first commit

DESCRIPTION

@@ -0,0 +1,10 @@
+Package: fls
+Type: Package
+Title: Flexible least squares and related weighted Kalman filter
+Version: 0.1
+Date: 2009-11-02
+Author: Bryan W. Lewis
+Maintainer: Bryan W. Lewis <bwaynelewis@gmail.com>
+Description: Implementation of the Kalaba-Tesfatsion flexible least squares algorithm and a related weighted Kalman filter of Montana, et. al. See the documentation for additional references.
+License: GPL
+LazyLoad: yes

R/fls.R

@@ -0,0 +1,42 @@
+`fls` <-
+function (A, b, mu=1, ncap=length(b), smoothed=TRUE)
+{
+  m <- nrow (A)
+  n <- ncol (A)
+  M <- array (0,c(n,n,ncap))
+  E <- array (0,c(n,ncap))
+  X <- array (0,c(n,ncap))
+  R <- matrix(0,n,n)
+  diag(R) <- diag(R) + mu
+  for (j in 1:ncap) {
+    Z <- solve(qr(R + tcrossprod(A[j,]),LAPACK=TRUE),diag(1.0,n));
+    M[,,j] <- mu*Z             # (5.7b)
+    v <- b[j]*A[j,]
+    if(j==1) p <- rep(0,n)
+    else p <- mu*E[,j-1]
+    w <- p + v
+    E[,j] <- Z %*% w           # (5.7c)
+    R <- -mu*mu*Z
+    diag(R) <- diag(R) + 2*mu
+  }
+# Calculate eqn (5.15) FLS estimate at ncap
+  Q <- -mu*M[,,ncap-1]
+  diag(Q) <- diag(Q) + mu
+  Ancap <- A[ncap,,drop=FALSE]
+  C <- Q + t(Ancap) %*% Ancap
+  d <- mu*E[,ncap-1,drop=FALSE] + b[ncap]*t(Ancap)
+  X[,ncap] <- C %*% d
+  X[,ncap] <- solve(qr(C,LAPACK=TRUE),d)
+  if (smoothed) {
+# Use eqn (5.16) to obtain smoothed FLS estimates for 
+# X[,1], X[,2], ..., X[,ncap-1]
+    for (j in 1:(ncap-1)) {
+      l <- ncap - j
+      X[,l] <- E[,l] + M[,,l] %*% X[,l+1]
+    }
+  }
+  else {
+    X <- X[,ncap]
+  }
+  X
+}

R/flskf.R

@@ -0,0 +1,26 @@
+`flskf` <-
+function (A, b, mu=1, ncap=length(b))
+{
+  m <- nrow (A)
+  n <- ncol (A)
+  X <- array (0,c(n,ncap))
+  R <- array (0,c(n,n))
+  diag (R) <- 1/mu
+  a <- t(A[1,,drop=FALSE])
+  phi <- c((1/mu) * crossprod(a) + 1)
+  w <- (1/phi) * R %*% a
+  rho <- b[1]
+  X[,1] <- rho * w
+  for (j in 2:ncap) {
+    R = R - phi * tcrossprod(w)
+    diag (R) <- diag (R) + 1/mu
+    a <- t(A[j,,drop=FALSE])
+    phi <- c(t(a) %*% R %*% a)
+    phi <- phi + 1
+    rho <- c(b[j] - crossprod (a, X[,j-1]))
+    w <- (1/phi) * R %*% a
+    X[,j] <- X[,j-1] + rho*w
+  }
+  X
+}
+

README

@@ -0,0 +1,5 @@
+The Kalaba-Tesfatsion Flexible Least Squares method. 
+Numbered equations refer to the paper:
+R. Kalaba and L. Tesfatsion, Time-Varying Linear Regression Via 
+Flexible Least Squares, Computers and Mathematics with Applications, 
+Vol. 17 (1989), pp. 1215-1245.

man/fls-package.Rd

@@ -0,0 +1,31 @@
+\name{fls-package}
+\alias{fls-package}
+\alias{fls}
+\docType{package}
+\title{
+What the package does (short line)
+}
+\description{
+More about what it does (maybe more than one line)
+}
+\details{
+}
+\author{
+Who wrote it
+
+Maintainer: Who to complain to <yourfault@somewhere.net>
+~~ The author and/or maintainer of the package ~~
+}
+\references{
+~~ Literature or other references for background information ~~
+}
+~~ Optionally other standard keywords, one per line, from file KEYWORDS in ~~
+~~ the R documentation directory ~~
+\keyword{ package }
+\seealso{
+~~ Optional links to other man pages, e.g. ~~
+~~ \code{\link[<pkg>:<pkg>-package]{<pkg>}} ~~
+}
+\examples{
+~~ simple examples of the most important functions ~~
+}

man/fls.Rd

@@ -0,0 +1,101 @@
+\name{fls}
+\Rdversion{1.1}
+\alias{fls}
+\title{Flexible Least Squares}
+\description{
+The Kalaba-Tesfatsion Flexible Least Squares method. 
+Numbered equations refer to the paper:
+R. Kalaba and L. Tesfatsion, Time-Varying Linear Regression Via 
+Flexible Least Squares, Computers and Mathematics with Applications, 
+Vol. 17 (1989), pp. 1215-1245.
+}
+\usage{
+fls(A, b, mu = 1, ncap = length(b))
+}
+%- maybe also 'usage' for other objects documented here.
+\arguments{
+  \item{A}{
+%%     ~~Describe \code{A} here~~
+}
+  \item{b}{
+%%     ~~Describe \code{b} here~~
+}
+  \item{mu}{
+%%     ~~Describe \code{mu} here~~
+}
+  \item{ncap}{
+%%     ~~Describe \code{ncap} here~~
+}
+}
+\details{
+%%  ~~ If necessary, more details than the description above ~~
+}
+\value{
+%%  ~Describe the value returned
+%%  If it is a LIST, use
+%%  \item{comp1 }{Description of 'comp1'}
+%%  \item{comp2 }{Description of 'comp2'}
+%% ...
+}
+\references{
+%% ~put references to the literature/web site here ~
+}
+\author{
+%%  ~~who you are~~
+}
+\note{
+%%  ~~further notes~~
+}
+
+%% ~Make other sections like Warning with \section{Warning }{....} ~
+
+\seealso{
+%% ~~objects to See Also as \code{\link{help}}, ~~~
+}
+\examples{
+##---- Should be DIRECTLY executable !! ----
+##-- ==>  Define data, use random,
+##--	or do  help(data=index)  for the standard data sets.
+
+## The function is currently defined as
+function (A, b, mu=1, ncap=length(b))
+{
+  m <- nrow (A)
+  n <- ncol (A)
+  M <- array (0,c(n,n,ncap))
+  E <- array (0,c(n,ncap))
+  X <- array (0,c(n,ncap))
+  R <- matrix(0,n,n)
+  diag(R) <- diag(R) + mu
+  for (j in 1:ncap) {
+    Z <- solve(qr(R + tcrossprod(A[j,]),LAPACK=TRUE),diag(1.0,n));
+    M[,,j] <- mu*Z             # (5.7b)
+    v <- b[j]*A[j,]
+    if(j==1) p <- rep(0,n)
+    else p <- mu*E[,j-1]
+    w <- p + v
+    E[,j] <- Z \%*\% w           # (5.7c)
+    R <- -mu*mu*Z
+    diag(R) <- diag(R) + 2*mu
+  }
+# Calculate eqn (5.15) FLS estimate at ncap
+  Q <- -mu*M[,,ncap-1]
+  diag(Q) <- diag(Q) + mu
+  Ancap <- A[ncap,,drop=FALSE]
+  C <- Q + t(Ancap) \%*\% Ancap
+  d <- mu*E[,ncap-1,drop=FALSE] + b[ncap]*t(Ancap)
+  X[,ncap] <- C \%*\% d
+  X[,ncap] <- solve(qr(C,LAPACK=TRUE),d)
+# Use eqn (5.16) to obtain smoothed FLS estimates for 
+# X[,1], X[,2], ..., X[,ncap-1]
+  for (j in 1:(ncap-1)) {
+    l <- ncap - j
+    X[,l] <- E[,l] + M[,,l] \%*\% X[,l+1]
+  }
+  X
+  }
+}
+% Add one or more standard keywords, see file 'KEYWORDS' in the
+% R documentation directory.
+\keyword{ ~kwd1 }
+\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line

man/flskf.Rd

@@ -0,0 +1,88 @@
+\name{flskf}
+\Rdversion{1.1}
+\alias{flskf}
+%- Also NEED an '\alias' for EACH other topic documented here.
+\title{
+%%  ~~function to do ... ~~
+}
+\description{
+%%  ~~ A concise (1-5 lines) description of what the function does. ~~
+}
+\usage{
+flskf(A, b, mu = 1, ncap = length(b))
+}
+%- maybe also 'usage' for other objects documented here.
+\arguments{
+  \item{A}{
+%%     ~~Describe \code{A} here~~
+}
+  \item{b}{
+%%     ~~Describe \code{b} here~~
+}
+  \item{mu}{
+%%     ~~Describe \code{mu} here~~
+}
+  \item{ncap}{
+%%     ~~Describe \code{ncap} here~~
+}
+}
+\details{
+%%  ~~ If necessary, more details than the description above ~~
+}
+\value{
+%%  ~Describe the value returned
+%%  If it is a LIST, use
+%%  \item{comp1 }{Description of 'comp1'}
+%%  \item{comp2 }{Description of 'comp2'}
+%% ...
+}
+\references{
+%% ~put references to the literature/web site here ~
+}
+\author{
+%%  ~~who you are~~
+}
+\note{
+%%  ~~further notes~~
+}
+
+%% ~Make other sections like Warning with \section{Warning }{....} ~
+
+\seealso{
+%% ~~objects to See Also as \code{\link{help}}, ~~~
+}
+\examples{
+##---- Should be DIRECTLY executable !! ----
+##-- ==>  Define data, use random,
+##--	or do  help(data=index)  for the standard data sets.
+
+## The function is currently defined as
+function (A, b, mu=1, ncap=length(b))
+{
+  m <- nrow (A)
+  n <- ncol (A)
+  X <- array (0,c(n,ncap))
+  R <- array (0,c(n,n))
+  diag (R) <- 1/mu
+  a <- t(A[1,,drop=FALSE])
+  phi <- c((1/mu) * crossprod(a) + 1)
+  w <- (1/phi) * R \%*\% a
+  rho <- b[1]
+  X[,1] <- rho * w
+  for (j in 2:ncap) {
+    R = R - phi * tcrossprod(w)
+    diag (R) <- diag (R) + 1/mu
+    a <- t(A[j,,drop=FALSE])
+    phi <- c(t(a) \%*\% R \%*\% a)
+    phi <- phi + 1
+    rho <- c(b[j] - crossprod (a, X[,j-1]))
+    w <- (1/phi) * R \%*\% a
+    X[,j] <- X[,j-1] + rho*w
+  }
+  X
+  }
+}
+% Add one or more standard keywords, see file 'KEYWORDS' in the
+% R documentation directory.
+\keyword{ ~kwd1 }
+\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line