shipunov (version 1.0)

Gradd: Classification grid

Description

Adds to the 2D ordination plot small semi-transparent points which make color classification grid

Usage

Gradd(model2var, data2var, spacing=75, trnsp=0.3, pch=20, cex=0.2,
 palette=NULL, type="ids", ...)

Arguments

model2var

model based on data2var

data2var

data with exactly 2 variables

spacing

space between points

trnsp

transparency

pch

type of point

cex

scale of point

palette

palette to use

type

type of the model: "ids", "lda", "neuralnet", "tree", or "user" (see examples)

...

arguments to 'plot()'

Details

'Gradd()' adds to the 2D ordination plot small semi-transparent points which make color classification grid.

Requires model with 'predict' method to be computed first.

Model should use ids (to make colors) and exactly 2 variables with names same as data2var column names, e.g:

model2var <- somefunction(ids ~ ., data=cbind(ids, data2var))

If type="user", uses predefined 'User.Predict(model2var, X)' function which must return factor ids from testing X data.

Please see examples to understand all of these better.

Note that instead of dots, one can use contours, but they are harder to employ because they need membership values in order to calculate borders (places where memberships are equal).

Examples

# NOT RUN {
## SVM:
library(e1071)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.svm.pca <- svm(Species ~ ., data=cbind(iris[5], iris.p))
plot(iris.p, type="n", main="SVM")
Gradd(iris.svm.pca, iris.p)
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## LDA:
library(MASS)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.lda.pca <- lda(Species ~ . , data=cbind(iris[5], iris.p))
plot(iris.p, type="n", main="LDA")
Gradd(iris.lda.pca, iris.p, type="lda")
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## 'tree::tree':
library(tree)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.tree.pca <- tree(Species ~ . , data=cbind(iris[5], iris.p))
plot(iris.p, type="n", main="tree")
Gradd(iris.tree.pca, iris.p, type="tree")
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## randomForest:
library(randomForest)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.rf.pca <- randomForest(Species ~ ., data=cbind(iris[5], iris.p))
plot(iris.p, type="n", main="randomForest")
Gradd(iris.rf.pca, iris.p)
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## naiveBayes:
library(e1071)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.nb.pca <- naiveBayes(Species ~ ., data=cbind(iris[5], iris.p))
plot(iris.p, type="n", main="naiveBayes")
Gradd(iris.nb.pca, iris.p)
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## neuralnet:
library(neuralnet)
iris.p2 <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.p2 <- cbind(iris.p2, Tobin(iris$Species, convert.names=FALSE))
iris.nn.pca <- neuralnet(setosa + versicolor + virginica ~ PC1 + PC2, data=iris.p2,
 hidden=3, lifesign="full")
plot(iris.p, type="n", main="neuralnet")
Gradd(iris.nn.pca, iris.p, type="neuralnet")
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## rpart + MDS for the base plot:
iris.dist <- dist(iris[, 1:4], method="manhattan")
iris.dist[iris.dist == 0] <- abs(jitter(0))
library(MASS)
iris.m <- isoMDS(iris.dist)$points
colnames(iris.m) <- c("Dim1", "Dim2")
library(rpart)
iris.rpart.mds <- rpart(Species ~ . , data=cbind(iris[5], iris.m))
plot(iris.m, type="n", main="rpart + MDS")
Gradd(iris.rpart.mds, iris.m, type="tree")
text(iris.m, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## QDA:
library(MASS)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.qda.pca <- qda(Species ~ . , data=cbind(iris[5], iris.p))
plot(iris.p, type="n", main="QDA")
Gradd(iris.qda.pca, iris.p, type="lda")
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## AdaBoost:
# }
# NOT RUN {
library(adabag)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.ada.pca <- boosting(Species ~ . , data=cbind(iris[5], iris.p)) # slow!
plot(iris.p, type="n", main="AdaBoost")
Gradd(iris.ada.pca, iris.p, type="lda")
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
# }
# NOT RUN {
##
## kNN:
library(class)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
plot(iris.p, type="n", main="kNN")
User.Predict <- function(model2var, X) knn(train=model2var[, 2:3], test=X,
 cl=model2var[, 1], k=5)
Gradd(cbind(iris[5], iris.p), iris.p, type="user")
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
## nnet:
library(nnet)
iris.p <- prcomp(iris[, 1:4], scale=TRUE)$x[, 1:2]
iris.nnet.pca <- nnet(Species ~ . , data=cbind(iris[5], iris.p), size=4)
plot(iris.p, type="n", main="nnet")
Gradd(iris.nnet.pca, iris.p, type="tree")
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))
##
# }