ASMbook (version 1.0.2)

simDat17: 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
)

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

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

Author

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))