similar to: get_all_vars fails with matrices (PR#13624)

mgcv 1.4 is now on CRAN. It includes new features to allow mgcv::gam to fit almost any (quadratically) penalized GLM, plus some extra smoother classes. New gam features ------------------------- * Linear functionals of smooths can be included in the gam linear predictor, allowing, e.g., functional generalized linear models/signal regression, smooths of interval data, etc. * The parametric

mgcv 1.4 is now on CRAN. It includes new features to allow mgcv::gam to fit almost any (quadratically) penalized GLM, plus some extra smoother classes. New gam features ------------------------- * Linear functionals of smooths can be included in the gam linear predictor, allowing, e.g., functional generalized linear models/signal regression, smooths of interval data, etc. * The parametric

re: Cubic splines in package "mgcv" I don't have access to Gu (2002) but clearly the function R(x,z) defined on p126 of Simon Wood's book is piecewise quartic, not piecewise cubic. Like Kunio Takezawa (below) I was puzzled by the word "cubic" on p126. As Simon Wood writes, this basis is not actually used by mgcv when specifying bs="cr". Maybe the point is

mgcv 1.5-0 is now on CRAN. Main changes are: * REML and ML smoothness selection are now available. * A Tweedie family has been added. * `gam.method' has been replaced (see arguments `method' and `optimizer' for `gam') For other changes see the changeLog. Simon -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603

mgcv 1.5-0 is now on CRAN. Main changes are: * REML and ML smoothness selection are now available. * A Tweedie family has been added. * `gam.method' has been replaced (see arguments `method' and `optimizer' for `gam') For other changes see the changeLog. Simon -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603

The plots look reasonable to me. The plot of residuals against linear predictor always looks scary when many of the fitted values are very close to zero, so I tend to look at residuals against sqrt(fitted) in such cases. I don't think that the presence of the zero curve is a reason to reject the model --- it's easy to produce such plots by fitting a completely correct model to simulated

Hi everyone, This is about documentation for the model.frame() page. The get_all_vars() function (added in R 2.5.0) is a great addition, but the behavior of its '...' argument is different from that of model.frame() with which it is documented and this creates ambiguity. The current docs read: \item{\dots}{further arguments such as \code{data}, \code{na.action}, \code{subset}. Any

x <- NA na.act <- na.action(na.exclude(x)) y <- rep(0,0) naresid(na.act,y) ... currently produces the result... numeric(0) ... whereas the documentation might lead you to expect NA The behaviour is caused by the line if (length(x) == 0L) return(x) in `stats:::naresid.exclude'. Removing this line results in the behaviour I'd expected in the above example (and in a

Hi Greg, For quasi families I've used extended quasi-likelihood (see Mccullagh and Nelder, Generalized Linear Models 2nd ed, section 9.6) in place of the likelihood/quasi-likelihood in the expression for the (RE)ML score. I hadn't realised that this was possible before the paper was published. best, Simon ps. sorry for slow reply, the original message slipped through my filter for

Hello again, It appears that get_all_vars() incorrectly handles model formulae that use a right-hand side (rhs) matrix. For example, consider these two substantively identical models: # model using named variables mpg <- mtcars$mpg wt <- mtcars$wt hp <- mtcars$hp m1 <- lm(mpg ~ wt + hp) # model using matrix y <- mtcars$mpg x <- cbind(mtcars$wt, mtcars$hp) m2 <- lm(y ~ x)

> get_all_vars(dist ~ speed, data = cars) Error in `row.names<-.data.frame`(`*tmp*`, value = c(NA, -50L)) : invalid 'row.names' length > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status Under development (unstable) major 2 minor 5.0 year 2007 month

Hi, Currently the `gaussian' family's initialization code signals an error if any response data are zero or negative and a log link is used. Given that zero or negative response data are perfectly legitimate under the GLM fitted using `gaussian("log")', this seems a bit unsatisfactory. Might it be worth changing it? The current offending code from `gaussian' is:

>>>>> Martin Maechler >>>>> on Mon, 18 Nov 2019 12:15:38 +0100 writes: >>>>> suharto anggono--- via R-devel >>>>> on Sun, 17 Nov 2019 10:34:31 +0000 writes: >> SVN revision 77401 changes >> x[isM] <- lapply(x[isM], function(o) `class<-`(o, class(o)[class(o) != "AsIs"])) >> to

SVN?revision?77401?changes ????????x[isM]?<-?lapply(x[isM],?function(o)?`class<-`(o,?class(o)[class(o)?!=?"AsIs"])) to ????????x[isM]?<-?lapply(x[isM],?function(o)?`class<-`(o,?class(o)[!inherits(o,"AsIs")])) in?function?'get_all_vars'?in?src/library/stats/R/models.R?in?R?devel. The?change?is?inappropriate.

class(o)[!inherits(o,"AsIs")] is still in function 'get_all_vars' in R patched (in https://svn.r-project.org/R/branches/R-3-6-branch/src/library/stats/R/models.R). It was ported to R patched by r77402. On Monday, 18 November 2019, 8:12:10 PM GMT+7, Martin Maechler <maechler at stat.math.ethz.ch> wrote: >>>>> Martin Maechler >>>>>? ?

I've got a data frame which I've split by a factor, creating a list of dataframes which I have then done various operations on individually. I next want to recombine the resulting dataframes (still held in a list, still with the same number of columns with the same names) and there does not appear to be a `good' way to do this - at the moment, I'm using a for-loop with the rbind

Hello All, The link to the binary & installation instructions (tar.gz binary not an ipk I'm afraid) is as follows: http://students.bath.ac.uk/enpsgp/Zaurus/#R It eventually dawned on me that the WORDS_BIGENDIAN define (or lack thereof) was causing the problems (after testing ieee NaN compliance that is). When cross-compiling it's probably fair enough that the configure script

The help pages of R-1.2.2 include several pages on various time series functions, but when I try to use these functions they appear not to be available .... am I missing something obvious, or are these functions not yet built? Chris Rogers ----------------------------------------------------------------------- L C G Rogers, Professor of Probability tel:+44 1225 826224 Department of

Hello All, I read in the archives that someone managed to compile R but that there were problems with the fp performance, specifically the handling of NaNs. Could someone please explain the problem to me and how to test whether it is occurring. I have just compiled R for the Zaurus 5500, and want to see whether it works or requires tweaking. Regards, Simon

Dear users, I am trying to show the equation (including coefficients from the model estimates) for a gam model but do not understand how to. Slide 7 from one of the authors presentations (gam-theory.pdf URL: http://people.bath.ac.uk/sw283/mgcv/) shows a general equation log{E(yi )} = ?+ ?xi + f (zi ) . What I would like to do is put my model coefficients and present the equation used. I am an