birds_ZI: Zero-Inflated Birds Dataset - Subset of the North American...

In MultiNMix: Multi-Species N-Mixture (MNM) Models with 'nimble'

Zero-Inflated Birds Dataset - Subset of the North American Breeding Bird Survey Dataset

Description

This dataset is a subset of the North American Breeding Bird Survey, containing data collected at 24 routes in Michigan, USA. Each route has 10 stops, and the dataset includes counts for 20 bird species.

Usage

birds_ZI

Format

A data frame with 2,880 rows and 13 columns:

Route

An identifier for the 24 survey sites.

Year

The year of the survey (numeric).

English_Common_Name

The English common name of the bird species surveyed (character).

Stop1

Count for replicate 1 at the site (numeric).

Stop2

Count for replicate 2 at the site (numeric).

Stop3

Count for replicate 3 at the site (numeric).

Stop4

Count for replicate 4 at the site (numeric).

Stop5

Count for replicate 5 at the site (numeric).

Stop6

Count for replicate 6 at the site (numeric).

Stop7

Count for replicate 7 at the site (numeric).

Stop8

Count for replicate 8 at the site (numeric).

Stop9

Count for replicate 9 at the site (numeric).

Stop10

Count for replicate 10 at the site (numeric).

Details

This dataset represents a subset of the North American Breeding Bird Survey. Data was collected in Michigan over six years, with observations for 20 bird species recorded at 24 routes, each surveyed 10 times. The dataset is used to study avian biodiversity and population trends.

Source

North American Breeding Bird Survey (https://www.pwrc.usgs.gov/BBS/)

Examples

data(birds_ZI)
head(birds_ZI)

# Example: Hurdle Model
# Data must first be reformatted to an array of dimension (R,T,S,K)
R <- 24
T <- 10
S <- 20
K <- 6
# Ensure data is ordered consistently
birds_ZI <- birds_ZI[order(birds_ZI$Route, birds_ZI$Year, birds_ZI$English_Common_Name), ]

# Create a 4D array with proper dimension
Y <- array(NA, dim = c(R, T, S, K))

# Map route, species, and year to indices
route_idx <- as.numeric(factor(birds_ZI$Route))
species_idx <- as.numeric(factor(birds_ZI$English_Common_Name))
year_idx <- as.numeric(factor(birds_ZI$Year))

# Populate the array
stop_data <- as.matrix(birds_ZI[, grep("^Stop", colnames(birds))])

for (i in seq_len(nrow(birds))) {
  Y[route_idx[i], , species_idx[i], year_idx[i]] <- stop_data[i, ]
  }

  # Assign dimnames
  dimnames(Y) <- list(
    Route = sort(unique(birds_ZI$Route)),
      Stop = paste0("Stop", 1:T),
        Species = sort(unique(birds_ZI$English_Common_Name)),
          Year = sort(unique(birds_ZI$Year))
          )

# Selecting only 5 bird species  for analysis:
Y<-Y[,,1:5,]

model<-MNM_fit(Y=Y, AR=TRUE, Hurdle=TRUE)

MultiNMix documentation built on April 3, 2025, 6:10 p.m.