Frogs Data

Description

The frogs data frame has 212 rows and 11 columns. The data are on the distribution of the Southern Corroboree frog, which occurs in the Snowy Mountains area of New South Wales, Australia.

Usage

frogs

Format

This data frame contains the following columns:

pres.abs

0 = frogs were absent, 1 = frogs were present

northing

reference point

easting

reference point

altitude

altitude , in meters

distance

distance in meters to nearest extant population

NoOfPools

number of potential breeding pools

NoOfSites

(number of potential breeding sites within a 2 km radius

avrain

mean rainfall for Spring period

meanmin

mean minimum Spring temperature

meanmax

mean maximum Spring temperature

Source

Hunter, D. (2000) The conservation and demography of the southern corroboree frog (Pseudophryne corroboree). M.Sc. thesis, University of Canberra, Canberra.

Examples

print("Multiple Logistic Regression - Example 8.2")

plot(northing ~ easting, data=frogs, pch=c(1,16)[frogs$pres.abs+1],
  xlab="Meters east of reference point", ylab="Meters north")

pause()

oldpar <- par(oma=c(2,2,2,2), cex=0.5)
pairs(frogs[,4:10])
par(oldpar)

pause()

oldpar <- par(mfrow=c(1,3))
for(nam in c("distance","NoOfPools","NoOfSites")){
  y <- frogs[,nam]
  plot(density(y),main="",xlab=nam)
par(oldpar)
}

pause()

attach(frogs)
pairs(cbind(altitude,log(distance),log(NoOfPools),NoOfSites),
  panel=panel.smooth, labels=c("altitude","log(distance)",
  "log(NoOfPools)","NoOfSites"))
detach(frogs)

frogs.glm0 <- glm(formula = pres.abs ~ altitude + log(distance) +
  log(NoOfPools) + NoOfSites + avrain + meanmin + meanmax,
  family = binomial, data = frogs)
summary(frogs.glm0)
pause()

frogs.glm <- glm(formula = pres.abs ~ log(distance) + log(NoOfPools) + 
meanmin +
  meanmax, family = binomial, data = frogs)
oldpar <- par(mfrow=c(2,2))
termplot(frogs.glm, data=frogs)
par(oldpar)
pause()

termplot(frogs.glm, data=frogs, partial.resid=TRUE)

cv.binary(frogs.glm0)   # All explanatory variables
pause()

cv.binary(frogs.glm)    # Reduced set of explanatory variables

pause()

for (j in 1:4){
 rand <- sample(1:10, 212, replace=TRUE)
 all.acc <- cv.binary(frogs.glm0, rand=rand, print.details=FALSE)$acc.cv
 reduced.acc <- cv.binary(frogs.glm, rand=rand, print.details=FALSE)$acc.cv
 cat("\nAll:", round(all.acc,3), "  Reduced:", round(reduced.acc,3))
}