similar to: [R] cut with infinite values -> NA (PR#1694)

I am doing work on changes in establishment sizes and came across behavior that is quite understandable and easily worked around but a little surprising. On R 1.5.1 on Debian unstable (see below for R.version output): > cut.off <- c(-Inf, 0, Inf) > x <- c(-Inf, -10, 0, 10, Inf) > is.numeric(x) [1] TRUE > is.double(x) [1] TRUE > # but > cut(x, cut.off, include.lowest=T) [1]

Dear list: since it took me a little while to figure out how to make use of naresid, I thought that the below R code might be useful as an example on the help page. Regards, Markus # generate some data x1 <- runif(20) y <- 10 + 5*x1 + rnorm(20) summary(lm.0 <- lm(y ~ x1)) # append some NA's to y y <- c(y, rep(NA, 5)) # generate some further x1s x1 <- c(x1, runif(5)) #

Dear list: since it took me a little while to figure out how to make use of naresid, I thought that the below R code might be useful as an example on the help page. Regards, Markus # generate some data x1 <- runif(20) y <- 10 + 5*x1 + rnorm(20) summary(lm.0 <- lm(y ~ x1)) # append some NA's to y y <- c(y, rep(NA, 5)) # generate some further x1s x1 <- c(x1, runif(5)) #

I ran into a to me surprising result on running lm with an orthogonal polynomial among the predictors. The lm command resulted in Error in qr(X) : NA/NaN/Inf in foreign function call (arg 1) Error during wrapup: despite my using a "subset" in the call to get rid of NA's. poly is apparently evaluated before any NA's are subsetted out of the data. Example code (attached to

I am having some trouble getting reftex, in particular the bibtex related features, to work properly in xemacs when editing text in Sweave files. I have added (defun Rnw-mode () (noweb-mode) (if (fboundp 'R-mode) (setq noweb-default-code-mode 'R-mode))) (add-to-list 'auto-mode-alist '("\\.Rnw\\'" . Rnw-mode)) (add-to-list 'auto-mode-alist

I am trying to use tapply to estimate the average wage in a set of Year*Education cells using sampling weights, as well as some other other statistics using the weights). I am having trouble trying to figure out how to pass the weights to the function, using: > tapply(wage,list(Educc,Year),weighted.mean, w=weight) whieh produces warnings of the type: 3: longer object length is not a

I am having trouble using Jim Lindsey's libraries because of unresolved symbols. I am currently using R 0.65.0, but had this problem in earlier releases as well. I have a RedHat 6.0 Linux on i386 and use egcs (upgraded to that distributed with rawhide, after first failures with the libraries): egcs-g77-1.1.2-19 egcs-1.1.2-19 Installation of the libraries works but on loading >

I have fitted a number of models with receipt of social assictance (toim1) during a year (values 0 or 1) with a number of covariates. The data include sampling weights which I use in the models. Using the exact same data, glm() under 1.0.1 and 1.1.0 give different results in many (but not all) of the models. I have re-installed 1.0.1 to check this and I found now mention in the NEWS file that

Dear All: I am having some trouble with date variables when NAs are present. The following example illustrates (see below for output of version): > library(chron) > minor.test <- c( "NA", "NA", "1988-02-08", + "1988-02-08", "NA", "1987-11-23", "1988-09-03", "1991-10-15", +

Hello -- I am plotting multiple densities in a single plot. If the max of a new density (added with 'lines()') is sufficiently larger than that of the one first plotted (using 'plot()'), the new density is cut or trucated off of the figure. Is there a simple way of adjusting the yscale after all lines have been added? Or is the procedure to be used to first estimate all

This smells like a bug to me. The error is triggered by the line: variables <- eval(predvars, data, env) inside model.frame.default(). At that point, na.action has not been applied, so poly() ended being called on data that still contains missing values. The qr() that issued the error is for generating the orthogonal basis when evaluating poly(), not for fitting the linear model itself.

This smells like a bug to me. The error is triggered by the line: variables <- eval(predvars, data, env) inside model.frame.default(). At that point, na.action has not been applied, so poly() ended being called on data that still contains missing values. The qr() that issued the error is for generating the orthogonal basis when evaluating poly(), not for fitting the linear model itself.

Vince Carey found this (thank you!). Since the fix to the problem is not entirely obvious, I post this to R-devel as RFC: help(dist) says: >> Missing values are allowed, and are excluded from all computations >> involving the rows within which they occur. If some columns are >> excluded in calculating a Euclidean, Manhattan or Canberra >> distance, the sum is

Dear All -- I am trying to use within a little table producing code an anova comparison of two gls fitted objects, contained in a list of such object, obtained using nlme function gls. The anova procedure fails to locate the second of the objects. The following code, borrowed from the help page of anova.gls, exemplifies: --------------- start example code --------------- library(nlme) ##

>>>>> peter dalgaard >>>>> on Sun, 8 Dec 2019 12:11:50 +0100 writes: > Yes, that looks like a bug and an easily fixable one too. agreed. > However, I spy another issue: Why do we check the > !R_FINITE(x) && mu == x before checking for sd < 0 ? The > difference is whether we > return ML_NAN; or ML_ERR_return_NAN;

Yes, that looks like a bug and an easily fixable one too. However, I spy another issue: Why do we check the !R_FINITE(x) && mu == x before checking for sd < 0 ? The difference is whether we return ML_NAN; or ML_ERR_return_NAN; but surely negative sd should always be an error? I'd be inclined to do if (sigma < 0) ML_ERR_return_NAN; if(!R_FINITE(sigma)) return R_D__0;

> > On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > > > In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: > > > > F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); > > if (*info == 0){ > >

Thanks to all who answered my third question. I learned something, but: On 2017-02-09 17:44, Martin Maechler wrote: > >>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>> >>> In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this:

Dear all, I try to fit a glmmPQL on a huge data with 384189 individuals id=1:384189: working in 1520 establishments est:1:1516. The minimum number of individuals in every establishment is 30. This works for a subsample excluding establishemnet cells smaller than 100, but fail when we include smaller cells: R> summary(glmmPQL(count ~ + I( age-ave(age,est) )* ave(age,est) + + I(

> On 10 Feb 2017, at 14:53, G?ran Brostr?m <goran.brostrom at umu.se> wrote: > > Thanks to all who answered my third question. I learned something, but: > > On 2017-02-09 17:44, Martin Maechler wrote: >> >>>> On 9 Feb 2017, at 16:00, G?ran Brostr?m <goran.brostrom at umu.se> wrote: >>>> >>>> In my package 'glmmML'