pondfrog: Fake Data of the Numbers of Frogs in Ponds

Description Usage Format Details Examples

Description

The data contain frog population sizes in different ponds with some characteristics of ponds. The data is simulated, thus the "true" model is known. The data can serve to play with different methods for doing model selection.

Usage

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Format

A data frame with 130 observations on the following 9 variables.

frog

a numeric vector

fish

a numeric vector

vegdensity

a numeric vector

ph

a numeric vector

surfacearea

a numeric vector

waterdepth

a numeric vector

region

a factor with levels north south

height

a numeric vector

temp

a numeric vector

Details

The r-code for producing the pondfrog data is

set.seed(196453) n <- 130 # sample size height <- sample(150:1500,n) region <- sample(c("south", "north"), n, replace=TRUE, prob=c(0.2, 0.8)) waterdepth <- sample(seq(0.3, 5.5, by=0.01), n) surfacearea <- sample(seq(3, 150), n) temp <- 20 - 0.01*height + 0.5*as.numeric(region=="south") -0.005*waterdepth + 0.1*sqrt(surfacearea) +rnorm(n, 0, 1.5) ph <- 7.5 - 0.8 * as.numeric(region=="south") + rnorm(n, 0, 0.2) vegdensity.logitp <- -3.5+0.3*ph + 0.2*temp+rnorm(n,0,1) vegdensity.p <- plogis(vegdensity.logitp) vegdensity <- rbinom(n, 1, prob=vegdensity.p) fish.logitp <- -4+0.3*ph + 0.2*waterdepth+rnorm(n,0,1) fish.p <- plogis(fish.logitp) fish <- rbinom(n, 1, prob=fish.p) frog.mu <- exp(3.5 + 0.2*(temp-mean(temp)) +0.2*(ph-mean(ph)) + 0.1*(ph-mean(ph))^2 - 0.3*(waterdepth-mean(waterdepth)) - 0.5 * fish + 0.5*fish*vegdensity) frog <- rpois(n, lambda=frog.mu)

dat <- data.frame(frog=frog, fish=fish, vegdensity=vegdensity, ph=ph, surfacearea=surfacearea, waterdepth=waterdepth, region=region, height=height, temp=temp)

Thus, the "true" model for the number of pondfrog (frog) is a Poisson model with log-link function and the following linear predictor:

3.5 + 0.2*(temp-mean(temp)) +0.2*(ph-mean(ph)) + 0.1*(ph-mean(ph))^2 - 0.3*(waterdepth-mean(waterdepth)) - 0.5 * fish + 0.5*fish*vegdensity

Examples

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blmeco documentation built on Dec. 5, 2019, 5:09 p.m.