similar to: [Bug 2374] New: Discrepancy in --exclude-from option between Gnu tar and rsync

https://bugzilla.samba.org/show_bug.cgi?id=2374 wayned@samba.org changed: What |Removed |Added ---------------------------------------------------------------------------- Status|NEW |RESOLVED Resolution| |WORKSFORME ------- Additional Comments From wayned@samba.org 2005-02-22 09:45

In the case of inherits (at least) this seems intended. The help page says: "If the object does not have a class attribute, it has an implicit class..." which I take to mean that if an object does have a class attribute it does not also have an implicit class. The behavior you noted below will apply to other types bearing implicit classes. For example: > inherits(1.0,

Hello, I am looking for an explanation and/or fix for a discrepancy in the behaviour of the R lm.influence() function [ version R 1.5.0 (2002-04-29) ] and the same function in Splus [ Splus version 5.1 release 1, running on SGI IRIX 6.2]. The discrepancy is of concern because I am migrating some Splus scripts to R and need to ensure consistency of results. Specifically, when I fit a glm()

https://bugzilla.samba.org/show_bug.cgi?id=2375 wayned@samba.org changed: What |Removed |Added ---------------------------------------------------------------------------- Status|NEW |ASSIGNED Summary|Support (a la Gnu tar) for |Support (a la Gnu tar) for - |'-C' (--directory DIR)

In the course of exploring response prediction, I stumbled upon a small discrepancy between the CIs produced by predict.lm() and all.effects() require(mlmRev) require(effects) hsb.lm <- lm(mAch ~ minrty * sector, Hsb82) hsb.new <- data.frame( minrty = rep(c('No', 'Yes'), 2), sector = rep(c('Public', 'Catholic'), each = 2)) hsb.eff <-

From: Brett Walker <brett.walker at geometry.com.au> Remove a discrepancy between code comments and the definition for arena_header, ARENA_PADDING and free_arena_header. When the size of int and pointer are both 32-bits; and compiled with DEBUG_MALLOC undefined; the following are true: sizeof(arena_header) == 16 and sizeof(free_arena_header) == 56. To correct this, the type of _pad needs

Dear Trevor, I've noticed a discrepancy in the degrees of freedom reported by gam() from the gam package in R vs. gam() in S-PLUS. The nonparametric df differ by 1; otherwise (except for things that depend upon the df), the output is the same: --------- snip ------------ *** From R (gam version 0.93): > mod.gam <- gam(prestige ~ lo(income, span=.6), data=Prestige) >

> I have noticed a discrepancy between is.list() and is(x, ?list?) There's a similar problem with inherits(). On R 3.5.3: > f = function () 1 > class (f) = "f" > is.function (f) [1] TRUE > inherits (f, "function") [1] FALSE I didn't check what happens with: > class (f) = c ("f", "function") However, they should have the same

I'm using R on Windows v2.0.1 with the nlme package (v3.1-53) and am finding some unexpected discrepancies in the output of intervals.lme and coef.lme. I've included a toy dataset at the end, but briefly, the data are longitudinal data from couples in marital therapy. Each spouse's relationship satisfaction is measured 4 times; I've fit both linear and quadratic models to the

Hi All, There is some discrepancy between what quota reports and what du reports -bash-3.00$ cat /etc/redhat-release CentOS release 4.4 (Final) -bash-3.00$ pwd /home/corview -bash-3.00$ df . Filesystem 1K-blocks Used Available Use% Mounted on /dev/mapper/vgroot-LogVol01 10095152 586932 8995404 7% /home -bash-3.00$ du -sh . 19M . -bash-3.00$

Hi, When I place a legend on a plot it looks exactly as I intended on the screen. However, almost always, when I export this to postscript file, the legend's text protrudes through the legend's frame (the latter being placed correctly). See the appended example code. I can send the EPS file as well for those that are interested (<4 kb; <200 lines). I found nothing in the FAQS,

Hi, I found a discrepancy between results in R and Stata for a factor analysis with a promax rotation. For Stata: . *rotate, factor(2) promax* (promax rotation) Rotated Factor Loadings Variable | 1 2 Uniqueness -------------+-------------------------------- pfq_amanag~y | -0.17802 0.64161 0.70698 pfq_bwalk_~ø | 0.72569 0.05570

Dear R-devel, There seems to be a discrepancy in the order in which lm and rlm evaluate their arguments. This causes rlm to sometimes produce an error where lm is just fine. Here is a little script that illustrate the issue: > library(MASS) > ## create data > n <- 100 > dat <- data.frame(x=rep(c(-1,0,1), n), y=rnorm(3*n)) > > ## call lm, works fine > summary(lm(y ~

Hi! I want to create a discrepancy matrix. I have got a data.frame Q: number colour date 2 343 b 1503 3 678 g 1701 4 347 b 1904 5 345 b 2001 6 123 g 1809 Now i want to create a matrix, whose entries are the total differences between the ranks of number and date for each

Is there any way to _test_ if a given sequence is a low-discrepancy sequence? Or, equivalently, is there any way to measure the "discrepancy" of a sequence? WTIW, ... http://en.wikipedia.org/wiki/Low-discrepancy_sequence ... doesn't look like something implementation-friendly :-) Alberto Monteiro PS: low discrepany sequences are easily generated in R by the functions

Dear R community, I am trying to resolve a discrepancy between the way SPSS and R handle 2-way, repeated measures ANOVA. An experiment was performed in which samples were drawn before and after treatment of four groups of subjects (control and disease states 1, 2 and 3). Each group contained five subjects. An experimental measurement was performed on each sample to yield a

Install the gmp package, run your code, and then try this: bu <- gmp::as.bigq(u) bs4 <- bu[1] + bu[2] + bu[3] + bu[4] + bu[5] s4 <- as.double(bs4) s1 - s4 ## [1] 0 s2[[2]] - s4 ## [1] 7.105427e-15 s3 - s4 ## [1] 7.105427e-15 identical(s1, s4) ## [1] TRUE `bs4` is the exact sum of the binary rationals in your `u` vector; `s4` is the closest double precision to this exact sum.

Hi folks, I am working with repeated measures data and I ran into issues where the paired t-test results did not match those obtained by employing glht() contrasts on a lme model. While the lme model itself appears to be fine, there seems to be some discrepancy with using glht() on the lme model (unless I am missing something here). I was wondering if someone could help identify the issue. On

I am utilizing an openssh tunnel between two Linux boxes. On the client box I issue the following commands to set up the tunnel; - ssh -w0:0 root at x.x.x.x -v where x.x.x.x is the IP address of the Linux system running sshd - ip addr add 10.0.5.1/32 peer 10.0.5.2 dev tun0 - ip link set tun0 up On the box running sshd I issue the following commands: - ip addr add 10.0.5.2/32 peer 10.0.5.1 dev

On Wednesday 10 December 2003 06:33 am, Tom Eastep wrote: > On Tuesday 09 December 2003 09:29 pm, you wrote: > > I am sorry. I thought I was clear. I guess I did not state explicitly > > the discrepancy. > > Sorry for being snippy -- I shouldn''t let my personal problems leak out onto > the list. > > > The comments (#) I put in the original post (below)