print.enetLTS {enetLTS} | R Documentation |
"enetLTS"
object
Print a summary of the enetLTS
object.
## S3 method for class 'enetLTS'
print(x,vers=c("reweighted","raw"),zeros=FALSE,...)
x |
fitted |
vers |
a character string specifying for which fit to make
predictions. Possible values are |
zeros |
a logical indicating whether to keep zero coefficients
( |
... |
additional arguments from the |
The call that produced the enetLTS
object is printed, followed by
the coefficients, the number of nonzero coefficients and penalty parameters.
The produced object, the coefficients, the number of nonzero coefficients and penalty parameters are returned.
Fatma Sevinc KURNAZ, Irene HOFFMANN, Peter FILZMOSER
Maintainer: Fatma Sevinc KURNAZ <fatmasevinckurnaz@gmail.com>;<fskurnaz@yildiz.edu.tr>
enetLTS
,
predict.enetLTS
,
coef.enetLTS
## for gaussian
set.seed(86)
n <- 100; p <- 25 # number of observations and variables
beta <- rep(0,p); beta[1:6] <- 1 # 10% nonzero coefficients
sigma <- 0.5 # controls signal-to-noise ratio
x <- matrix(rnorm(n*p, sigma),nrow=n)
e <- rnorm(n,0,1) # error terms
eps <- 0.1 # contamination level
m <- ceiling(eps*n) # observations to be contaminated
eout <- e; eout[1:m] <- eout[1:m] + 10 # vertical outliers
yout <- c(x %*% beta + sigma * eout) # response
xout <- x; xout[1:m,] <- xout[1:m,] + 10 # bad leverage points
fit1 <- enetLTS(xout,yout,alphas=0.5,lambdas=0.05,plot=FALSE)
print(fit1)
print(fit1,vers="raw")
print(fit1,vers="raw",zeros=TRUE)
print(fit1,zeros=TRUE)
## for binomial
eps <-0.05 # %10 contamination to only class 0
m <- ceiling(eps*n)
y <- sample(0:1,n,replace=TRUE)
xout <- x
xout[y==0,][1:m,] <- xout[1:m,] + 10; # class 0
yout <- y # wrong classification for vertical outliers
fit2 <- enetLTS(xout,yout,family="binomial",alphas=0.5,lambdas=0.05,plot=FALSE)
print(fit2)
print(fit2,vers="raw")
print(fit2,vers="raw",zeros=TRUE)
print(fit2,zeros=TRUE)