Fit hierarchical generalized linear models to predict occupancy
Source:R/fit_hglm_occupancy_models.R
fit_hglm_occupancy_models.RdEstimate probability of occupancy for a set of features in a set of
planning units. Models are fitted as hierarchical generalized linear models
that account for for imperfect detection (following Royle & Link 2006)
using JAGS (via runjags::run.jags()). To limit over-fitting,
covariate coefficients are sampled using a Laplace prior distribution
(equivalent to L1 regularization used in machine learning contexts)
(Park & Casella 2008).
Usage
fit_hglm_occupancy_models(
site_data,
feature_data,
site_detection_columns,
site_n_surveys_columns,
site_env_vars_columns,
feature_survey_sensitivity_column,
feature_survey_specificity_column,
jags_n_samples = rep(10000, length(site_detection_columns)),
jags_n_burnin = rep(1000, length(site_detection_columns)),
jags_n_thin = rep(100, length(site_detection_columns)),
jags_n_adapt = rep(1000, length(site_detection_columns)),
jags_n_chains = rep(4, length(site_detection_columns)),
n_folds = rep(5, length(site_detection_columns)),
n_threads = 1,
seed = 500,
verbose = FALSE
)Arguments
- site_data
sf::sf()object with site data.- feature_data
base::data.frame()object with feature data.- site_detection_columns
characternames ofnumericcolumns in the argument tosite_datathat contain the proportion of surveys conducted within each site that detected each feature. Each column should correspond to a different feature, and contain a proportion value (between zero and one). If a site has not previously been surveyed, a value of zero should be used.- site_n_surveys_columns
characternames ofnumericcolumns in the argument tosite_datathat contain the total number of surveys conducted for each each feature within each site. Each column should correspond to a different feature, and contain a non-negative integer number (e.g. 0, 1, 2, 3). If a site has not previously been surveyed, a value of zero should be used.- site_env_vars_columns
characternames of columns in the argument tosite_datathat contain environmental information for fitting updated occupancy models based on possible survey outcomes. Each column should correspond to a different environmental variable, and containnumeric,factor, orcharacterdata. No missing (NA) values are permitted in these columns.- feature_survey_sensitivity_column
charactername of the column in the argument tofeature_datathat contains probability of future surveys correctly detecting a presence of each feature in a given site (i.e. the sensitivity of the survey methodology). This column should havenumericvalues that are between zero and one. No missing (NA) values are permitted in this column.- feature_survey_specificity_column
charactername of the column in the argument tofeature_datathat contains probability of future surveys correctly detecting an absence of each feature in a given site (i.e. the specificity of the survey methodology). This column should havenumericvalues that are between zero and one. No missing (NA) values are permitted in this column.- jags_n_samples
integernumber of sample to generate per chain for MCMC analyses. See documentation for thesampleparameter inrunjags::run.jags()for more information). Defaults to 10,000 for each feature.- jags_n_burnin
integernumber of warm up iterations for MCMC analyses. See documentation for theburninparameter inrunjags::run.jags()for more information). Defaults to 10,000 for each feature.- jags_n_thin
integernumber of thinning iterations for MCMC analyses. See documentation for thethinparameter inrunjags::run.jags()for more information). Defaults to 100 for each feature.- jags_n_adapt
integernumber of adapting iterations for MCMC analyses. See documentation for theadaptparameter inrunjags::run.jags()for more information). Defaults to 1,000 for each feature.- jags_n_chains
integertotal number of chains for MCMC analyses. See documentation for then.chainsparameter inrunjags::run.jags()for more information). Defaults to 4 for each fold for each feature.- n_folds
numericnumber of folds to split the training data into when fitting models for each feature. Defaults to 5 for each feature.- n_threads
integernumber of threads to use for parameter tuning. Defaults to 1.- seed
integerinitial random number generator state for model fitting. Defaults to 500.- verbose
logicalindicating if information should be printed during computations. Defaults toFALSE.
Value
A list object containing:
- models
listoflistobjects containing the models.- predictions
tibble::tibble()object containing predictions for each feature.- performance
tibble::tibble()object containing the performance of the best models for each feature. It contains the following columns:- feature
name of the feature.
- max_mpsrf
maximum multi-variate potential scale reduction factor (MPSRF) value for the models. A MPSRF value less than 1.05 means that all coefficients in a given model have converged, and so a value less than 1.05 in this column means that all the models fit for a given feature have successfully converged.
- train_tss_mean
mean TSS statistic for models calculated using training data in cross-validation.
- train_tss_std
standard deviation in TSS statistics for models calculated using training data in cross-validation.
- train_sensitivity_mean
mean sensitivity statistic for models calculated using training data in cross-validation.
- train_sensitivity_std
standard deviation in sensitivity statistics for models calculated using training data in cross-validation.
- train_specificity_mean
mean specificity statistic for models calculated using training data in cross-validation.
- train_specificity_std
standard deviation in specificity statistics for models calculated using training data in cross-validation.
- test_tss_mean
mean TSS statistic for models calculated using test data in cross-validation.
- test_tss_std
standard deviation in TSS statistics for models calculated using test data in cross-validation.
- test_sensitivity_mean
mean sensitivity statistic for models calculated using test data in cross-validation.
- test_sensitivity_std
standard deviation in sensitivity statistics for models calculated using test data in cross-validation.
- test_specificity_mean
mean specificity statistic for models calculated using test data in cross-validation.
- test_specificity_std
standard deviation in specificity statistics for models calculated using test data in cross-validation.
Details
This function (i) prepares the data for model fitting, (ii) fits the models, and (iii) assesses the performance of the models. These analyses are performed separately for each feature. For a given feature:
The data are prepared for model fitting by partitioning the data using k-fold cross-validation (set via argument to
n_folds). The training and evaluation folds are constructed in such a manner as to ensure that each training and evaluation fold contains at least one presence and one absence observation.A model for fit separately for each fold (see
inst/jags/model.jagsfor model code). To assess convergence, the multi-variate potential scale reduction factor (MPSRF) statistic is calculated for each model.The performance of the cross-validation models is evaluated. Specifically, the TSS, sensitivity, and specificity statistics are calculated (if relevant, weighted by the argument to
site_weights_data). These performance values are calculated using the models' training and evaluation folds. To assess convergence, the maximum MPSRF statistic for the models fit for each feature is calculated.
Dependencies
This function requires the JAGS software to be installed. For information on installing the JAGS software, please consult the documentation for the rjags package.
References
Park T & Casella G (2008) The Bayesian lasso. Journal of the American Statistical Association, 103: 681–686.
Royle JA & Link WA (2006) Generalized site occupancy models allowing for false positive and false negative errors. Ecology, 87: 835–841.
Examples
# \dontrun{
# set seeds for reproducibility
set.seed(123)
# simulate data for 200 sites, 2 features, and 3 environmental variables
site_data <- simulate_site_data(n_sites = 30, n_features = 2, prop = 0.1)
feature_data <- simulate_feature_data(n_features = 2, prop = 1)
# print JAGS model code
cat(readLines(system.file("jags", "model.jags", package = "surveyvoi")),
sep = "\n")
#> model {
#> # data
#> ## int<lower=1> n_vars
#> ## int<lower=1> train_n_sites
#> ## real sens
#> ## real spec
#> ## real[train_n_sites, n_vars] train_model_matrix
#> ## real[train_n_sites] train_n_obs
#> ## matrix[train_n_sites, max(train_n_obs)] train_obs
#>
#> # priors
#> lambda ~ dunif(0.001, 10) # regularization parameter
#> coefs[1] ~ dnorm(0, 0.001) # intercept parameter
#> for (i in 2:n_vars) {
#> coefs[i] ~ ddexp(0, lambda) # covariate parameters
#> }
#>
#> # transformed parameters
#> for (i in 1:train_n_sites) {
#> logit(yhat[i]) <- coefs %*% train_model_matrix[i, ]
#> }
#>
#> # likelihood
#> for (i in 1:train_n_sites) {
#> occ[i] ~ dbern(yhat[i])
#> for (j in 1:train_n_obs[i]) {
#> train_obs[i, j] ~ dbern((occ[i] * sens) + ((1 - occ[i]) * spec))
#> }
#> }
#> }
# fit models
# note that we use a small number of MCMC iterations so that the example
# finishes quickly, you probably want to use the defaults for real work
results <- fit_hglm_occupancy_models(
site_data, feature_data,
c("f1", "f2"), c("n1", "n2"), c("e1", "e2", "e3"),
"survey_sensitivity", "survey_specificity",
n_folds = rep(5, 2),
jags_n_samples = rep(250, 2), jags_n_burnin = rep(250, 2),
jags_n_thin = rep(1, 2), jags_n_adapt = rep(100, 2),
n_threads = 1)
#>
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| | 0%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:42 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
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#> . . Reading data file data.txt
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#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:44 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
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#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
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#> Graph information:
#> Observed stochastic nodes: 67
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#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:47 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
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#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 66
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
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|===== | 8%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:48 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
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|======= | 10%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:50 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 63
#> Unobserved stochastic nodes: 24
#> Total graph size: 353
#> . Reading parameter file inits1.txt
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|========= | 12%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:52 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 74
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
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|========== | 15%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:54 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 67
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
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|============ | 18%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:56 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 66
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
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|============== | 20%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:19:58 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
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|================ | 22%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:00 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 63
#> Unobserved stochastic nodes: 24
#> Total graph size: 353
#> . Reading parameter file inits1.txt
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|================== | 25%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:02 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 74
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
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|=================== | 28%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:03 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 67
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
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#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
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|===================== | 30%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:05 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 66
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
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#> .
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|======================= | 32%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:07 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
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#> ************************************************** 100%
#> . . Updating 250
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#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
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#>
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|======================== | 35%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:09 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 63
#> Unobserved stochastic nodes: 24
#> Total graph size: 353
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
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#> ************************************************** 100%
#> . . Updating 250
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#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|========================== | 38%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:11 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 74
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
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#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|============================ | 40%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:13 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 67
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|============================== | 42%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:14 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 66
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|================================ | 45%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:16 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|================================= | 48%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:18 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 63
#> Unobserved stochastic nodes: 24
#> Total graph size: 353
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|=================================== | 50%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:20 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 71
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|===================================== | 52%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:22 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|====================================== | 55%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:24 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|======================================== | 58%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:26 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 68
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|========================================== | 60%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:27 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 61
#> Unobserved stochastic nodes: 24
#> Total graph size: 353
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|============================================ | 62%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:29 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 71
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|============================================== | 65%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:31 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|=============================================== | 68%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:33 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|================================================= | 70%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:35 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 68
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|=================================================== | 72%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:37 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 61
#> Unobserved stochastic nodes: 24
#> Total graph size: 353
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|==================================================== | 75%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:39 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 71
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|====================================================== | 78%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:40 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|======================================================== | 80%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:42 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|========================================================== | 82%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:44 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 68
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|============================================================ | 85%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:46 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 61
#> Unobserved stochastic nodes: 24
#> Total graph size: 353
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|============================================================= | 88%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:48 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 71
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|=============================================================== | 90%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:50 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|================================================================= | 92%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:52 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 70
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|================================================================== | 95%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:53 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 68
#> Unobserved stochastic nodes: 26
#> Total graph size: 387
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|==================================================================== | 98%Calling the simulation...
#> Welcome to JAGS 4.3.0 on Mon Sep 23 16:20:55 2024
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 61
#> Unobserved stochastic nodes: 24
#> Total graph size: 353
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . Updating 250
#> -------------------------------------------------| 250
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Calculating summary statistics...
#> Finished running the simulation
#>
|
|======================================================================| 100%
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
#> Calculating summary statistics...
#> Calculating the Gelman-Rubin statistic for 3 variables....
# print model predictions
print(results$predictions)
#> # A tibble: 30 × 2
#> f1 f2
#> <dbl> <dbl>
#> 1 5.88e- 2 0.948
#> 2 1.00e+ 0 0.00000153
#> 3 5.37e-10 1.00
#> 4 1.00e+ 0 0.0000000615
#> 5 1.00e+ 0 0.0000633
#> 6 3.23e- 3 0.998
#> 7 6.25e- 3 0.881
#> 8 9.97e- 1 0.0608
#> 9 1.89e- 4 0.999
#> 10 2.89e- 1 0.829
#> # ℹ 20 more rows
# print model performance
print(results$performance, width = Inf)
#> # A tibble: 2 × 14
#> feature max_mpsrf train_tss_mean train_tss_std train_sensitivity_mean
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 f1 1.17 0.866 0.0651 0.958
#> 2 f2 2.57 0.836 0.0812 0.967
#> train_sensitivity_std train_specificity_mean train_specificity_std
#> <dbl> <dbl> <dbl>
#> 1 0.0421 0.909 0.0371
#> 2 0.0453 0.869 0.0581
#> test_tss_mean test_tss_std test_sensitivity_mean test_sensitivity_std
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.818 0.209 0.882 0.161
#> 2 0.655 0.222 0.881 0.163
#> test_specificity_mean test_specificity_std
#> <dbl> <dbl>
#> 1 0.935 0.0926
#> 2 0.774 0.230
# }