On this Picostat.com statistics page, you will find information about the frogs data set which pertains to Frogs Data. The frogs data set is found in the DAAG R package. You can load the frogs data set in R by issuing the following command at the console data("frogs"). This will load the data into a variable called frogs. If R says the frogs data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the frogs R data set. The size of this file is about 12,680 bytes.

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$pres.abs+1],
  xlab="Meters east of reference point", ylab="Meters north")
pairs(frogs[,4:10])
attach(frogs)
pairs(cbind(altitude,log(distance),log(NoOfPools),NoOfSites),
  panel=panel.smooth, labels=c("altitude","log(distance)",
  "log(NoOfPools)","NoOfSites"))
detach(frogs)frogs.glm0 <- glm(formula = pres.abs ~ altitude + log(distance) +
  log(NoOfPools) + NoOfSites + avrain + meanmin + meanmax,
  family = binomial, data = frogs)
summary(frogs.glm0)frogs.glm <- glm(formula = pres.abs ~ log(distance) + log(NoOfPools) + 
meanmin +
  meanmax, family = binomial, data = frogs)
oldpar <- par(mfrow=c(2,2))
termplot(frogs.glm, data=frogs)termplot(frogs.glm, data=frogs, partial.resid=TRUE)cv.binary(frogs.glm0)   # All explanatory variables
pause()cv.binary(frogs.glm)    # Reduced set of explanatory variablesfor (j in 1:4){
 rand <- sample(1:10, 212, replace=TRUE)
 all.acc <- cv.binary(frogs.glm0, rand=rand, print.details=FALSE)$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))
}

--

Dataset imported from https://www.r-project.org.