nonzeroCoef.enetLTS

0th

Percentile

nonzero coefficients indices from the "enetLTS" object

A numeric vector which gives the indices of nonzero coefficients from the current model.

Keywords
regression, classification
Usage
nonzeroCoef.enetLTS(beta)
Arguments
beta

Coefficient vector

Value

A numeric vector containing the requeste.

See Also

enetLTS, predict.enetLTS, coef.enetLTS

Aliases
  • nonzeroCoef.enetLTS
Examples
# NOT RUN {
## 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

# }
# NOT RUN {
fit1 <- enetLTS(xout,yout,alphas=0.5,lambdas=0.05,plot=FALSE)
beta1 <- coef(fit1)
nonzeroCoef.enetLTS(beta1)
# }
# NOT RUN {
## 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

# }
# NOT RUN {
fit2 <- enetLTS(xout,yout,family="binomial",alphas=0.5,lambdas=0.05,plot=FALSE)
beta1 <- coef(fit2,vers="raw")
nonzeroCoef.enetLTS(beta1)
# }
Documentation reproduced from package enetLTS, version 0.1.0, License: GPL (>= 3)

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