R Under development (unstable) (2025-03-05 r87891 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2025 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/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: 0.7032914 
Posterior Probabilities for Model Shapes
                       lin
  Posterior Prob 0.7032914 
Bayesian Multiple Comparison Procedure
  Significant:                   0 
  Critical Probability:          0.95 
  Maximum Posterior Probability: 0.7032914 
Posterior Probabilities for Model Shapes
                       lin
  Posterior Prob 0.7032914 
Bayesian Multiple Comparison Procedure
  Significant:                   1 
  Critical Probability:          0.5703569 
  Maximum Posterior Probability: 0.6412959 
Posterior Probabilities for Model Shapes
                       lin     emax1     emax2       exp
  Posterior Prob 0.6191201 0.6412959 0.6410572 0.5839261 
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
  lin    e0 = 0.5, delta = 0.3 
  emax   e0 = 0, eMax = 2.2, ed50 = 0.8 
  exp    e0 = 0.5, e1 = 3.4, delta = 16 
Dose Levels
  Ctrl = 0, DG_1 = 0.5, DG_2 = 2, DG_3 = 4, DG_4 = 8 
Predictions, Maximum Effect, gAIC, Model Weights & Significance
         Ctrl DG_1 DG_2 DG_3 DG_4 mEff gAIC    w Sign
  lin     0.5  0.6  1.0  1.6  2.7  2.3  4.0  0.6  1.0 
  emax    0.0  0.9  1.6  1.9  2.1  2.0  6.0  0.2  1.0 
  exp     0.5  0.6  1.0  1.5  2.7  2.2  6.0  0.2  1.0 
  avgFit  0.4  0.7  1.2  1.6  2.6  2.2   NA   NA  1.0 
Bayesian Multiple Comparison Procedure
  Significant:                   1 
  Critical Probability:          0.5703569 
  Maximum Posterior Probability: 0.6412959 
Posterior Probabilities for Model Shapes
                       lin     emax1     emax2       exp
  Posterior Prob 0.6191201 0.6412959 0.6410572 0.5839261 
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
  lin    e0 = 0.5, delta = 0.3 
  emax   e0 = 0, eMax = 2.2, ed50 = 0.8 
  exp    e0 = 0.5, e1 = 3.4, delta = 16 
Dose Levels
  Ctrl = 0, DG_1 = 0.5, DG_2 = 2, DG_3 = 4, DG_4 = 8 
Predictions, Maximum Effect, gAIC, Model Weights & Significance
         Ctrl DG_1 DG_2 DG_3 DG_4 mEff gAIC    w Sign
  lin     0.5  0.6  1.0  1.6  2.7  2.3  4.0  0.6  1.0 
  emax    0.0  0.9  1.6  1.9  2.1  2.0  6.0  0.2  1.0 
  exp     0.5  0.6  1.0  1.5  2.7  2.2  6.0  0.2  1.0 
  avgFit  0.4  0.7  1.2  1.6  2.6  2.2   NA   NA  1.0 
$Ctr
Univariate normal mixture
Reference scale: 9.21011
Mixture Components:
  comp1       comp2       robust     
w   0.3332982   0.1667018   0.5000000
m -12.1416870 -12.4170316 -12.2334879
s   1.2347651   2.9981883   9.3080515

$DG_1
Univariate normal mixture
Reference scale: 9.308051
Mixture Components:
  comp1     
w   1.000000
m -12.233488
s   9.308051

$DG_2
Univariate normal mixture
Reference scale: 9.308051
Mixture Components:
  comp1     
w   1.000000
m -12.233488
s   9.308051

$DG_3
Univariate normal mixture
Reference scale: 9.308051
Mixture Components:
  comp1     
w   1.000000
m -12.233488
s   9.308051

$`Summary of Posterior Distributions`
          mean       sd      2.5%     50.0%    97.5%
Ctr  -12.23349 6.733127 -27.54388 -12.19502 3.076898
DG_1 -12.23349 9.308051 -30.47693 -12.23349 6.009958
DG_2 -12.23349 9.308051 -30.47693 -12.23349 6.009958
DG_3 -12.23349 9.308051 -30.47693 -12.23349 6.009958

$`Maximum Difference to Control and Dose Group`
max_diff       DG 
       0        0 

$`Posterior Distributions`
$`Posterior Distributions`$Ctr
Univariate normal mixture
Reference scale: 9.21011
Mixture Components:
  comp1       comp2       robust     
w   0.3332982   0.1667018   0.5000000
m -12.1416870 -12.4170316 -12.2334879
s   1.2347651   2.9981883   9.3080515

$`Posterior Distributions`$DG_1
Univariate normal mixture
Reference scale: 9.308051
Mixture Components:
  comp1     
w   1.000000
m -12.233488
s   9.308051

$`Posterior Distributions`$DG_2
Univariate normal mixture
Reference scale: 9.308051
Mixture Components:
  comp1     
w   1.000000
m -12.233488
s   9.308051

$`Posterior Distributions`$DG_3
Univariate normal mixture
Reference scale: 9.308051
Mixture Components:
  comp1     
w   1.000000
m -12.233488
s   9.308051


Model Coefficients
  lin    e0 = -0.1, delta = 0.5 
Dose Levels
  Ctr = 0, DG_1 = 2.5 
Predictions, Maximum Effect, gAIC & Model Weights
          Ctr DG_1 mEff gAIC    w
  lin    -0.1  1.0  1.2  4.0  1.0 
  avgFit -0.1  1.0  1.2   NA   NA 
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 130 ]
> 
> proc.time()
   user  system elapsed 
  97.26    2.62   99.89