Diagonal Discriminant Analysis
This function implements a simple Gaussian maximum likelihood discriminant rule, for diagonal class covariance matrices.
In machine learning lingo, this is called “Naive Bayes” (for continuous predictors). Note that naive Bayes is more general, as it models discrete predictors as multinomial, i.e., binary predictor variables as Binomial / Bernoulli.
dDA(x, cll, pool = TRUE) ## S3 method for class 'dDA' predict(object, newdata, pool = object$pool, ...) ## S3 method for class 'dDA' print(x, ...) diagDA(ls, cll, ts, pool = TRUE)
x,ls |
learning set data matrix, with rows corresponding to cases (e.g., mRNA samples) and columns to predictor variables (e.g., genes). |
cll |
class labels for learning set, must be consecutive integers. |
object |
object of class |
ts, newdata |
test set (prediction) data matrix, with rows corresponding to cases and columns to predictor variables. |
pool |
logical flag. If true (by default), the covariance matrices
are assumed to be constant across classes and the discriminant rule
is linear in the data. Otherwise ( |
... |
further arguments passed to and from methods. |
diagDA() returns an integer vector of class predictions for the
test set.
Sandrine Dudoit, sandrine@stat.berkeley.edu and
Jane Fridlyand, janef@stat.berkeley.edu originally wrote
stat.diag.da() in CRAN package sma which was modified
for speedup by Martin Maechler maechler@R-project.org
who also introduced dDA etc.
S. Dudoit, J. Fridlyand, and T. P. Speed. (2000) Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. (Statistics, UC Berkeley, June 2000, Tech Report \#576)
lda and qda from the
MASS package;
naiveBayes from e1071.
## two artificial examples by Andreas Greutert:
d1 <- data.frame(x = c(1, 5, 5, 5, 10, 25, 25, 25, 25, 29),
y = c(4, 1, 2, 4, 4, 4, 6:8, 7))
n.plot(d1)
library(cluster)
(cl1P <- pam(d1,k=4)$cluster) # 4 surprising clusters
with(d1, points(x+0.5, y, col = cl1P, pch =cl1P))
i1 <- c(1,3,5,6)
tr1 <- d1[-i1,]
cl1. <- c(1,2,1,2,1,3)
cl1 <- c(2,2,1,1,1,3)
plot(tr1, cex=2, col = cl1, pch = 20+cl1)
(dd.<- diagDA(tr1, cl1., ts = d1[ i1,]))# ok
(dd <- diagDA(tr1, cl1 , ts = d1[ i1,]))# ok, too!
points(d1[ i1,], pch = 10, cex=3, col = dd)
## use new fit + predict instead :
(r1 <- dDA(tr1, cl1))
(r1.<- dDA(tr1, cl1.))
stopifnot(dd == predict(r1, new = d1[ i1,]),
dd.== predict(r1., new = d1[ i1,]))
plot(tr1, cex=2, col = cl1, bg = cl1, pch = 20+cl1,
xlim=c(1,30), ylim= c(0,10))
xy <- cbind(x= runif(500, min=1,max=30), y = runif(500, min=0, max=10))
points(xy, cex= 0.5, col = predict(r1, new = xy))
abline(v=c( mean(c(5,25)), mean(c(25,29))))
## example where one variable xj has Var(xj) = 0:
x4 <- matrix(c(2:4,7, 6,8,5,6, 7,2,3,1, 7,7,7,7), ncol=4)
y <- c(2,2, 1,1)
m4.1 <- dDA(x4, y, pool = FALSE)
m4.2 <- dDA(x4, y, pool = TRUE)
xx <- matrix(c(3,7,5,7), ncol=4)
predict(m4.1, xx)## gave integer(0) previously
predict(m4.2, xx)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.