frogs | R Documentation |
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.
frogs
This data frame contains the following columns:
0 = frogs were absent, 1 = frogs were present
reference point
reference point
altitude , in meters
distance in meters to nearest extant population
number of potential breeding pools
(number of potential breeding sites within a 2 km radius
mean rainfall for Spring period
mean minimum Spring temperature
mean maximum Spring temperature
Hunter, D. (2000) The conservation and demography of the southern corroboree frog (Pseudophryne corroboree). M.Sc. thesis, University of Canberra, Canberra.
print("Multiple Logistic Regression - Example 8.2")
plot(northing ~ easting, data=frogs, pch=c(1,16)[frogs$pres.abs+1],
xlab="Meters east of reference point", ylab="Meters north")
pairs(frogs[,4:10])
attach(frogs)
pairs(cbind(altitude,log(distance),log(NoOfPools),NoOfSites),
panel=panel.smooth, labels=c("altitude","log(distance)",
"log(NoOfPools)","NoOfSites"))
detach(frogs)
frogs.glm0 <- glm(formula = pres.abs ~ altitude + log(distance) +
log(NoOfPools) + NoOfSites + avrain + meanmin + meanmax,
family = binomial, data = frogs)
summary(frogs.glm0)
frogs.glm <- glm(formula = pres.abs ~ log(distance) + log(NoOfPools) +
meanmin +
meanmax, family = binomial, data = frogs)
oldpar <- par(mfrow=c(2,2))
termplot(frogs.glm, data=frogs)
termplot(frogs.glm, data=frogs, partial.resid=TRUE)
cv.binary(frogs.glm0) # All explanatory variables
pause()
cv.binary(frogs.glm) # Reduced set of explanatory variables
for (j in 1:4){
rand <- sample(1:10, 212, replace=TRUE)
all.acc <- cv.binary(frogs.glm0, rand=rand, print.details=FALSE)$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))
}
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