set automatically to n. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The g hyperparameter in Zellner's g prior is not specified. It will be set automatically to n. Finished with 500 preliminary posterior draws. control_value missing; set to be the 1st quintile of tr treat_value missing; set to be the 4th quintile of tr Finished with 500 preliminary posterior draws. Finished with 500 preliminary posterior draws. # A tibble: 8 x 5 Estimand Estimate Lower Upper `Prob Dir` 1 ACME (Control) 0.638 0.367 1.01 1 2 ACME (Treatment) 2.30 1.44 3.60 1 3 ADE (Control) 0.927 0.687 1.26 1 4 ADE (Treatment) 2.59 2.02 3.52 1 5 Total Effect 3.23 2.45 4.50 1 6 ACME (Average) 1.47 0.933 2.30 1 7 ADE (Average) 1.76 1.43 2.24 1 8 Prop. Mediated (Average) 0.450 0.359 0.527 NA The g hyperparameter in Zellner's g prior is not specified. It will be set automatically to n. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. The g hyperparameter in Zellner's g prior is not specified. It will be set automatically to n. The hyperparameters for the residual variance were not provided. Instead, the prior will put 50% prior probability that R^2 is between 0.1^2 and 0.9^2. # A tibble: 4 x 5 Estimand Estimate Lower Upper `Prob Dir` 1 ACME 2.04 1.48 2.55 1 2 ADE 2.27 1.89 2.63 1 3 Total Effect 4.31 3.76 4.88 1 4 Prop. Mediated 0.473 0.381 0.555 NA Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. ---------- Values given in terms of odds ratios ---------- # A tibble: 6 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 x1 2.46 1.28 4.71 0.997 0.00176 (0.972,1.029) 2 x2 0.841 0.458 1.54 0.712 0.0671 (0.97,1.031) 3 x3b 0.360 0.0271 4.77 0.781 0.0528 (0.889,1.125) 4 x3c 0.840 0.135 5.22 0.574 0.0989 (0.889,1.125) 5 x3d 9.33 1.36 64.3 0.988 0.00735 (0.889,1.125) 6 x3e 23.8 3.68 153. 1.000 0.000397 (0.889,1.125) # A tibble: 7 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 (Intercept) -2.33 -3.83 -0.819 0.999 NA (NA,NA) 2 x1 0.900 0.250 1.55 0.997 0.00176 (-0.029,0.029) 3 x2 -0.174 -0.781 0.434 0.712 0.0671 (-0.031,0.031) 4 x3b -1.02 -3.61 1.56 0.781 0.0528 (-0.118,0.118) 5 x3c -0.175 -2.00 1.65 0.574 0.0989 (-0.118,0.118) 6 x3d 2.23 0.304 4.16 0.988 0.00735 (-0.118,0.118) 7 x3e 3.17 1.30 5.03 1.000 0.000397 (-0.118,0.118) Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. Assuming all observations correspond to Bernoulli, i.e., Binomial with one trial. ---------- Values given in terms of rate ratios ---------- # A tibble: 6 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 x1 2.56 2.17 3.01 1 3.18e-28 (0.972,1.029) 2 x2 0.997 0.766 1.30 0.510 1.79e- 1 (0.97,1.031) 3 x3b 1.79 0.279 11.5 0.730 8.20e- 2 (0.889,1.125) 4 x3c 1.38 0.204 9.35 0.629 9.10e- 2 (0.889,1.125) 5 x3d 9.14 1.60 52.3 0.994 4.85e- 3 (0.889,1.125) 6 x3e 10.2 1.78 58.5 0.995 3.59e- 3 (0.889,1.125) # A tibble: 7 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 (Intercept) -2.08 -3.80 -0.368 0.991 NA (NA,NA) 2 x1 0.939 0.776 1.10 1 3.18e-28 (-0.029,0.029) 3 x2 -0.00337 -0.267 0.260 0.510 1.79e- 1 (-0.031,0.031) 4 x3b 0.582 -1.28 2.44 0.730 8.20e- 2 (-0.118,0.118) 5 x3c 0.322 -1.59 2.24 0.629 9.10e- 2 (-0.118,0.118) 6 x3d 2.21 0.470 3.96 0.994 4.85e- 3 (-0.118,0.118) 7 x3e 2.32 0.576 4.07 0.995 3.59e- 3 (-0.118,0.118) Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. ---------- Values given in terms of rate ratios ---------- # A tibble: 7 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 x1 3.41 2.00 5.84 1.000 0.00000373 (0.972,1.029) 2 x2 0.730 0.460 1.16 0.909 0.0426 (0.97,1.031) 3 x3b 0.522 0.0802 3.40 0.752 0.0779 (0.889,1.125) 4 x3c 0.653 0.0921 4.63 0.665 0.0857 (0.889,1.125) 5 x3d 4.18 0.743 23.5 0.948 0.0287 (0.889,1.125) 6 x3e 2.56 0.441 14.9 0.853 0.0604 (0.889,1.125) 7 phi 0.893 0.458 1.74 0.630 NA (NA,NA) # A tibble: 8 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 (Intercept) -1.57 -3.19 0.0365 0.972 NA (NA,NA) 2 x1 1.23 0.691 1.76 1.000 0.00000373 (-0.029,0.029) 3 x2 -0.314 -0.776 0.148 0.909 0.0426 (-0.031,0.031) 4 x3b -0.650 -2.52 1.22 0.752 0.0779 (-0.118,0.118) 5 x3c -0.426 -2.38 1.53 0.665 0.0857 (-0.118,0.118) 6 x3d 1.43 -0.297 3.16 0.948 0.0287 (-0.118,0.118) 7 x3e 0.942 -0.819 2.70 0.853 0.0604 (-0.118,0.118) 8 log(phi) -0.113 -0.781 0.556 0.630 NA (NA,NA) Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. # A tibble: 7 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 (Intercept) -0.693 -1.49 0.102 0.956 NA (NA,NA) 2 x1 0.877 0.492 1.26 1.000 0.0000231 (-0.079,0.079) 3 x2 -0.0322 -0.438 0.373 0.562 0.309 (-0.083,0.083) 4 x3b -0.742 -1.93 0.444 0.890 0.204 (-0.321,0.321) 5 x3c 0.160 -0.806 1.13 0.627 0.464 (-0.321,0.321) 6 x3d 1.48 0.351 2.61 0.995 0.0212 (-0.321,0.321) 7 x3e 1.61 0.376 2.85 0.995 0.0193 (-0.321,0.321) # A tibble: 7 x 7 Variable `Post Mean` Lower Upper `Prob Dir` ROPE `ROPE bounds` 1 (Intercept) -0.693 -1.49 0.102 0.956 NA (NA,NA) 2 x1 0.877 0.492 1.26 1.000 0.0000231 (-0.079,0.079) 3 x2 -0.0322 -0.438 0.373 0.562 0.309 (-0.083,0.083) 4 x3b -0.742 -1.93 0.444 0.890 0.204 (-0.321,0.321) 5 x3c 0.160 -0.806 1.13 0.627 0.464 (-0.321,0.321) 6 x3d 1.48 0.351 2.61 0.995 0.0212 (-0.321,0.321) 7 x3e 1.61 0.376 2.85 0.995 0.0193 (-0.321,0.321) Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Missing other covariate values in 'exemplar_covariates.' Using medoid observation instead. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. A flat Gamma(0.001,0.001) prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. A flat Gamma(0.001,0.001) prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. A uniform prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Jeffrey's prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. A uniform prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. A uniform prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. Prior shape parameters were not supplied. Beta(2,2) prior will be used. [ FAIL 0 | WARN 17 | SKIP 0 | PASS 566 ] [ FAIL 0 | WARN 17 | SKIP 0 | PASS 566 ] > > proc.time() user system elapsed 246.51 25.70 272.64