simDat16: Simulate data for Chapter 16: Binomial ANCOVA

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

Simulate data for Chapter 16: Binomial ANCOVA

Description

Simulate Number black individuals ~ wetness regressions in adders in 3 regions

Usage

simDat16(nRegion = 3, nSite = 10, beta.vec = c(-4, 1, 2, 6, 2, -5))

Arguments

nRegion	Number of regions
nSite	Number of sites per region
beta.vec	Vector of regression coefficients

Value

A list of simulated data and parameters.

nRegion	Number of regions
nSite	Number of sites per region
beta	Vector of regression coefficients
x	Indicator for region number
region	Region name (factor)
wetness	Wetness covariate
N	Number of adders captured at each site
C	Number of black adders captured at each site

Author(s)

Marc Kéry

Examples

str(dat <- simDat16())      # Implicit default arguments

# Revert to main-effects model with parallel lines on the logit link scale
# (also larger sample size to better see patterns)
str(dat <- simDat16(nSite = 100, beta.vec = c(-4, 1, 2, 6, 0, 0)))

# Same with less strong logistic regression coefficient
str(dat <- simDat16(nSite = 100, beta.vec = c(-4, 1, 2, 3, 0, 0)))

# Revert to simple logit-linear binomial regression: no effect of pop (and weaker coefficient)
str(dat <- simDat16(nSite = 100, beta.vec = c(-4, 0, 0, 3, 0, 0)))

# Revert to one-way ANOVA binomial model: no effect of wetness
# (Choose greater differences in the intercepts to better show patterns)
str(dat <- simDat16(nSite = 100, beta.vec = c(-2, 2, 3, 0, 0, 0)))

# Revert to binomial "model-of-the-mean": no effects of either wetness or population
# Intercept chosen such that average proportion of black adders is 0.6
str(dat <- simDat16(nSite = 100, beta.vec = c(qlogis(0.6), 0, 0, 0, 0, 0)))
mean(dat$C / dat$N)        # Average is about 0.6

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