simDat14: Simulate data for Chapter 14: Poisson GLMM

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

Simulate data for Chapter 14: Poisson GLMM

Description

Simulate count ~ year regressions in 16 populations of red-backed shrikes

Usage

simDat14(
  nPops = 16,
  nYears = 30,
  mu.alpha = 3,
  sigma.alpha = 1,
  mu.beta = -2,
  sigma.beta = 0.6
)

Arguments

nPops	Number of populations
nYears	Number of years sampled in each population
mu.alpha	Mean of random intercepts
sigma.alpha	SD of random intercepts
mu.beta	Mean of random slopes
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
orig.year	Year values, non-scaled
year	Year values, scaled to be between 0 and 1
alpha	Random intercepts
beta	Random slopes
C	Simulated shrike counts

Author(s)

Marc Kéry

Examples

library(lattice)
str(dat <- simDat14())
xyplot(dat$C ~ dat$orig.year | dat$pop, ylab = "Red-backed shrike counts", xlab = "Year", pch = 16,
       cex = 1.2, col = rgb(0, 0, 0, 0.4), 
       main = 'Realized population trends\n(random-coefficients model)') # works

# Revert to random intercept model. Increased sigma.alpha to emphasize the random intercepts part
str(dat <- simDat14(nPops = 16, sigma.alpha = 1, sigma.beta = 0))
xyplot(dat$C ~ dat$orig.year | dat$pop, ylab = "Red-backed shrike counts", xlab = "Year",
       pch = 16, cex = 1.2, col = rgb(0, 0, 0, 0.4), 
       main = 'Realized population trends (random-intercepts model)')

# Revert to random-effects one-way Poisson ANOVA model: random intercepts, but zero slopes
str(dat <- simDat14(nPops = 16, sigma.alpha = 1, mu.beta = 0, sigma.beta = 0))
xyplot(dat$C ~ dat$orig.year | dat$pop, ylab = "Red-backed shrike counts", xlab = "Year",
       pch = 16, cex = 1.2, col = rgb(0, 0, 0, 0.4), 
       main = 'Realized population trends
       (random-effects, one-way Poisson ANOVA model)')

# Revert to simple log-linear Poisson regression (no effects of pop on intercepts or slopes)
str(dat <- simDat14(nPops = 16, sigma.alpha = 0, sigma.beta = 0))
xyplot(dat$C ~ dat$orig.year | dat$pop, ylab = "Red-backed shrike counts", 
       xlab = "Year", pch = 16, cex = 1.2, col = rgb(0, 0, 0, 0.4), 
       main = 'Realized population trends\n(simple log-linear Poisson regression)')

# Revert to Poisson "model-of-the-mean": no effects of either population or body length
str(dat <- simDat14(nPops = 16, sigma.alpha = 0, mu.beta = 0, sigma.beta = 0))
xyplot(dat$C ~ dat$orig.year | dat$pop, ylab = "Red-backed shrike counts", 
       xlab = "Year", pch = 16, cex = 1.2, col = rgb(0, 0, 0, 0.4), 
       main = 'Realized population trends\n(Poisson "model-of-the-mean")')

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