Hi,
I did it with
Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp =
rep(c("s","c","v"), rep(50,3)))
train <- sample(1:150, 75) table(Iris$Sp[train])
z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train)
Then I did plot(z,xlim=c(-10,10),ylim=c(-10,10)) before drawing
points(predict(z)$x,
col=palette()[predict(z)$class],xlim=c(-10,10),ylim=c(-10,10)) and
all the
points are superimposed. The only difference I found was the
different x-
and y-axis when I drew them separately, i.e.
plot(z)
plot(predict(z)$x, col=palette()[predict(z)$class])
Alain
Alejo C.S. wrote:
I'm confused on how is the right way to plot a discriminant
analysis made
by
lda function (MASS package).
(I had attached my data fro reproduction). When I plot a lda
object :
X <- read.table("data", header=T)
lda_analysis <- lda(formula(X), data=X)
plot(lda_analysis)
#the above plot is completely different to:
plot(predict(lda_analysis)$x, col=palette()[predict(lda_analysis)
$class])
that should be the same graph than the first?
In the second case, I use predict function to obtain the LD1 and LD2
coordinates of lda_analysis (predict(lda_analysis)$x) and it's
respective
class (predict(lda_analysis)$class), but it seems that the classes
are
different:
table(X$G3, predict(lda_analysis)$class)
B G M
B 29 0 3
G 0 26 2
M 4 0 46
any clues?
Regards,
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Alain Guillet
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SMCS - Institut de statistique - Université catholique de Louvain
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