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> # 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/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
> 
> library(testthat)
> library(BayesianMCPMod)
> 
> test_check("BayesianMCPMod")
Using default prior reference scale 7.06484551805801
Using default prior reference scale 7.1096879209905
Using default prior reference scale 7.1096879209905
Using default prior reference scale 7.1096879209905
Bayesian Multiple Comparison Procedure
  Significant:                   0 
  Critical Probability:          0.95 
  Maximum Posterior Probability: 0.703399 
Posterior Probabilities for Model Shapes
                      lin
  Posterior Prob 0.703399 
  Significant           0 
Bayesian Multiple Comparison Procedure
  Significant:                   0 
  Critical Probability:          0.95 
  Maximum Posterior Probability: 0.703399 
Posterior Probabilities for Model Shapes
                      lin
  Posterior Prob 0.703399 
  Significant           0 
Bayesian Multiple Comparison Procedure
  Significant:                   1 
  Critical Probability:          0.5703569 
  Maximum Posterior Probability: 0.7899262 
Posterior Probabilities for Model Shapes
                       lin     emax1     emax2       exp
  Posterior Prob 0.7598337 0.7866026 0.7899262 0.6997892 
  Significant            1         1         1         1 
MED Assessment
  Selection Method:    avgFit 
  Identification Rate: 1 
   Dose Level: 0.5 2.0 4.0 8.0 
   MED Freq:     0   0   1   0 
  MED not reached Freq:        0 
  No success in MCP step Freq: 0 
Model Coefficients
  emax   e0 = 0.1, eMax = 2.5, ed50 = 1.1 
  exp    e0 = 0.6, e1 = 3.3, delta = 16 
  lin    e0 = 0.6, delta = 0.3 
Dose Levels
  Ctrl = 0, DG_1 = 0.5, DG_2 = 2, DG_3 = 4, DG_4 = 8 
Predictions, Maximum Effect, gAIC, avgFit Model Weights & Significance
         Ctrl DG_1 DG_2 DG_3 DG_4 mEff gAIC    w Sign
  avgFit  0.5  0.8  1.2  1.7  2.7  2.2   NA   NA   NA 
  emax    0.1  0.9  1.7  2.0  2.3  2.2  6.0  0.2  1.0 
  exp     0.6  0.7  1.1  1.6  2.8  2.2  6.2  0.2  1.0 
  lin     0.6  0.7  1.1  1.7  2.8  2.2  4.1  0.6  1.0 
Bayesian Multiple Comparison Procedure
  Significant:                   1 
  Critical Probability:          0.5703569 
  Maximum Posterior Probability: 0.7899262 
Posterior Probabilities for Model Shapes
                       lin     emax1     emax2       exp
  Posterior Prob 0.7598337 0.7866026 0.7899262 0.6997892 
  Significant            1         1         1         1 
MED Assessment
  Selection Method:    avgFit 
  Identification Rate: 1 
   Dose Level: 0.5 2.0 4.0 8.0 
   MED Freq:     0   0   1   0 
  MED not reached Freq:        0 
  No success in MCP step Freq: 0 
Model Coefficients
  emax   e0 = 0.1, eMax = 2.5, ed50 = 1.1 
  exp    e0 = 0.6, e1 = 3.3, delta = 16 
  lin    e0 = 0.6, delta = 0.3 
Dose Levels
  Ctrl = 0, DG_1 = 0.5, DG_2 = 2, DG_3 = 4, DG_4 = 8 
Predictions, Maximum Effect, gAIC, avgFit Model Weights & Significance
         Ctrl DG_1 DG_2 DG_3 DG_4 mEff gAIC    w Sign
  avgFit  0.5  0.8  1.2  1.7  2.7  2.2   NA   NA   NA 
  emax    0.1  0.9  1.7  2.0  2.3  2.2  6.0  0.2  1.0 
  exp     0.6  0.7  1.1  1.6  2.8  2.2  6.2  0.2  1.0 
  lin     0.6  0.7  1.1  1.7  2.8  2.2  4.1  0.6  1.0 
$`Summary of Posterior Distributions`
          mean       sd       2.5%    50.0%     97.5%
Ctrl 0.9289555 3.047512  -5.446244 1.035471  6.648349
DG_1 1.0000000 3.794733  -6.437540 1.000000  8.437540
DG_2 1.2088153 3.793750  -6.235961 1.191302  8.775696
DG_3 3.0229259 4.290196  -5.858047 3.484048  9.134780
DG_4 2.3626609 6.813267 -10.991096 2.362661 15.716418

$`Maximum Difference to Control and Dose Group`
max_diff       DG 
2.448577 3.000000 

$`Posterior Distributions`
$`Posterior Distributions`$Ctrl
Univariate normal mixture
Reference scale: 2
Mixture Components:
  comp1     comp2    
w 0.3683102 0.6316898
m 0.0000000 1.4705882
s 3.5355339 2.5724788

$`Posterior Distributions`$DG_1
Univariate normal mixture
Reference scale: 2
Mixture Components:
  comp1   
w 1.000000
m 1.000000
s 3.794733

$`Posterior Distributions`$DG_2
Univariate normal mixture
Reference scale: 2
Mixture Components:
  comp1     comp2    
w 0.1981843 0.8018157
m 1.4312102 1.1538462
s 5.2673734 3.3282012

$`Posterior Distributions`$DG_3
Univariate normal mixture
Reference scale: 2
Mixture Components:
  comp1     comp2     comp3    
w 0.1393727 0.5599776 0.3006497
m 1.7982353 1.2461538 6.9000000
s 5.9056304 3.4729726 0.9899495

$`Posterior Distributions`$DG_4
Univariate normal mixture
Reference scale: 2
Mixture Components:
  comp1   
w 1.000000
m 2.362661
s 6.813267

attr(,"ess")
numeric(0)

Note: For a more detailed posterior output including
      covariance matricies across mixture components,
      please use attr(x, 'posteriorInfo').Model Coefficients
  lin    e0 = 0.2, delta = 0.1 
Dose Levels
  Ctr = 0, DG_1 = 2.5 
Predictions, Maximum Effect, gAIC & avgFit Model Weights
          Ctr DG_1 mEff gAIC    w
  avgFit  0.2  0.3  0.1   NA   NA 
  lin     0.2  0.3  0.1  4.0  1.0 
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 132 ]
> 
> proc.time()
   user  system elapsed 
  59.95    1.54   61.48