From the output of the function fit_trend() (or read_bugs()), this
function extracts estimated parameters into a data.frame.
The resulting data.frame has no particular use in popbayes but it can be
useful for users.
Arguments
- data
a named
listof BUGS outputs. The output offit_trend()orread_bugs()
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)
#> module glm loaded
#> 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 ----
bugs <- popbayes::read_bugs(series = "garamba__alcelaphus_buselaphus",
path = temp_path)
## Extract estimated parameters ----
popbayes::bugs_to_df(bugs)
#> series parameter mean sd
#> 1 garamba__alcelaphus_buselaphus N[1] 1.790628e+04 1.689316e+03
#> 2 garamba__alcelaphus_buselaphus N[2] 3.883761e+03 5.036603e+02
#> 3 garamba__alcelaphus_buselaphus N[3] 3.330386e+03 3.923482e+02
#> 4 garamba__alcelaphus_buselaphus N[4] 3.010467e+03 4.223448e+02
#> 5 garamba__alcelaphus_buselaphus N[5] 2.665824e+03 3.322338e+02
#> 6 garamba__alcelaphus_buselaphus N[6] 3.193770e+03 5.397259e+02
#> 7 garamba__alcelaphus_buselaphus N[7] 3.713310e+03 6.753730e+02
#> 8 garamba__alcelaphus_buselaphus N[8] 3.467460e+03 3.706429e+02
#> 9 garamba__alcelaphus_buselaphus N[9] 2.890285e+03 2.078029e+02
#> 10 garamba__alcelaphus_buselaphus N[10] 2.752082e+03 2.100026e+02
#> 11 garamba__alcelaphus_buselaphus N[11] 2.761856e+03 2.631722e+02
#> 12 garamba__alcelaphus_buselaphus N[12] 2.634945e+03 3.012109e+02
#> 13 garamba__alcelaphus_buselaphus N[13] 1.310768e+03 7.332821e+01
#> 14 garamba__alcelaphus_buselaphus N[14] 1.570273e+03 8.972287e+01
#> 15 garamba__alcelaphus_buselaphus N[15] 2.392812e+03 1.519921e+02
#> 16 garamba__alcelaphus_buselaphus deviance 2.363015e+02 5.181431e+00
#> 17 garamba__alcelaphus_buselaphus meanr -4.902770e-02 2.819670e-03
#> 18 garamba__alcelaphus_buselaphus r[1] -2.188956e-01 2.249388e-02
#> 19 garamba__alcelaphus_buselaphus r[2] -1.522620e-01 6.868541e-02
#> 20 garamba__alcelaphus_buselaphus r[3] -5.193814e-02 5.945068e-02
#> 21 garamba__alcelaphus_buselaphus r[4] -2.391272e-02 3.275661e-02
#> 22 garamba__alcelaphus_buselaphus r[5] 8.711951e-02 6.419089e-02
#> 23 garamba__alcelaphus_buselaphus r[6] 7.427235e-02 5.737202e-02
#> 24 garamba__alcelaphus_buselaphus r[7] -1.924144e-02 5.199198e-02
#> 25 garamba__alcelaphus_buselaphus r[8] -8.945231e-02 5.030599e-02
#> 26 garamba__alcelaphus_buselaphus r[9] -2.466457e-02 4.405818e-02
#> 27 garamba__alcelaphus_buselaphus r[10] 1.906944e-03 6.695877e-02
#> 28 garamba__alcelaphus_buselaphus r[11] -4.904788e-02 6.548532e-02
#> 29 garamba__alcelaphus_buselaphus r[12] -8.665564e-02 1.547881e-02
#> 30 garamba__alcelaphus_buselaphus r[13] 9.028380e-02 3.556965e-02
#> 31 garamba__alcelaphus_buselaphus r[14] 1.402760e-01 2.739756e-02
#> 32 garamba__alcelaphus_buselaphus sdr 1.120436e-01 9.450154e-03
#> 33 garamba__alcelaphus_buselaphus vrrmax 4.614849e-01 3.892327e-02
#> 2.5% 25% 50% 75% 97.5%
#> 1 1.455630e+04 1.679222e+04 1.794258e+04 1.906091e+04 2.108549e+04
#> 2 2.935841e+03 3.535667e+03 3.872586e+03 4.213489e+03 4.910685e+03
#> 3 2.582710e+03 3.062950e+03 3.323238e+03 3.591467e+03 4.112219e+03
#> 4 2.234585e+03 2.716025e+03 2.991303e+03 3.288185e+03 3.882509e+03
#> 5 2.016537e+03 2.443837e+03 2.661995e+03 2.884955e+03 3.337119e+03
#> 6 2.235988e+03 2.808924e+03 3.168554e+03 3.544353e+03 4.326298e+03
#> 7 2.527789e+03 3.229544e+03 3.671713e+03 4.145033e+03 5.160717e+03
#> 8 2.764007e+03 3.213610e+03 3.460817e+03 3.713733e+03 4.213639e+03
#> 9 2.493077e+03 2.747081e+03 2.889112e+03 3.031210e+03 3.300216e+03
#> 10 2.350436e+03 2.608777e+03 2.749921e+03 2.894179e+03 3.168772e+03
#> 11 2.262168e+03 2.580459e+03 2.756181e+03 2.937379e+03 3.293515e+03
#> 12 2.075658e+03 2.422325e+03 2.629233e+03 2.834604e+03 3.248096e+03
#> 13 1.167060e+03 1.261315e+03 1.310487e+03 1.360599e+03 1.456304e+03
#> 14 1.395943e+03 1.510051e+03 1.569787e+03 1.630492e+03 1.747357e+03
#> 15 2.094038e+03 2.291104e+03 2.392646e+03 2.495365e+03 2.688947e+03
#> 16 2.274005e+02 2.326555e+02 2.358766e+02 2.394561e+02 2.477067e+02
#> 17 -5.427471e-02 -5.095713e-02 -4.913123e-02 -4.719951e-02 -4.332071e-02
#> 18 -2.629863e-01 -2.338235e-01 -2.188880e-01 -2.039872e-01 -1.750085e-01
#> 19 -2.851717e-01 -1.986203e-01 -1.521632e-01 -1.062113e-01 -1.635170e-02
#> 20 -1.692003e-01 -9.253924e-02 -5.134203e-02 -1.145511e-02 6.390797e-02
#> 21 -8.831008e-02 -4.594500e-02 -2.345445e-02 -1.398169e-03 3.869870e-02
#> 22 -4.173912e-02 4.479642e-02 8.691001e-02 1.300715e-01 2.155638e-01
#> 23 -3.637369e-02 3.514278e-02 7.402677e-02 1.130141e-01 1.882260e-01
#> 24 -1.189778e-01 -5.503172e-02 -1.982559e-02 1.549653e-02 8.470463e-02
#> 25 -1.867284e-01 -1.233874e-01 -9.041489e-02 -5.631910e-02 1.278464e-02
#> 26 -1.110432e-01 -5.448632e-02 -2.426217e-02 5.643973e-03 5.989999e-02
#> 27 -1.311905e-01 -4.252263e-02 2.182841e-03 4.747425e-02 1.330028e-01
#> 28 -1.784189e-01 -9.310263e-02 -4.833946e-02 -5.093828e-03 7.814803e-02
#> 29 -1.159740e-01 -9.737567e-02 -8.697705e-02 -7.610833e-02 -5.614843e-02
#> 30 2.011630e-02 6.656763e-02 9.023546e-02 1.142945e-01 1.594547e-01
#> 31 8.602679e-02 1.219673e-01 1.406142e-01 1.586507e-01 1.932516e-01
#> 32 9.430415e-02 1.055843e-01 1.117477e-01 1.181311e-01 1.316164e-01
#> 33 3.884197e-01 4.348804e-01 4.602663e-01 4.865581e-01 5.421014e-01
#> Rhat n.eff
#> 1 1.020905 770
#> 2 1.000976 27000
#> 3 1.001021 25000
#> 4 1.001295 4500
#> 5 1.001089 12000
#> 6 1.002497 980
#> 7 1.002446 1000
#> 8 1.000969 27000
#> 9 1.001274 4800
#> 10 1.001179 6900
#> 11 1.001156 7700
#> 12 1.000983 27000
#> 13 1.001130 8900
#> 14 1.001017 27000
#> 15 1.001020 25000
#> 16 1.003186 680
#> 17 1.011542 890
#> 18 1.001527 2700
#> 19 1.001400 3400
#> 20 1.001833 1700
#> 21 1.001010 27000
#> 22 1.002627 900
#> 23 1.000971 27000
#> 24 1.002824 810
#> 25 1.001208 6100
#> 26 1.000964 27000
#> 27 1.000971 27000
#> 28 1.001108 10000
#> 29 1.001067 14000
#> 30 1.001226 5700
#> 31 1.000965 27000
#> 32 1.001592 2400
#> 33 1.001592 2400
# }