Nothing
covMest <- function(x, cor=FALSE, r = 0.45, arp = 0.05, eps=1e-3, maxiter=120, control, t0, S0)
{
.Deprecated(new="CovMest")
## Analize and validate the input parameters ...
## if a control object was supplied, take the option parameters from it,
## but if single parameters were passed (not defaults) they will override the
## control object.
if(!missing(control)){
defcontrol <- rrcov.control() # default control
# if(r == defcontrol$r) r <- control$r
# if(arp == defcontrol$arp) arp <- control$arp
# if(eps == defcontrol$eps) eps <- control$eps
# if(maxiter == defcontrol$maxiter) maxiter <- control$maxiter
}
if(is.data.frame(x))
x <- data.matrix(x)
else if (!is.matrix(x))
x <- matrix(x, length(x), 1,
dimnames = list(names(x), deparse(substitute(x))))
## drop all rows with missing values (!!) :
na.x <- !is.finite(x %*% rep(1, ncol(x)))
ok <- !na.x
x <- x[ok, , drop = FALSE]
dx <- dim(x)
if(!length(dx))
stop("All observations have missing values!")
dimn <- dimnames(x)
n <- dx[1]
p <- dx[2]
if(n < 2 * p)
stop("Need at least 2*(number of variables) observations ")
ans <- list(method = "M-Estimates", call = match.call())
## If not provided initial estimates, compute them as MVE
## Take the raw estimates and standardise the covariance
## matrix to determinant=1
if(missing(t0) || missing(S0)){
mcd <- CovMve(x)
t0 <- mcd@raw.center
S0 <- mcd@raw.cov
detS0 <-det(S0)
detS02 <- detS0^(1.0/p)
S0 <- S0/detS02
}
## calculate the constants M and c
## for the translated biweight function
psix <- new("PsiBwt", n=n, p=p, r=r, alpha=arp)
psix <- csolve(psix)
mest <- iterM(psix, x, t0, S0, eps=1e-3, maxiter=maxiter)
## this was the version without OO
##const <- csolve.bt(n, p, r, arp)
##mest <- .iterM(x, t0, S0, const$c1, const$M, eps, maxiter)
ans$n.obs <- n
##ans$c1 <- const$c1
##ans$M <- const$M
ans$c1 <- psix@c1
ans$M <- psix@M
ans$iter <- mest$iter
ans$cov <- mest$s
ans$center <- mest$t1
ans$mah <- mahalanobis(x, mest$t1, mest$s)
ans$crit <- determinant(mest$s, logarithm = TRUE)$modulus[1]
if(cor && !is.null(ans$cov))
cor <- cov2cor(ans$cov)
class(ans) <- c("mest", "mcd")
attr(ans, "call") <- sys.call()
ans$method <- paste("M-Estimator.")
ans$X <- x
return(ans)
}
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