MultiNMix (version 0.1.0)

birds: 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).

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)