| covsample {DAAGxtras} | R Documentation |
Forest cover type is recorded, for every 50th observation taken from 581012 observations in the original dataset, together with a physical geographical variables that may account for the forest cover type.
data(covsample)
A data frame with 11318 observations on the following 55 variables.
V1V2V3V4V5V6V7V8V9V10V11V12V13V14V15V16V17V18V19V20V21V22V23V24V25V26V27V28V29V30V31V32V33V34V35V36V37V38V39V40V41V42V43V44V45V46V47V48V49V50V51V52V53V54V55For details, see http://kdd.ics.uci.edu/databases/covertype/covertype.data.html
For detailed information on the UCI dataset, see http://kdd.ics.uci.edu/databases/covertype/covertype.data.html
Variables V1 to V54 are physical geographical
variables. Variable V55 is cover type, one of types 1 - 7.
Note the omission of any information on geographical location. Distance through the data seems however to be, in part, a proxy for geographical location.
http://kdd.ics.uci.edu/databases/covertype/covertype.html
Blackard, Jock A. 1998. "Comparison of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types." Ph.D. dissertation. Department of Forest Sciences. Colorado State University. Fort Collins, Colorado.
data(covsample) options(digits=3) tab.sample <- table(covsample$V55) tab.sample/sum(tab.sample) rm(covsample) data(covtrain) tab.train <- table(covtrain$V55) tab.train/sum(tab.train) rm(covtrain) data(covtest) tab.test <- table(covtest$V55) tab.test/sum(tab.test) rm(covtest)