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] 1.772100e+04 1.729775e+03
#> 2 garamba__alcelaphus_buselaphus N[2] 3.850141e+03 4.973625e+02
#> 3 garamba__alcelaphus_buselaphus N[3] 3.309152e+03 3.905891e+02
#> 4 garamba__alcelaphus_buselaphus N[4] 2.998527e+03 4.323555e+02
#> 5 garamba__alcelaphus_buselaphus N[5] 2.678312e+03 3.307934e+02
#> 6 garamba__alcelaphus_buselaphus N[6] 3.233291e+03 5.526038e+02
#> 7 garamba__alcelaphus_buselaphus N[7] 3.765074e+03 6.764909e+02
#> 8 garamba__alcelaphus_buselaphus N[8] 3.472602e+03 3.705043e+02
#> 9 garamba__alcelaphus_buselaphus N[9] 2.888552e+03 2.072682e+02
#> 10 garamba__alcelaphus_buselaphus N[10] 2.748165e+03 2.034497e+02
#> 11 garamba__alcelaphus_buselaphus N[11] 2.752900e+03 2.580135e+02
#> 12 garamba__alcelaphus_buselaphus N[12] 2.625408e+03 2.982512e+02
#> 13 garamba__alcelaphus_buselaphus N[13] 1.311394e+03 7.328570e+01
#> 14 garamba__alcelaphus_buselaphus N[14] 1.571261e+03 9.053393e+01
#> 15 garamba__alcelaphus_buselaphus N[15] 2.394501e+03 1.539111e+02
#> 16 garamba__alcelaphus_buselaphus deviance 2.362347e+02 5.165043e+00
#> 17 garamba__alcelaphus_buselaphus meanr -4.875138e-02 2.885809e-03
#> 18 garamba__alcelaphus_buselaphus r[1] -2.186147e-01 2.270729e-02
#> 19 garamba__alcelaphus_buselaphus r[2] -1.500121e-01 6.877398e-02
#> 20 garamba__alcelaphus_buselaphus r[3] -5.099129e-02 6.069916e-02
#> 21 garamba__alcelaphus_buselaphus r[4] -2.204245e-02 3.392850e-02
#> 22 garamba__alcelaphus_buselaphus r[5] 9.074184e-02 6.432717e-02
#> 23 garamba__alcelaphus_buselaphus r[6] 7.540158e-02 5.851457e-02
#> 24 garamba__alcelaphus_buselaphus r[7] -2.350364e-02 5.097567e-02
#> 25 garamba__alcelaphus_buselaphus r[8] -9.050151e-02 5.096770e-02
#> 26 garamba__alcelaphus_buselaphus r[9] -2.499481e-02 4.316500e-02
#> 27 garamba__alcelaphus_buselaphus r[10] 5.962291e-05 6.665749e-02
#> 28 garamba__alcelaphus_buselaphus r[11] -4.950209e-02 6.553757e-02
#> 29 garamba__alcelaphus_buselaphus r[12] -8.615151e-02 1.541633e-02
#> 30 garamba__alcelaphus_buselaphus r[13] 9.034500e-02 3.563934e-02
#> 31 garamba__alcelaphus_buselaphus r[14] 1.402948e-01 2.779964e-02
#> 32 garamba__alcelaphus_buselaphus sdr 1.122059e-01 9.657620e-03
#> 33 garamba__alcelaphus_buselaphus vrrmax 4.621535e-01 3.977778e-02
#> 2.5% 25% 50% 75% 97.5%
#> 1 1.431631e+04 1.655658e+04 1.772194e+04 1.888266e+04 2.110299e+04
#> 2 2.888981e+03 3.509654e+03 3.847487e+03 4.180559e+03 4.834674e+03
#> 3 2.550177e+03 3.042992e+03 3.309741e+03 3.573129e+03 4.072483e+03
#> 4 2.195827e+03 2.698451e+03 2.982672e+03 3.280820e+03 3.887447e+03
#> 5 2.033679e+03 2.450529e+03 2.677881e+03 2.904399e+03 3.324855e+03
#> 6 2.234661e+03 2.841034e+03 3.203521e+03 3.583267e+03 4.414243e+03
#> 7 2.583617e+03 3.282852e+03 3.721283e+03 4.190219e+03 5.220882e+03
#> 8 2.774144e+03 3.215877e+03 3.464590e+03 3.721169e+03 4.215468e+03
#> 9 2.487402e+03 2.746939e+03 2.887398e+03 3.027517e+03 3.300742e+03
#> 10 2.354293e+03 2.609529e+03 2.746285e+03 2.885595e+03 3.145791e+03
#> 11 2.259670e+03 2.576350e+03 2.747009e+03 2.927198e+03 3.267404e+03
#> 12 2.058893e+03 2.419733e+03 2.619069e+03 2.822791e+03 3.229276e+03
#> 13 1.169513e+03 1.261833e+03 1.310949e+03 1.361339e+03 1.455309e+03
#> 14 1.395993e+03 1.510320e+03 1.570637e+03 1.631658e+03 1.752060e+03
#> 15 2.094380e+03 2.290871e+03 2.393927e+03 2.498309e+03 2.696495e+03
#> 16 2.273803e+02 2.326268e+02 2.357662e+02 2.393482e+02 2.476382e+02
#> 17 -5.417675e-02 -5.071391e-02 -4.881654e-02 -4.688358e-02 -4.284532e-02
#> 18 -2.643631e-01 -2.335204e-01 -2.181308e-01 -2.035562e-01 -1.739981e-01
#> 19 -2.840044e-01 -1.966724e-01 -1.502095e-01 -1.037954e-01 -1.386374e-02
#> 20 -1.686733e-01 -9.192219e-02 -5.164791e-02 -1.002543e-02 6.891448e-02
#> 21 -8.773795e-02 -4.476381e-02 -2.245801e-02 3.076995e-04 4.682962e-02
#> 22 -3.326142e-02 4.651487e-02 9.047525e-02 1.342468e-01 2.222861e-01
#> 23 -3.663444e-02 3.581146e-02 7.492059e-02 1.140134e-01 1.938999e-01
#> 24 -1.225695e-01 -5.760341e-02 -2.414298e-02 9.806327e-03 7.841485e-02
#> 25 -1.880805e-01 -1.252855e-01 -9.160568e-02 -5.617282e-02 1.119226e-02
#> 26 -1.097735e-01 -5.432977e-02 -2.501886e-02 4.182605e-03 6.042202e-02
#> 27 -1.309067e-01 -4.508699e-02 3.956121e-04 4.575904e-02 1.280129e-01
#> 28 -1.791352e-01 -9.338575e-02 -4.914100e-02 -5.119069e-03 7.850645e-02
#> 29 -1.155664e-01 -9.653620e-02 -8.644329e-02 -7.610274e-02 -5.473144e-02
#> 30 2.001179e-02 6.653616e-02 9.037790e-02 1.141177e-01 1.597086e-01
#> 31 8.434997e-02 1.217361e-01 1.405392e-01 1.589559e-01 1.941114e-01
#> 32 9.409520e-02 1.056223e-01 1.117709e-01 1.185014e-01 1.322428e-01
#> 33 3.875591e-01 4.350368e-01 4.603616e-01 4.880831e-01 5.446815e-01
#> Rhat n.eff
#> 1 1.001631 2200
#> 2 1.001109 10000
#> 3 1.001016 27000
#> 4 1.000970 27000
#> 5 1.000962 27000
#> 6 1.001053 16000
#> 7 1.002393 1000
#> 8 1.000988 27000
#> 9 1.001137 8500
#> 10 1.001087 12000
#> 11 1.001677 2100
#> 12 1.002717 860
#> 13 1.000967 27000
#> 14 1.001001 27000
#> 15 1.001032 21000
#> 16 1.002141 1300
#> 17 1.001651 2200
#> 18 1.001647 2200
#> 19 1.001066 14000
#> 20 1.000975 27000
#> 21 1.000972 27000
#> 22 1.001151 7900
#> 23 1.002909 770
#> 24 1.003283 650
#> 25 1.000976 27000
#> 26 1.001392 3500
#> 27 1.001601 2300
#> 28 1.002164 1200
#> 29 1.002384 1100
#> 30 1.001011 27000
#> 31 1.001065 14000
#> 32 1.000967 27000
#> 33 1.000967 27000
# }