AHMbook-package {AHMbook} | R Documentation |

Provides functions and data sets needed to replicate the analyses shown in the two-volume publication, *Applied Hierarchical Modeling in Ecology: analysis of distribution, abundance and species richness in R and BUGS* by Marc Kéry and Andy Royle, Academic Press (Vol 1, 2016; Vol 2, 2021).

The functions are listed by chapter below, where AHM1 refers to volume 1 and AHM2 to volume 2.

`sim.fn`

Simulate a homogeneous Poisson point process and illustrate the fundamental relationships between intensity, abundance and occurrence (AHM1 - section 1.1)

`data.fn`

Simulate count data that are replicated in space and in time according to the binomial N-mixture model of Royle (2004) (this is for much simpler cases than is possible with function

`simNmix`

in Chapter 6 below) (AHM1 - 4.3)

`ppc.plot`

Plot results from posterior predictive checks in section AHM1 - 6.8, for a fitted binomial N-mixture model object with JAGS

`simNmix`

Simulate count data and individual detection histories for binomial and multinomial mixture models respectively under a wide range of conditions (AHM1 - 6.9.3)

`plot_Nmix_resi`

Do diagnostic plots for one binomial N-mixture model fitted with all three mixture distributions currently available in unmarked: Poisson, negative binomial and zero-inflated Poisson (AHM1 - 6.9.3)

`map.Nmix.resi`

Produce a map of the residuals from a binomial N-mixture model (see Section AHM1 - 6.9.3)

`simpleNmix`

Simulate count data under a very simple version of the binomial mixture model, with time for space substitution (AHM1 - 6.12)

`playRN`

Play Royle-Nichols (RN) model: generate count data under the binomial N-mixture model of Royle (2004), then 'degrade' the data to detection/nondetection and fit the RN model using unmarked and estimate site-specific abundance (AHM1 - 6.13.1)

`instRemPiFun`

,`crPiFun`

,`crPiFun.Mb`

,`MhPiFun`

Define the relationship between the multinomial cell probabilities and the underlying detection probability parameters (i.e., a pi function) in various designs (AHM1 - 7.8 and AHM2 - Chapter 2)

`sim.ldata`

Simulate data under a non-hierarchical line transect distance sampling model (AHM1 - 8.2.3)

`sim.pdata`

Simulate data under a non-hierarchical point transect (= point count) distance sampling model (AHM1 - 8.2.5.1)

`simHDS`

Simulate data under a hierarchical distance sampling protocol (line or point) (AHM1 - 8.5.1)

`simHDSg`

Simulate data under a hierarchical distance sampling (HDS) protocol with groups (AHM1 - 9.2.1)

`simHDStr`

Simulate data under a time-removal/distance sampling design (AHM1 - 9.3.2)

`simHDSopen`

Simulate open hierarchical distance sampling data (AHM1 - 9.5.4)

`issj.sim`

Simulate data under the open distance sampling protocol for the Island Scrub Jays (AHM1 - 9.7.1)

`sim.spatialDS`

Simulate data under a basic spatial distance sampling model (AHM1 - 9.8.3)

`sim.spatialHDS`

Simulate data under a spatial hierarchical distance sampling model (AHM1 - 9.8.5)

`simOcc`

Simulate detection/nondetection data under static occupancy models under a wide range of conditions (AHM1 - 10.5)

`sim3Occ`

Simulate detection/nondetection data under a static 3-level occupancy model (AHM1 - 10.10)

`simOccttd`

Simulate 'timing data' under a static time-to-detection occupancy design (AHM1 - 10.12.1)

`wigglyOcc`

Simulate detection/nondetection data under a static occupancy model with really wiggly covariate relationships in occupancy and detection probability (AHM1 - 10.14)

`spline.prep`

Prepare input for BUGS model when fitting a spline for a covariate (AHM1 - 10.14)

`simComm`

Simulate detection/nondetection or count data under a community occupancy or abundance model respectively (AHM1 - 11.2)

`simNpC`

Simulate data on abundance (N), detection probability (p) and resulting counts (C) under a counting process with imperfect detection (AHM2 - 1.2)

`simPOP`

Simulate count data under a demographic state-space, or Dail-Madsen, model (no robust design) (AHM2 - 1.7.1)

`simPH`

Simulate count data with phenological curves within a year (AHM2 - 1.8.1)

`graphSSM`

Plot trajectories of counts and latent abundance from a fitted Gaussian state-space model (AHM2 - 1.6.1)

`simDM0`

Simulate count data from a Dail-Madsen model under a robust design, no covariates (AHM2 - 2.5.1)

`simDM`

Simulate count data from a Dail-Madsen model under a robust design, with covariates (AHM2 - 2.5.5)

`simMultMix`

Simulate “removal” count data from a multinomial-mixture model (AHM2 - 2.7.1)

`simFrogDisease`

Simulate detection data for diseased frogs (AHM2 - 2.9.1)

`simCJS`

Simulate individual capture history data under a Cormack-Jolly-Seber (CJS) survival model (AHM2 - 3.2.2)

`ch2marray`

Convert capture history data to the m-array aggregation (AHM2 - 3.4.1)

`simDynocc`

Simulate detection/nondetection data under a dynamic occupancy model under a wide range of conditions (AHM2 - 4.4)

`simDemoDynocc`

Simulate detection/nondetection data under a demographic dynamic occupancy model (AHM2 - 4.12)

`simDCM`

Simulate detection/nondetection data under a general dynamic community model (site-occupancy variant) (AHM2 - 5.2)

`valid_data`

Partial validation of simulated data with false positives (AHM2 - 7.6.2)

`getLVcorrMat`

Compute the correlation matrix from an analysis of a latent variable occupancy or binomial N-mixture model (AHM2 - 8.4.2)

`simDynoccSpatial`

Simulate detection/nondetection data under a dynamic occupancy model with spatial covariate and spatial autocorrelation (AHM2 - 9.6.1.1)

`simExpCorrRF`

Simulate data from a Gaussian random field with negative exponential correlation function (AHM2 - 9.2)

`simOccSpatial`

Simulate detection/nondetection data under a spatial, static occupancy model for a real landscape in the Bernese Oberland (Swiss Alps) (AHM2 - 9.2)

`simNmixSpatial`

Simulate counts under a spatial, static binomial N-mixture model for a real landscape in the Bernese Oberland (Swiss Alps) (AHM2 - 9.2)

`simPPe`

Simulate a spatial point pattern in a heterogeneous landscape and show aggregation to abundance and occurrence ('e' for educational version) (AHM2 - 10.2)

`simDataDK`

Simulate data for an integrated species distribution model (SDM) of Dorazio-Koshkina (AHM2 - 10.6.1)

`simSpatialDSline`

Simulate line transect distance sampling data with spatial variation in density (AHM2 - 11.5)

`simSpatialDSte`

Simulate data for replicate line transect distance sampling surveys with spatial variation in density and temporary emigration (AHM2 - 11.8.1)

`simDSM`

Simulate line transect data for density surface modeling (AHM2 - 11.10.1)

`BerneseOberland`

Landscape data for the Bernese Oberland around Interlaken, Switzerland (AHM2 - 9.2)

`crestedTit`

Crested Tit data from the Swiss Breeding Bird Survey MHB (Monitoring Häufige Brutvögel) for 1999 to 2015 (AHM2 - 1.3)

`cswa`

Chestnut-sided Warbler data for point counts and spot-mapping from White Mountain National Forest (AHM2 - 2.4.3)

`crossbillAHM`

Crossbill data from the Swiss Breeding Bird Survey for 2001 to 2012 (AHM2 - 4.9)

`dragonflies`

Toy data set used in AHM1 - 3.1

`duskySalamanders`

Counts of juvenile vs adult salamanders over 7 years (AHM2 - 2.9.2)

`EurasianLynx`

Data for Eurasian Lynx in Italy and Switzerland (AHM2 - 7.3.2)

`Finnmark`

Data from surveys of birds in Finnmark in NE Norway (AHM2 - 5.7)

`FrenchPeregrines`

Detection data for peregrines in the French Jura (AHM2 - 4.11)

`greenWoodpecker`

Count data for Green Woodpeckers in Switzerland from the MHB (AHM2 - 2.2)

`HubbardBrook`

Point count data for warblers from Hubbard Brook, New Hampshire (AHM2 - 8.2)

`jay`

The European Jay data set (from the MHB) is now included in unmarked (AHM1 - 7.9)

`MesoCarnivores`

Camera trap data for 3 species of meso-carnivores (AHM2 - 8.2)

`MHB2014`

Complete data from the Swiss Breeding Bird Survey MHB (Monitoring Häufige Brutvögel) for the year 2014 (AHM1 - 11.3)

`spottedWoodpecker`

Data for Middle Spotted Woodpeckers in Switzerland (AHM2 - 4.11.2)

`SwissAtlasHa`

A 1ha-scale subset of the count data from the Swiss Breeding Bird Atlas (AHM2 - 8.4.2)

`SwissEagleOwls`

Territory-level, multi-state detection/nondetection data for Eagle Owls in Switzerland (AHM2 - 6.4)

`SwissMarbledWhite`

Data from the Biodiversity Monitoring Program (LANAG) in the Swiss Canton of Aargau for Marbled White butterfly (AHM2 - 1.8.2)

`SwissSquirrels`

Count data for Red Squirrels in Switzerland from the Swiss breeding bird survey MHB (AHM1 - 10.9)

`SwissTits`

Data for 6 species of tits in Switzerland from from the Swiss breeding bird survey MHB during 2004 to 2013 (AHM1 - 6.13.1)

`treeSparrow`

Data for Tree Sparrows in Alaska (AHM2 - 11.8.4)

`ttdPeregrine`

Time-to-detection data for Peregrines (AHM1 - 10.12.2)

`UKmarbledWhite`

Data from the UK Butterfly Monitoring Scheme (UKBMS) for Marbled White butterfly (AHM2 - 1.8.2)

`wagtail`

Distance sampling data for Yellow Wagtails in The Netherlands (AHM1 - 9.5.3)

`waterVoles`

Detection/nondetection data for the Mighty Water Vole of Scotland (AHM2 - 7.2.2)

`wigglyLine`

Coordinates for a wiggly transect line (AHM2 - 11.9)

`willowWarbler`

Capture-history (survival) data for Willow Warblers in Britain (AHM2 - 3.4.1)

`zinit`

Generate starting values for fitting survival models (introduced in AHM2 - 3.2.3).

`standardize`

Standardize covariates to mean 0, SD 1.

`fitstats`

,`fitstats2`

Calculate fit-statistics used in parboot GOF tests throughout the book (eg, Sections AHM1 - 7.5.4, AHM1 - 7.9.3, AHM2 - 2.3.3)

`e2dist`

Compute a matrix of Euclidean distances

`image_scale`

Draw scale for image (introduced in chapter AHM1 - 9.8.3)

`bigCrossCorr`

Report cross-correlations above a given threshold

`Color_Ramps`

Color ramps for use with image or raster plots

Marc Kéry, Andy Royle, Mike Meredith

[Package *AHMbook* version 0.2.3 Index]