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This function imports a list of BUGS outputs previously exported by fit_trend(). Users can import one, several, or all models.

Usage

read_bugs(series = NULL, path = ".")

Arguments

series

a vector of character strings. One or several count series names. If NULL (default) BUGS outputs for all count series will be imported. Users can run list_series() to get the correct spelling of count series names.

path

a character string. The directory in which BUGS outputs have been saved by the function fit_trend().

Value

An n-element list (where n is the number of count series). See fit_trend() for further information.

Examples

## Load Garamba raw dataset ----
file_path <- system.file("extdata", "garamba_survey.csv", 
                         package = "popbayes")
                         
garamba <- read.csv(file = file_path)

## Create temporary folder ----
temp_path <- tempdir()

## Format dataset ----
garamba_formatted <- popbayes::format_data(
  data              = garamba, 
  path              = temp_path,
  field_method      = "field_method",
  pref_field_method = "pref_field_method",
  conversion_A2G    = "conversion_A2G",
  rmax              = "rmax")
#>  Detecting 10 count series.

## Select one serie ----
a_buselaphus <- popbayes::filter_series(garamba_formatted, 
                                        location = "Garamba",
                                        species  = "Alcelaphus buselaphus")
#>  Found 1 series with "Alcelaphus buselaphus" and "Garamba".
# \donttest{
## Fit population trends (requires JAGS) ----
a_buselaphus_mod <- popbayes::fit_trend(a_buselaphus, path = temp_path)
#> Compiling data graph
#>    Resolving undeclared variables
#>    Allocating nodes
#>    Initializing
#>    Reading data back into data table
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 15
#>    Unobserved stochastic nodes: 15
#>    Total graph size: 227
#> 
#> Initializing model
#> 

## Import BUGS outputs for one count series ----
popbayes::read_bugs(series = "garamba__alcelaphus_buselaphus", 
                    path   = temp_path)
#> $garamba__alcelaphus_buselaphus
#> Inference for Bugs model at "model.bug", fit using jags,
#>  2 chains, each with 50000 iterations (first 10000 discarded), n.thin = 3
#>  n.sims = 26666 iterations saved
#>            mu.vect  sd.vect      2.5%       25%       50%       75%     97.5%
#> N[1]     17704.622 1732.733 14325.106 16540.416 17703.472 18854.702 21163.344
#> N[2]      3852.142  502.639  2892.122  3508.397  3843.527  4184.916  4857.835
#> N[3]      3313.482  390.724  2546.642  3049.617  3310.108  3578.820  4084.715
#> N[4]      3014.466  429.307  2205.787  2717.981  3003.240  3293.856  3893.400
#> N[5]      2675.016  326.426  2044.811  2451.061  2673.333  2895.294  3318.233
#> N[6]      3218.382  532.626  2269.759  2844.462  3181.803  3555.310  4353.830
#> N[7]      3748.142  663.983  2546.570  3288.082  3705.828  4163.506  5177.966
#> N[8]      3466.347  365.261  2769.738  3216.155  3459.593  3711.216  4200.803
#> N[9]      2886.866  208.334  2487.130  2745.760  2884.456  3027.397  3297.413
#> N[10]     2752.945  204.828  2356.488  2615.835  2749.331  2887.875  3159.933
#> N[11]     2760.178  257.254  2266.479  2584.772  2753.992  2929.448  3276.859
#> N[12]     2637.946  299.094  2078.455  2431.105  2627.825  2836.367  3248.185
#> N[13]     1311.255   73.453  1168.538  1261.814  1310.698  1360.683  1457.331
#> N[14]     1569.207   90.255  1393.237  1507.414  1568.650  1629.738  1747.739
#> N[15]     2393.256  151.154  2102.252  2291.875  2391.633  2494.663  2692.417
#> meanr       -0.049    0.003    -0.054    -0.051    -0.049    -0.047    -0.043
#> r[1]        -0.218    0.023    -0.265    -0.233    -0.218    -0.203    -0.174
#> r[2]        -0.149    0.069    -0.284    -0.195    -0.149    -0.103    -0.014
#> r[3]        -0.049    0.061    -0.168    -0.090    -0.049    -0.008     0.072
#> r[4]        -0.023    0.033    -0.087    -0.046    -0.024    -0.001     0.044
#> r[5]         0.089    0.062    -0.035     0.048     0.090     0.131     0.216
#> r[6]         0.075    0.057    -0.037     0.037     0.076     0.114     0.187
#> r[7]        -0.023    0.051    -0.122    -0.057    -0.023     0.011     0.079
#> r[8]        -0.090    0.050    -0.185    -0.124    -0.091    -0.057     0.009
#> r[9]        -0.024    0.043    -0.109    -0.053    -0.024     0.005     0.061
#> r[10]        0.001    0.067    -0.132    -0.044     0.001     0.046     0.131
#> r[11]       -0.047    0.065    -0.175    -0.092    -0.047    -0.003     0.080
#> r[12]       -0.087    0.015    -0.116    -0.097    -0.087    -0.077    -0.056
#> r[13]        0.090    0.036     0.020     0.066     0.090     0.114     0.159
#> r[14]        0.141    0.028     0.086     0.122     0.141     0.159     0.194
#> sdr          0.112    0.010     0.094     0.105     0.112     0.118     0.132
#> vrrmax       0.461    0.040     0.388     0.434     0.460     0.487     0.543
#> deviance   236.153    5.155   227.325   232.521   235.731   239.276   247.447
#>           Rhat n.eff
#> N[1]     1.002  1600
#> N[2]     1.001 27000
#> N[3]     1.001 13000
#> N[4]     1.001 13000
#> N[5]     1.001  7100
#> N[6]     1.001  3200
#> N[7]     1.004  1400
#> N[8]     1.001  6300
#> N[9]     1.001 27000
#> N[10]    1.001 12000
#> N[11]    1.002  2400
#> N[12]    1.002  1400
#> N[13]    1.001 21000
#> N[14]    1.001 27000
#> N[15]    1.001 27000
#> meanr    1.002  1900
#> r[1]     1.001  3000
#> r[2]     1.001 14000
#> r[3]     1.001 27000
#> r[4]     1.001 27000
#> r[5]     1.003  7400
#> r[6]     1.002  3600
#> r[7]     1.003  1900
#> r[8]     1.001 12000
#> r[9]     1.001  6100
#> r[10]    1.001  2900
#> r[11]    1.001  3200
#> r[12]    1.002  1300
#> r[13]    1.001 19000
#> r[14]    1.001 27000
#> sdr      1.002  1300
#> vrrmax   1.002  1300
#> deviance 1.001 27000
#> 
#> For each parameter, n.eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
#> 
#> DIC info (using the rule, pD = var(deviance)/2)
#> pD = 13.3 and DIC = 249.4
#> DIC is an estimate of expected predictive error (lower deviance is better).
#> 

## Import BUGS outputs for all count series ----
popbayes::read_bugs(path = temp_path)
#> $garamba__alcelaphus_buselaphus
#> Inference for Bugs model at "model.bug", fit using jags,
#>  2 chains, each with 50000 iterations (first 10000 discarded), n.thin = 3
#>  n.sims = 26666 iterations saved
#>            mu.vect  sd.vect      2.5%       25%       50%       75%     97.5%
#> N[1]     17704.622 1732.733 14325.106 16540.416 17703.472 18854.702 21163.344
#> N[2]      3852.142  502.639  2892.122  3508.397  3843.527  4184.916  4857.835
#> N[3]      3313.482  390.724  2546.642  3049.617  3310.108  3578.820  4084.715
#> N[4]      3014.466  429.307  2205.787  2717.981  3003.240  3293.856  3893.400
#> N[5]      2675.016  326.426  2044.811  2451.061  2673.333  2895.294  3318.233
#> N[6]      3218.382  532.626  2269.759  2844.462  3181.803  3555.310  4353.830
#> N[7]      3748.142  663.983  2546.570  3288.082  3705.828  4163.506  5177.966
#> N[8]      3466.347  365.261  2769.738  3216.155  3459.593  3711.216  4200.803
#> N[9]      2886.866  208.334  2487.130  2745.760  2884.456  3027.397  3297.413
#> N[10]     2752.945  204.828  2356.488  2615.835  2749.331  2887.875  3159.933
#> N[11]     2760.178  257.254  2266.479  2584.772  2753.992  2929.448  3276.859
#> N[12]     2637.946  299.094  2078.455  2431.105  2627.825  2836.367  3248.185
#> N[13]     1311.255   73.453  1168.538  1261.814  1310.698  1360.683  1457.331
#> N[14]     1569.207   90.255  1393.237  1507.414  1568.650  1629.738  1747.739
#> N[15]     2393.256  151.154  2102.252  2291.875  2391.633  2494.663  2692.417
#> meanr       -0.049    0.003    -0.054    -0.051    -0.049    -0.047    -0.043
#> r[1]        -0.218    0.023    -0.265    -0.233    -0.218    -0.203    -0.174
#> r[2]        -0.149    0.069    -0.284    -0.195    -0.149    -0.103    -0.014
#> r[3]        -0.049    0.061    -0.168    -0.090    -0.049    -0.008     0.072
#> r[4]        -0.023    0.033    -0.087    -0.046    -0.024    -0.001     0.044
#> r[5]         0.089    0.062    -0.035     0.048     0.090     0.131     0.216
#> r[6]         0.075    0.057    -0.037     0.037     0.076     0.114     0.187
#> r[7]        -0.023    0.051    -0.122    -0.057    -0.023     0.011     0.079
#> r[8]        -0.090    0.050    -0.185    -0.124    -0.091    -0.057     0.009
#> r[9]        -0.024    0.043    -0.109    -0.053    -0.024     0.005     0.061
#> r[10]        0.001    0.067    -0.132    -0.044     0.001     0.046     0.131
#> r[11]       -0.047    0.065    -0.175    -0.092    -0.047    -0.003     0.080
#> r[12]       -0.087    0.015    -0.116    -0.097    -0.087    -0.077    -0.056
#> r[13]        0.090    0.036     0.020     0.066     0.090     0.114     0.159
#> r[14]        0.141    0.028     0.086     0.122     0.141     0.159     0.194
#> sdr          0.112    0.010     0.094     0.105     0.112     0.118     0.132
#> vrrmax       0.461    0.040     0.388     0.434     0.460     0.487     0.543
#> deviance   236.153    5.155   227.325   232.521   235.731   239.276   247.447
#>           Rhat n.eff
#> N[1]     1.002  1600
#> N[2]     1.001 27000
#> N[3]     1.001 13000
#> N[4]     1.001 13000
#> N[5]     1.001  7100
#> N[6]     1.001  3200
#> N[7]     1.004  1400
#> N[8]     1.001  6300
#> N[9]     1.001 27000
#> N[10]    1.001 12000
#> N[11]    1.002  2400
#> N[12]    1.002  1400
#> N[13]    1.001 21000
#> N[14]    1.001 27000
#> N[15]    1.001 27000
#> meanr    1.002  1900
#> r[1]     1.001  3000
#> r[2]     1.001 14000
#> r[3]     1.001 27000
#> r[4]     1.001 27000
#> r[5]     1.003  7400
#> r[6]     1.002  3600
#> r[7]     1.003  1900
#> r[8]     1.001 12000
#> r[9]     1.001  6100
#> r[10]    1.001  2900
#> r[11]    1.001  3200
#> r[12]    1.002  1300
#> r[13]    1.001 19000
#> r[14]    1.001 27000
#> sdr      1.002  1300
#> vrrmax   1.002  1300
#> deviance 1.001 27000
#> 
#> For each parameter, n.eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
#> 
#> DIC info (using the rule, pD = var(deviance)/2)
#> pD = 13.3 and DIC = 249.4
#> DIC is an estimate of expected predictive error (lower deviance is better).
#> 
# }