print.penldacv: Print PenalizedLDA CV results.

In penalizedLDA: Penalized Classification using Fisher's Linear Discriminant

Description

A nice print-out of the results obtained from running the CV function for PenalizedLDA.

Usage

## S3 method for class 'penldacv'
print(x,...)

Arguments

x	A "penldacv" object; this is the output of the PenalizedLDA CV function.
...	...

Author(s)

Daniela M. Witten

References

D Witten and R Tibshirani (2011) Penalized classification using Fisher's linear discrimint. To appear in JRSSB.

Examples

set.seed(1)
n <- 20
p <- 100
x <- matrix(rnorm(n*p), ncol=p)
xte <- matrix(rnorm(n*p), ncol=p)
y <- c(rep(1,5),rep(2,5),rep(3,10))
x[y==1,1:10] <- x[y==1,1:10] + 2
x[y==2,11:20] <- x[y==2,11:20] - 2
xte[y==1,1:10] <- xte[y==1,1:10] + 2
xte[y==2,11:20] <- xte[y==2,11:20] - 2
out <- PenalizedLDA(x,y,xte,lambda=.14,K=2)
print(out)
plot(out)
pred.out <- predict(out,xte=xte)
cat("Predictions obtained using PenalizedLDA function and using
predict.penlda function are the same.")
print(cor(pred.out$ypred,out$ypred))

Example output

Number of discriminant vectors:  2
Number of nonzero features in discriminant vector  1 : 29
Number of nonzero features in discriminant vector  2 : 35
Total number of nonzero features:  45

Details:
Type:  standard
Lambda:  0.14
Predictions obtained using PenalizedLDA function and using
predict.penlda function are the same.          [,1]      [,2]
[1,] 1.0000000 0.8361702
[2,] 0.8361702 1.0000000

penalizedLDA documentation built on May 2, 2019, 8:36 a.m.