% File src/library/base/man/kappa.Rd % Part of the R package, http://www.R-project.org % Copyright 1995-2012 R Core Team % Copyright 2008-2010 The R Foundation % Distributed under GPL 2 or later \name{kappa} \alias{rcond} \alias{kappa} \alias{kappa.default} \alias{kappa.lm} \alias{kappa.qr} \alias{.kappa_tri} \title{Compute or Estimate the Condition Number of a Matrix} \usage{ kappa(z, \dots) \method{kappa}{default}(z, exact = FALSE, norm = NULL, method = c("qr", "direct"), \dots) \method{kappa}{lm}(z, \dots) \method{kappa}{qr}(z, \dots) .kappa_tri(z, exact = FALSE, LINPACK = TRUE, norm = NULL, \dots) rcond(x, norm = c("O","I","1"), triangular = FALSE, \dots) } \arguments{ \item{z, x}{A matrix or a the result of \code{\link{qr}} or a fit from a class inheriting from \code{"lm"}.} \item{exact}{logical. Should the result be exact?} \item{norm}{character string, specifying the matrix norm with respect to which the condition number is to be computed, see also \code{\link{norm}}. For \code{rcond}, the default is \code{"O"}, meaning the \bold{O}ne- or 1-norm. The (currently only) other possible value is \code{"I"} for the infinity norm.} \item{method}{character string, specifying the method to be used; \code{"qr"} is default for back-compatibility, mainly.} \item{triangular}{logical. If true, the matrix used is just the lower triangular part of \code{z}.} \item{LINPACK}{logical. If true and \code{z} is not complex, the Linpack routine \code{dtrco()} is called; otherwise the relevant Lapack routine is.} \item{\dots}{further arguments passed to or from other methods; for \code{kappa.*()}, notably \code{LINPACK} when \code{norm} is not \code{"2"}.} } \description{ The condition number of a regular (square) matrix is the product of the \emph{norm} of the matrix and the norm of its inverse (or pseudo-inverse), and hence depends on the kind of matrix-norm. \code{kappa()} computes by default (an estimate of) the 2-norm condition number of a matrix or of the \eqn{R} matrix of a \eqn{QR} decomposition, perhaps of a linear fit. The 2-norm condition number can be shown to be the ratio of the largest to the smallest \emph{non-zero} singular value of the matrix. \code{rcond()} computes an approximation of the \bold{r}eciprocal \bold{cond}ition number, see the details. } \details{ For \code{kappa()}, if \code{exact = FALSE} (the default) the 2-norm condition number is estimated by a cheap approximation. However, the exact calculation (via \code{\link{svd}}) is also likely to be quick enough. Note that the 1- and Inf-norm condition numbers are much faster to calculate, and \code{rcond()} computes these \emph{\bold{r}eciprocal} condition numbers, also for complex matrices, using standard Lapack routines. \code{kappa} and \code{rcond} are different interfaces to \emph{partly} identical functionality. \code{.kappa_tri} is an internal function called by \code{kappa.qr} and \code{kappa.default}. } \value{ The condition number, \eqn{kappa}, or an approximation if \code{exact = FALSE}. } \source{ The LAPACK routines \code{DTRCON} and \code{ZTRCON} and the LINPACK routine \code{DTRCO}. LAPACK and LINPACK are from \url{http://www.netlib.org/lapack} and \url{http://www.netlib.org/linpack} and their guides are listed in the references. } \references{ Anderson. E. and ten others (1999) \emph{LAPACK Users' Guide}. Third Edition. SIAM.\cr Available on-line at \url{http://www.netlib.org/lapack/lug/lapack_lug.html}. Chambers, J. M. (1992) \emph{Linear models.} Chapter 4 of \emph{Statistical Models in S} eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole. Dongarra, J. J., Bunch, J. R., Moler, C. B. and Stewart, G. W. (1978) \emph{LINPACK Users Guide.} Philadelphia: SIAM Publications. } \author{ The design was inspired by (but differs considerably from) the S function of the same name described in Chambers (1992). } \seealso{ \code{\link{norm}}; \code{\link{svd}} for the singular value decomposition and \code{\link{qr}} for the \eqn{QR} one. } \examples{ kappa(x1 <- cbind(1, 1:10)) # 15.71 kappa(x1, exact = TRUE) # 13.68 kappa(x2 <- cbind(x1, 2:11)) # high! [x2 is singular!] hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") } sv9 <- svd(h9 <- hilbert(9))$ d kappa(h9) # pretty high! kappa(h9, exact = TRUE) == max(sv9) / min(sv9) kappa(h9, exact = TRUE) / kappa(h9) # 0.677 (i.e., rel.error = 32\%) } \keyword{math}