frogs {DAAG}R Documentation

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))
}


[Package DAAG version 0.76 Index]