birds_ZI {MultiNMix} | R Documentation |
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)