simDat17: Simulate data for Chapter 17: Binomial GLMM

In ASMbook: Functions for the Book "Applied Statistical Modeling for Ecologists"

Simulate data for Chapter 17: Binomial GLMM

Description

Simulate Number of successful pairs ~ precipitation regressions in 16 populations of woodchat shrikes

Usage

simDat17(
  nPops = 16,
  nYears = 10,
  mu.alpha = 0,
  mu.beta = -2,
  sigma.alpha = 1,
  sigma.beta = 1
)

Arguments

nPops	Number of populations
nYears	Number of years sampled in each population
mu.alpha	Mean of random intercepts
mu.beta	Mean of random slopes
sigma.alpha	SD of random intercepts
sigma.beta	SD of random slopes

Value

A list of simulated data and parameters.

nPops	Number of populations
nYears	Number of years sampled
mu.alpha	Mean of random intercepts
sigma.alpha	SD of random intercepts
mu.beta	Mean of random slopes
sigma.beta	SD of random slopes
pop	Population index
precip	Precipitation covariate values
alpha	Random intercepts
beta	Random slopes
N	Number of shrike pairs at each site
C	Number of successful shrike pairs at each site

Author(s)

Marc Kéry

Examples

library(lattice)
str(dat <- simDat17())      # Implicit default arguments (DOES NOT PRODUCE PLOT FOR SOME REASON)
xyplot(dat$C/dat$N ~ dat$precip | dat$pop, ylab = "Realized woodchat shrike breeding success ", 
       xlab = "Spring precipitation index", main = "Realized breeding success", pch = 16, cex = 1.2,
      col = rgb(0, 0, 0, 0.4))

# Revert to random intercept model. Increased sigma.alpha to emphasize the random intercepts part
str(dat <- simDat17(nPops = 16, sigma.alpha = 1, sigma.beta = 0))
xyplot(dat$C/dat$N ~ dat$precip | dat$pop, ylab = "Realized woodchat shrike breeding success ", 
       xlab = "Spring precipitation index", 
       main = "Realized breeding success (random-intercepts model)",
      pch = 16, cex = 1.2, col = rgb(0, 0, 0, 0.4))

# Revert to random-effects one-way binomial ANOVA model: random intercepts, but zero slopes
str(dat <- simDat17(nPops = 16, sigma.alpha = 1, mu.beta = 0, sigma.beta = 0))
xyplot(dat$C/dat$N ~ dat$precip | dat$pop, ylab = "Realized woodchat shrike breeding success ", 
       xlab = "Spring precipitation index",
       main = "Realized breeding success (random-effects,
       one-way binomial ANOVA model)", 
       pch = 16, cex = 1.2, col = rgb(0, 0, 0, 0.4))

# Revert to simple log-linear binomial (i.e., logistic) regression
#   (= no effects of pop on either intercepts or slopes)
str(dat <- simDat17(nPops = 16, sigma.alpha = 0, sigma.beta = 0))
xyplot(dat$C/dat$N ~ dat$precip | dat$pop, ylab = "Realized woodchat shrike breeding success ", 
       xlab = "Spring precipitation index", 
       main = "Realized breeding success\n(simple logistic regression model)", 
       pch = 16, cex = 1.2, col = rgb(0, 0, 0, 0.4))

# Revert to binomial "model-of-the-mean": no effects of either population or precipitation
str(dat <- simDat17(nPops = 16, sigma.alpha = 0, mu.beta = 0, sigma.beta = 0))
xyplot(dat$C/dat$N ~ dat$precip | dat$pop, ylab = "Realized woodchat shrike breeding success ", 
       xlab = "Spring precipitation index", 
       main = "Realized breeding success (binomial 'model-of-the-mean')",
      pch = 16, cex = 1.2, col = rgb(0, 0, 0, 0.4))

ASMbook documentation built on Sept. 11, 2024, 5:38 p.m.