ASMbook (version 1.0.2)

simDat13: Simulate data for Chapter 13: Poisson ANCOVA

Description

Simulate parasite load ~ size regressions in 3 populations of goldenring dragonflies

Usage

simDat13(nPops = 3, nSample = 100, beta.vec = c(-2, 1, 2, 4, -2, -5))

Value

A list of simulated data and parameters.

nPops

Number of populations

nSample

Number of samples per population

beta

Vector of regression coefficients

x

Indicator for population number

pop

Population name (factor)

orig.length

Wing length, non-centered

wing.length

Wing length, centered

load

Simulated parasite loads

Arguments

nPops

Number of populations

nSample

Number of samples per population

beta.vec

Vector of regression coefficients

Author

Marc Kéry

Examples

str(dat <- simDat13())      # Implicit default arguments

# Revert to main-effects model with parallel lines on the log link scale
str(dat <- simDat13(nSample = 100, beta.vec = c(-2, 1, 2, 4, 0, 0)))

# Same with less strong regression coefficient
str(dat <- simDat13(nSample = 100, beta.vec = c(-2, 1, 2, 3, 0, 0)))

# Revert to simple linear Poisson regression: no effect of population (and less strong coefficient)
str(dat <- simDat13(nSample = 100, beta.vec = c(-2, 0, 0, 3, 0, 0)))

# Revert to one-way ANOVA Poisson model: no effect of wing length
# (Choose larger sample size and greater differences in the intercepts to better show patterns)
str(dat <- simDat13(nSample = 100, beta.vec = c(-1, 3, 5, 0, 0, 0)))

# Revert to Poisson "model-of-the-mean": no effects of either wing length or population
# Intercept chosen such that average parasite load is 10
str(dat <- simDat13(nSample = 100, beta.vec = c(log(10), 0, 0, 0, 0, 0)))
mean(dat$load)        # Average is about 10