search for: wfit

...2.7.1 OS: Ubuntu 8.04 / Windows XP Submission from: (NULL) (76.119.235.225) When running lm(modeleq, weights=wt, data=dataset) with small weights (<1e-10), I have encountered an odd phenomenon with fitted values. Due to numerical precision issues, the fitted values and residuals returned by lm.wfit (from its .Fortran call to dqrls) can differ greatly from those returned by running predict on the resulting lm object. This is completely attributable to the numerical precision passed to the given function, but I wonder if a warning message for weights below as certain threshold may be in order.

...gam call (changed to a NULL object) * Comparing results from environments 4 and 5 shows that reverting to an older version of the gam package in R 3.3.2 still produced altered AIC values and the NULL output from step.gam call Further investigations into these differences seem to show that the lm.wfit call in the gam.fit function (called from within gam and step.gam) may result in different values in R 3.3.0 and 3.3.2. So my questions are 2-fold: 1- Would you have any information about why lm.wfit would produce different outcomes in R 3.3.0 and 3.3.2? The source code does not appear significa...

R 0.63.3 (any platform) library(lqs) example(lqs) gives Warning in (y - z$residuals) + offset : longer object length is not a multiple of shorter object length repeatedly. The problem is in lm.wfit, which removes cases with zero weights, but does not adjust the offset. The fix is z$fitted.values <- (y - z$residuals) z$weights <- w if (zero.weights) { coef[is.na(coef)] <- 0 f0 <- x0 %*% coef if (ny > 1) { save.r[ok, ] <- z$residuals save.r[nok, ] &l...

Dear Group, I am having trouble with using rlm on multivariate data sets. When I call rlm I get Error in lm.wfit(x, y, w, method = "qr") : incompatible dimensions lm on the same data sets seem to work well (see code example). Am I doing something wrong? I have already browsed through the forums and google but could not find any related discussions. I use Windows XP and R Version 2.0.1 (...

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y * wts, ny = ny, tol = as.double(tol), coefficients = mat.or.vec(p, ny), residuals = y, effects = mat.or.vec(n, ny), rank = integer(1), pivot = 1:p, qra...

Hi: Could anyone help me to clarify this: are the weights normalized inside lm function (package:stats) before applied to the error term? For example: >lm (cost ~ material, weights=quatity, data=receipt) will lm normalize quatity such that sum(quatity) = 1? I traced to lm.wfit and then the weights get transferred into a precompiled FORTRAN module so I can't figure out. Thanks! > version _ platform sparc-sun-solaris2.9 arch sparc os solaris2.9 system sparc, solaris2.9 status...

...> test(7) [1] 49 > test() Error in test() : recursive default argument reference here is a code fragment from lm() using the same syntax: else { x <- model.matrix(mt, mf, contrasts) z <- if (is.null(w)) lm.fit(x, y, offset = offset, ...) else lm.wfit(x, y, w, offset = offset, ...) What is wrong? Kjetil Halvorsen -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the...

..."model.frame") return(m) Terms <- attr(m, "terms") weights <- model.extract(m, weights) Y <- model.extract(m, response) X <- as.matrix.csr(model.matrix(Terms, m, contrasts)) fit <- { if (length(weights)) slm.wfit(X, Y, weights, method, ...) else slm.fit(X, Y, method, ...) } fit$terms <- Terms fit$call <- call attr(fit, "na.message") <- attr(m, "na.message") class(fit) <- c(if (is.matrix(Y)) "mslm", "slm") fit } ur...

...there in 1.8.0 already). Sorted by "self" part of the output, the top 10 lines are: R 1.7.1 % self % total self seconds total seconds name 10.75 100.36 10.75 100.36 ".Fortran" 8.71 81.32 34.27 319.88 "lm.wfit" 5.43 50.66 81.19 757.88 "FLXfit" 4.30 40.16 4.30 40.16 "^" 4.26 39.80 4.26 39.80 "==" 4.15 38.74 4.99 46.62 "names" 3.90 36.38 20.57 191.98 "initialize"...

Dear all, I'm trying to fit a curve to some 1 year failure-time data, so that I can extrapolate and predict failure rates up to 3 years. The data is in the general form: Treatment Time Status Treatment A 28 0 Treatment B 28 0 Treatment B 28 0 Treatment A 28

Dear R developers, Taking advantage of the yesterday discussion about the use of Depends/Import/Suggests/Enhances, I would like to add a related question. Let's assume, in the DESCRIPTION file of my package foo0, I have: Depends: foo1 Imports: foo2 while in the NAMESPACE file of my package I have importFrom("foo2", f2) and within my package I use the following two external

I'm trying to use stepAIC on sparse matrices, and I need some help. The documentation for slm.fit suggests: slm.fit and slm.wfit call slm.fit.csr to do Cholesky decomposition and then backsolve to obtain the least squares estimated coefficients. These functions can be called directly if the user is willing to specify the design matrix in matrix.csr form. This is often advantageous in large problems to reduce memory requir...

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y * wts, ny = ny, tol = as.double(tol), coefficients = mat.or.vec(p, ny), residuals = y, effects = mat.or.vec(n, ny), rank = integer(1), pivot = 1:p, qra...

Hi, In using the betareg package, I encounter the following error message: Error in lm.wfit(x, linkfun(y), weights, offset = offset) : NA/NaN/Inf in foreign function call (arg 4) Any help will be most appreciated. Thanks in advance. Alex

...ction(n) { int <- -5 x <- rnorm(n, 50, 7) e <- rnorm(n, 0, 1) y <- int + (1.2 * x) + e return(data.frame(y, x)) } gc.time() data <- createData(500000) data.y <- as.matrix(data[1]) data.x <- model.matrix(y ~ ., data) for (i in 1:100) speedglm.wfit(X=data.x, y=data.y, family=gaussian()) gc.time() nate at haswell:~$ time Rscript gc.R Loading required package: Matrix Loading required package: methods [1] 0 0 0 0 0 [1] 10.410 0.024 10.441 0.000 0.000 real 0m17.167s user 0m16.996s sys 0m0.176s The total execution time is 17 seconds, and...

...!is.numeric(w)) stop("'weights' must be a numeric vector") x <- model.matrix(mt, mf, contrasts) # fixup to remove intercept??? z <- if (is.null(w)) cancor.default(x, y, ...) else stop("weights are not yet implemented") # lm.wfit(x, y, w, ...) z$call <- cl z$terms <- mt z } TESTME <- FALSE if (TESTME) { # need to get latest version, 0.6-1 from R-Forge install.packages("candisc", repo="http://R-Forge.R-project.org") library(candisc) data(Rohwer) # this bombs: needs interc...

I want to estimate the center of a distribution with lots of outliers in one tail, and thought I would use a function such as S-plus's location.m() with psi.fun=bisquare (as per MASS 3 p. 131). However, R seems not have such a function, so my questions are: 1) Is there an R equivalent to location.m()? 2) Would huber() give me results that are similar (i.e., close enough)? Thanks.

...)) { z$fitted.values <- offset z$residuals <- y - offset } } else { x <- model.matrix(mt, mf, contrasts) z <- if (is.null(w)) lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok, ...) } class(z) <- c(if (mlm) "mlm", "lm") z$na.action <- attr(mf, "na.action") z$offset <- offset z$contrasts <- attr(x, "contrasts") z$xlevels <- .getXle...

...z$residuals <- y - offset > } > } > else { > x <- model.matrix(mt, mf, contrasts) > z <- if (is.null(w)) > lm.fit(x, y, offset = offset, singular.ok = singular.ok, > ...) > else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok, > ...) > } > class(z) <- c(if (mlm) "mlm", "lm") > z$na.action <- attr(mf, "na.action") > z$offset <- offset > z$contrasts <- attr(x, "contrast...

Peter Dalgaard BSA <p.dalgaard at biostat.ku.dk> wrote: > Martin Maechler <maechler at stat.math.ethz.ch> writes: [ snip... ] > > >From this logic, coercing to a matrix should coerce to character rather > > than integer/numeric codes. > > Hm. I'm not sure there really is much logic in this... Factors are > sometimes character-like, sometimes