X-Git-Url: https://git.auder.net/?p=agghoo.git;a=blobdiff_plain;f=agghoo.Rcheck%2F00_pkg_src%2Fagghoo%2Fexample%2Fexample.R;fp=agghoo.Rcheck%2F00_pkg_src%2Fagghoo%2Fexample%2Fexample.R;h=0000000000000000000000000000000000000000;hp=7fae2ce139f5d1c9b58050489f1cefda9136172a;hb=16906f6e8c432b811ddf99da1b18a2a357a75235;hpb=97f16440280a40a49c4898a75942e374880bfca3

diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/example/example.R b/agghoo.Rcheck/00_pkg_src/agghoo/example/example.R
deleted file mode 100644
index 7fae2ce..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/example/example.R
+++ /dev/null
@@ -1,43 +0,0 @@
-library(agghoo)
-
-data(iris) #already there
-library(mlbench)
-data(PimaIndiansDiabetes)
-
-# Run only agghoo on iris dataset (split into train/test, etc).
-# Default parameters: see ?agghoo and ?AgghooCV
-compareTo(iris[,-5], iris[,5], agghoo_run)
-
-# Run both agghoo and standard CV, specifiying some parameters.
-compareTo(iris[,-5], iris[,5], list(agghoo_run, standardCV_run), gmodel="tree")
-compareTo(iris[,-5], iris[,5], list(agghoo_run, standardCV_run),
-          gmodel="knn", params=c(3, 7, 13, 17, 23, 31),
-          CV = list(type="vfold", V=5, shuffle=T))
-
-# Run both agghoo and standard CV, averaging errors over N=10 runs
-# (possible for a single method but wouldn't make much sense...).
-compareMulti(PimaIndiansDiabetes[,-9], PimaIndiansDiabetes[,9],
-             list(agghoo_run, standardCV_run), N=10, gmodel="rf")
-
-# Compare several values of V
-compareRange(PimaIndiansDiabetes[,-9], PimaIndiansDiabetes[,9],
-             list(agghoo_run, standardCV_run), N=10, V_range=c(10, 20, 30))
-
-# For example to use average of squared differences.
-# Default is "mean(abs(y1 - y2))".
-loss2 <- function(y1, y2) mean((y1 - y2)^2)
-
-# In regression on artificial datasets (TODO: real data?)
-data <- mlbench.twonorm(300, 3)$x
-target <- rowSums(data)
-compareMulti(data, target, list(agghoo_run, standardCV_run),
-             N=10, gmodel="tree", params=c(1, 3, 5, 7, 9), loss=loss2,
-             CV = list(type="MC", V=12, test_size=0.3))
-
-compareMulti(data, target, list(agghoo_run, standardCV_run),
-             N=10, floss=loss2, CV = list(type="vfold", V=10, shuffle=F))
-
-# Random tests to check that method doesn't fail in 1D case
-M <- matrix(rnorm(200), ncol=2)
-compareTo(as.matrix(M[,-2]), M[,2], list(agghoo_run, standardCV_run), gmodel="knn")
-compareTo(as.matrix(M[,-2]), M[,2], list(agghoo_run, standardCV_run), gmodel="tree")