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=7fae2ce139f5d1c9b58050489f1cefda9136172a;hp=0000000000000000000000000000000000000000;hb=97f16440280a40a49c4898a75942e374880bfca3;hpb=3b8affec63125c3816a3d15f0f49776dc14867a2

diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/example/example.R b/agghoo.Rcheck/00_pkg_src/agghoo/example/example.R
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+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")