View source: R/residuals.enetLTS.R
residuals.enetLTS | R Documentation |
"enetLTS"
object
A numeric vector which returns residuals from the enetLTS object. Residuals
correspond to deviances if family="multinomial"
and family="binomial"
.
## S3 method for class 'enetLTS' residuals(object,vers=c("reweighted","raw","both"),...)
object |
the model fit from which to extract residuals. |
vers |
a character string specifying for which estimator to extract
outlier weights. Possible values are |
... |
additional arguments from the enetLTS object. |
A numeric vector containing the requested residuals.
Fatma Sevinc KURNAZ, Irene HOFFMANN, Peter FILZMOSER
Maintainer: Fatma Sevinc KURNAZ <fatmasevinckurnaz@gmail.com>;<fskurnaz@yildiz.edu.tr>
enetLTS
,
fitted.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) residuals(fit1) residuals(fit1,vers="raw") residuals(fit1,vers="both") ## 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") residuals(fit2) residuals(fit2,vers="raw") residuals(fit2,vers="both") ## for multinomial n <- 120; p <- 15 NC <- 3 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 fit3 <- enetLTS(xout,yout,family="multinomial") residuals(fit3) residuals(fit3,vers="raw") residuals(fit3,vers="both")
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