example/example.R

   1 library(agghoo)
   2
   3 data(iris) #already there
   4 library(mlbench)
   5 data(PimaIndiansDiabetes)
   6
   7 # Run only agghoo on iris dataset (split into train/test, etc).
   8 # Default parameters: see ?agghoo and ?AgghooCV
   9 compareTo(iris[,-5], iris[,5], agghoo_run)
  10
  11 # Run both agghoo and standard CV, specifiying some parameters.
  12 compareTo(iris[,-5], iris[,5], list(agghoo_run, standardCV_run), gmodel="tree")
  13 compareTo(iris[,-5], iris[,5], list(agghoo_run, standardCV_run),
  14           gmodel="knn", params=c(3, 7, 13, 17, 23, 31),
  15           CV = list(type="vfold", V=5, shuffle=T))
  16
  17 # Run both agghoo and standard CV, averaging errors over N=10 runs
  18 # (possible for a single method but wouldn't make much sense...).
  19 compareMulti(PimaIndiansDiabetes[,-9], PimaIndiansDiabetes[,9],
  20              list(agghoo_run, standardCV_run), N=10, gmodel="rf")
  21
  22 # Compare several values of V
  23 compareRange(PimaIndiansDiabetes[,-9], PimaIndiansDiabetes[,9],
  24              list(agghoo_run, standardCV_run), N=10, V_range=c(10, 20, 30))
  25
  26 # For example to use average of squared differences.
  27 # Default is "mean(abs(y1 - y2))".
  28 loss2 <- function(y1, y2) mean((y1 - y2)^2)
  29
  30 # In regression on artificial datasets (TODO: real data?)
  31 data <- mlbench.twonorm(300, 3)$x
  32 target <- rowSums(data)
  33 compareMulti(data, target, list(agghoo_run, standardCV_run),
  34              N=10, gmodel="tree", params=c(1, 3, 5, 7, 9), loss=loss2,
  35              CV = list(type="MC", V=12, test_size=0.3))
  36
  37 compareMulti(data, target, list(agghoo_run, standardCV_run),
  38              N=10, floss=loss2, CV = list(type="vfold", V=10, shuffle=F))
  39
  40 # Random tests to check that method doesn't fail in 1D case
  41 M <- matrix(rnorm(200), ncol=2)
  42 compareTo(as.matrix(M[,-2]), M[,2], list(agghoo_run, standardCV_run), gmodel="knn")
  43 compareTo(as.matrix(M[,-2]), M[,2], list(agghoo_run, standardCV_run), gmodel="tree")