fitted.enetLTS: the fitted values from the '"enetLTS"' object.

In enetLTS: Robust and Sparse Methods for High Dimensional Linear and Binary and Multinomial Regression

the fitted values from the "enetLTS" object.

Description

A numeric vector which extract fitted values from the current model.

Usage

  ## S3 method for class 'enetLTS'
fitted(object,vers=c("reweighted","raw","both"),type=c("response","class"),...)

Arguments

object	the model fit from which to extract fitted values.
vers	a character string specifying for which fit to make predictions. Possible values are "reweighted" (the default) for predicting values from the reweighted fit, "raw" for predicting values from the raw fit, or "both" for predicting values from both fits.
type	type of prediction required. type="response" gives the fitted probabilities for "multinomial" and "binomial" and gives the fitted values for "gaussian". type="class" is available only for "multinomial" and "binomial" model, and produces the class label corresponding to the maximum probability.
...	additional arguments from the enetLTS object if needed.

Value

A numeric vector containing the requested fitted values.

Author(s)

Fatma Sevinc KURNAZ, Irene HOFFMANN, Peter FILZMOSER
Maintainer: Fatma Sevinc KURNAZ <[e-mail]>;<[e-mail]>

See Also

enetLTS, predict.enetLTS, residuals.enetLTS

Examples

## 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,crit.plot=FALSE)
fitted(fit1)
fitted(fit1,vers="raw")
fitted(fit1,vers="both")
fitted(fit1,vers="reweighted",type="response")


## 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")
fitted(fit2)
fitted(fit2,vers="raw")
fitted(fit2,vers="both",type="class")
fitted(fit2,vers="both")
fitted(fit2,vers="reweighted",type="class")




## 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")
fitted(fit3)
fitted(fit3,vers="raw")
fitted(fit3,vers="both",type="class")
fitted(fit3,vers="both")
fitted(fit3,vers="reweighted",type="class")

enetLTS documentation built on May 22, 2022, 1:05 a.m.