birds {MultiNMix} | R Documentation |
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 15 routes in Michigan, USA. Each route has 10 stops, and the dataset includes counts for 10 bird species.
Usage
birds
Format
A data frame with 600 rows and 13 columns:
- Route
An identifier for the 15 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).
Source
North American Breeding Bird Survey (https://www.pwrc.usgs.gov/BBS/)
Examples
# MNM Model
#' # Example: Hurdle Model
# Data must first be reformatted to an array of dimension (R,T,S,K)
R <- 15
T <- 10
S <- 10
K <- 4
# Ensure data is ordered consistently
birds <- birds[order(birds$Route, birds$Year, birds$English_Common_Name), ]
# Create a 4D array with proper dimension
Y <- array(NA, dim = c(R, T, S, K))
# Map route, species, and year to indices
route_idx <- as.numeric(factor(birds$Route))
species_idx <- as.numeric(factor(birds$English_Common_Name))
year_idx <- as.numeric(factor(birds$Year))
# Populate the array
stop_data <- as.matrix(birds[, 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$Route)),
Stop = paste0("Stop", 1:T),
Species = sort(unique(birds$English_Common_Name)),
Year = sort(unique(birds$Year))
)
# Selecting only 5 bird species and 1 year for analysis:
Y<-Y[,,1:5,1]
model<-MNM_fit(Y=Y, AR=FALSE, Hurdle=FALSE)