Running 'bayescount_example.R' [176s]
  Running 'count.model_examples.R' [15s]
  Running 'fec.analysis_examples.R' [183s]
  Running 'fecrt.model_examples.R' [43s]
  Running 'likelihood_examples.R' [11s]
Running the tests in 'tests/bayescount_example.R' failed.
Complete output:
  > library("bayescount")
  > 
  > data <- array(dim=c(20,10,2))
  > means1 <- rgamma(20, 10, 1)
  > means2 <- rgamma(20, 5, 1)
  > for(i in 1:20){
  + data[i,,1] <- rpois(10, means1[i])
  + data[i,,2] <- rpois(10, means2[i])
  + }
  > # Missing data is permissible but means the likelihood cannot be
  > # calculated - a warning will be printed:
  > data[sample(1:(20*10*2), 10)] <- NA
  > try(unlink("analysis.ZILP.csv"), silent=TRUE)
  > # Run the analysis:
  > bayescount(name="analysis", data=data, model = "ZILP",
  + setnames=c("Simulated group A", "Simulated group B"), likelihood=TRUE)
  
  --- 'Bayescount': Analyse count data using a Bayesian distributional simulation model implemented in JAGS ---
  
  *PLEASE NOTE:  THIS SOFTWARE IS INTENDED FOR EDUCATIONAL PURPOSES ONLY AND SHOULD NOT BE RELIED UPON FOR REAL WORLD APPLICATIONS*
  *ANALYSING DATA USING MCMC SAMPLING CAN PRODUCE MISLEADING RESULTS IF USED INAPPROPRIATELY*
  
  Settings are as follows:
  Name of analysis:                             analysis
  First column used for individual names:       FALSE
  Number of datasets:                           2
  Number of animals in dataset one              20
  Divide data by                                1
  Adjust mean by                                1
  Model to use:                                 zero-inflated lognormal Poisson
  Omit datasets with all zero counts:           TRUE
  Use alternative prior for variance :          FALSE
  Adjust z-i means to mean of whole population: FALSE
  Calculate the likelihood for each model:      TRUE
  Suppress JAGS output to screen:               FALSE
  Write the results to file:                    TRUE
  System call to activate JAGS:                 C:/Program Files/JAGS/JAGS-4.3.1/x64/bin/jags-terminal.exe
  
  You are using R Under development (unstable) (2024-03-22 r86169 ucrt)
  on a windows machine, with the RTerm GUI
  JAGS version 4.3.1 found successfully using the command 'C:/Program
  Files/JAGS/JAGS-4.3.1/x64/bin/jags-terminal.exe'
  The rjags package is installed
  
  
  Testing the model function
  Finished running the simulation
  Test completed successfully
  
  
  Running the ZILP model for dataset 'Simulated group A'...
  Compiling rjags model...
  
  Analysing dataset using the ZILP model...
  
  Auto-run JAGS
  
  Running a pilot chain...
  Compiling rjags model...
  Calling the simulation using the rjags method...
  Adapting the model for 1000 iterations...
  Burning in the model for 4000 iterations...
  Running the model for 10000 iterations...
  Simulation complete
  Finished running the simulation
  
  Calculating the Gelman-Rubin statistic for 3 variables....
  The Gelman-Rubin statistic is below 1.05 for all parameters
  
  Calculating the necessary sample length based on the Raftery and
  Lewis's diagnostic...
  Indicated sample length achieved
  Auto-run JAGS complete
  
  Calculating results
  *PLEASE NOTE:  THIS SOFTWARE IS INTENDED FOR EDUCATIONAL PURPOSES ONLY AND SHOULD NOT BE RELIED UPON FOR REAL WORLD APPLICATIONS*
  Finished running the model
  
  Dataset 'Simulated group A' completed in 12.8 seconds with the ZILP model
  1 of 2 datasets (50%) completed
  12.8 seconds elapsed, estimated time remaining: 12 seconds
  0 failed convergence, and 0 quit with an error
  
  
  Running the ZILP model for dataset 'Simulated group B'...
  Compiling rjags model...
  
  Analysing dataset using the ZILP model...
  
  Auto-run JAGS
  
  Running a pilot chain...
  Compiling rjags model...
  Calling the simulation using the rjags method...
  Adapting the model for 1000 iterations...
  Burning in the model for 4000 iterations...
  Running the model for 10000 iterations...
  Simulation complete
  Finished running the simulation
  
  Calculating the Gelman-Rubin statistic for 3 variables....
  The Gelman-Rubin statistic is below 1.05 for all parameters
  
  Calculating the necessary sample length based on the Raftery and
  Lewis's diagnostic...
  Indicated sample length achieved
  Auto-run JAGS complete
  
  Calculating results
  *PLEASE NOTE:  THIS SOFTWARE IS INTENDED FOR EDUCATIONAL PURPOSES ONLY AND SHOULD NOT BE RELIED UPON FOR REAL WORLD APPLICATIONS*
  Finished running the model
  
  Dataset 'Simulated group B' completed in 12.7 seconds with the ZILP model
  2 of 2 datasets (100%) completed
  25.6 seconds elapsed, estimated time remaining: 0 seconds
  0 failed convergence, and 0 quit with an error
  
  All models completed.  Total time taken:  25.6 seconds
  
  *PLEASE NOTE:  THIS SOFTWARE IS INTENDED FOR EDUCATIONAL PURPOSES ONLY AND SHOULD NOT BE RELIED UPON FOR REAL WORLD APPLICATIONS*
  *ANALYSING DATA USING MCMC SAMPLING CAN PRODUCE MISLEADING RESULTS IF USED INAPPROPRIATELY*
  
  --- End ---
  
  Warning messages:
  1: In bayescount(name = "analysis", data = data, model = "ZILP", setnames = c("Simulated group A",  :
    'bayescount' is deprecated.
  Use 'fec.analysis' instead.
  See help("Deprecated") and help("bayescount-deprecated").
  2: In bayescount.single(model = model, data = setdata, alt.prior = alt.prior,  :
    The likelihood computation is not available for unequal repeated measures so has not been calculated
  3: In bayescount.single(model = model, data = setdata, alt.prior = alt.prior,  :
    The likelihood computation is not available for unequal repeated measures so has not been calculated
  > unlink("analysis.ZILP.csv")
  > 
  > proc.time()
     user  system elapsed 
    19.46    0.59   29.43 
Running the tests in 'tests/fec.analysis_examples.R' failed.
Complete output:
  > library('bayescount')
  > 
  > results <- fec.analysis(data=c(0,5,3,7,0,4,3,8,0),
  +                         model="ZILP", silent.jags=TRUE)
  Compiling rjags model...
  
  Analysing dataset using the ZILP model...
  
  Auto-run JAGS
  Running a pilot chain...
  Finished running the simulation
  
  Calculating the Gelman-Rubin statistic for 3 variables....
  The Gelman-Rubin statistic is below 1.05 for all parameters
  
  Calculating the necessary sample length based on the Raftery and
  Lewis's diagnostic...
  The model will need to be run for a further 14062 updates.  This will
  take approximately 1.9 seconds.
  
  Finished running the simulation
  Indicated sample length achieved
  Auto-run JAGS complete
  
  Calculating results
  *PLEASE NOTE:  THIS SOFTWARE IS INTENDED FOR EDUCATIONAL PURPOSES ONLY AND SHOULD NOT BE RELIED UPON FOR REAL WORLD APPLICATIONS*
  Finished running the model
  
  > 
  > proc.time()
     user  system elapsed 
     8.15    0.28   12.45