# 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 {
# determine user supplied alpha and lambda sequences
# alphas=seq(0,1,length=11)
# l0 <- robustHD::lambda0(xout,yout) # use lambda0 function from robustHD package
# lambdas <- seq(l0,0,by=-0.1*l0)
# fit <- enetLTS(xout,yout,alphas=alphas,lambdas=lambdas)
# }
# 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 {
# determine user supplied alpha and lambda sequences
# alphas=seq(0,1,length=11)
# l00 <- lambda00(xout,yout,normalize=TRUE,intercept=TRUE)
# lambdas <- seq(l00,0,by=-0.01*l00)
# fit <- enetLTS(xout,yout,family="binomial",alphas=alphas,lambdas=lambdas)
# }
# NOT RUN {
## for multinomial
n <- 120; p <- 15
NC <- 3 # number of groups
X <- matrix(rnorm(n * p), n, p)
betas <- matrix(1:NC, ncol=NC, nrow=p, byrow=TRUE)
betas[(p-5):p,]=0; betas <- rbind(rep(0,NC),betas)
lv <- cbind(1,X) %*% betas
probs <- exp(lv)/apply(exp(lv),1,sum)
y <- apply(probs,1,function(prob){sample(1:NC, 1, TRUE, prob)})
xout <- X
eps <-0.05 # %10 contamination to only class 0
m <- ceiling(eps*n)
xout[1:m,] <- xout[1:m,] + 10 # bad leverage points
yout <- y
# }
# NOT RUN {
# determine user supplied alpha and lambda sequences
alphas=seq(0,1,length=11)
lambdas <- seq(from=0.95,to=0.05,by=-0.05)
fit <- enetLTS(xout,yout,family="multinomial",alphas=alphas,lambdas=lambdas)
# }
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