Gradd: Classification grid and decision boundaries

In shipunov: Miscellaneous Functions from Alexey Shipunov

Classification grid and decision boundaries

Description

Adds to the 2D ordination either colored points to make classification grid, or lines to show decision boundaries

Usage

Gradd(model2var, data2var, spacing=75, what="points",
 trnsp=0.2, pch=16, cex=0.8, lwd=2, lty=2, lcol="grey", palette=NULL,
 type="ids", User.Predict=function(model2var, X) {}, ...)

Arguments

model2var	Model based on 'data2var' (see below).
data2var	Data with _exactly_ 2 variables, e.g., result of PCA.
spacing	Density of points to predict.
what	What to draw: either "points" for classification grid, or "lines" for decision boundaries
trnsp	Transparency of points.
pch	Type of points.
cex	Scale of points.
lwd	Width of lines.
lty	Type of lines.
lcol	Color of lines.
palette	Palette to use.
type	Type of the model: "ids", "lda", "tree", or "user" (see examples).
User.Predict	Function to define in case of 'type="user"'.
...	Additional arguments to points() or contour().

Details

Gradd() takes model and its 2D data, makes new data with the same range but made of dense equidistantly spaced (grid-like) points, then predicts class labels from this new data, probably also calculates decision boundaries, and finally plots either points colored by predition, or lines along boundaries.

Before you run Gradd(), make the model. This model should have 'predict' method, and use ids (to make colors) and exactly 2 variables with names same as 'data2var' column names, e.g:

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

If the model type is "user", the Gradd() uses predefined 'User.Predict(model2var, X)' function which must return factor ids from testing X data (see examples).

To plot both lines and grid, use Gradd() twice.

Gradd() is mainly a teching demo. It is useful if the goal is to illustrate the general properties of the supervised method and/or underlying data. It uses the entire 2D dataset to learn new data but learning from training subset is also possible, see the Naive Bayes example below.

Author(s)

Alexey Shipunov

Examples

## SVM:
library(e1071)
iris.p <- prcomp(iris[, -5])$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) # type="ids" (default)
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))

## LDA:
library(MASS)
iris.p <- prcomp(iris[, -5])$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() (note how to draw decision boundaries):
library(tree)
iris.p <- prcomp(iris[, -5])$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", what="lines")
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))

## randomForest:
library(randomForest)
iris.p <- prcomp(iris[, -5])$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) # type="ids" (default)
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))

## naiveBayes (note how to use training subsample):
library(e1071)
iris.p <- prcomp(iris[, -5])$x[, 1:2]
sel <- 1:nrow(iris) 
plot(iris.p, col=iris$Species, pch=ifelse(sel, 19, 1), main="naiveBayes")
iris.nb2 <- naiveBayes(Species ~ ., data=cbind(iris[5], iris.p)[sel, ])
Gradd(iris.nb2, iris.p[sel, ], what="lines")

## rpart (note how to use MDS for the base plot):
iris.dist <- dist(iris[, -5], 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[, -5])$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"))

## nnet:
library(nnet)
iris.p <- prcomp(iris[, -5])$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"))

## kNN (note how to employ User.Predict()):
library(class)
iris.p <- prcomp(iris[, -5])$x[, 1:2]
plot(iris.p, type="n", main="kNN")
Gradd(cbind(iris[5], iris.p), iris.p, type="user",
 User.Predict=function(model2var, X) knn(model2var[, 2:3], X, model2var[, 1], k=5))
text(iris.p, col=as.numeric(iris[, 5]), labels=abbreviate(iris[, 5], 1,
 method="both.sides"))

shipunov documentation built on Feb. 16, 2023, 9:05 p.m.