similar to: Problem in summaryBy

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file

Hi, I ran R (version 2.9.0) CMD build under root in Fedora (9). When it tried to remove "junk files" it removed EVERYTHING in my local account! (See below). Can anyone tell me what happened, and even more importantly if I can I restore what was lost. Panickingly, Jarrod [jarrod at localhost AManal]$ R CMD build MCMCglmm_2.05 * checking for file 'MCMCglmm_2.05/DESCRIPTION'

I want to employ a parsimonious model to draw nomograms, as the full model is too complex to draw nomograms readily (several interactions of continuous variables). However, one interesting variable stays or leaves based on whether I choose "p value" or "AIC" options to fastbw(). My question boils down to this: Is there a theoretical reason to prefer one over another?

This looks like the result of including a C++ system header inside an extern "C" block. There is no evidence of this happening in the current version 2.22.1. However, it did happen in the previous version 2.22 via the chain of inclusions: MCMCglmmcc.h -> cs.h -> R.h -> various C++ system headers See Writing R Extensions P 108. I would check that the people reporting this bug

Hi All, Users have contacted me because they can not build MCMCglmm from source. All are using R 3.3.0 on various machines with different compilers gcc (Ubuntu 5.4.0-6ubuntu1~16.04.2) 5.4.0 g++ (Ubuntu 4.8.4-2ubuntu1~14.04.3) 4.8.4 Mac OS X El Capitan (version/compiler unspecified) The issue seems to be with mixing C/C++ with the repeated error: /usr/include/c++/5/bits/cpp_type_traits.h:118:3:

Jarrod, On 19 August 2016 at 04:43, Jarrod Hadfield wrote: | Hi All, | | Users have contacted me because they can not build MCMCglmm from source. All are using R 3.3.0 on various machines with different compilers | | gcc (Ubuntu 5.4.0-6ubuntu1~16.04.2) 5.4.0 | g++ (Ubuntu 4.8.4-2ubuntu1~14.04.3) 4.8.4 | Mac OS X El Capitan (version/compiler unspecified) | | The issue seems to be with mixing

I went through the gallium-nine tree and picked out nouveau patches that are general bug-fixes. The first bunch I'd like to also get into 10.2. I've reviewed all of them and they make sense to me, but sending them out for public review as well in case there are any objections. Unless I hear objections, I'd like to push this by Friday. Christoph Bumiller (11): nv50,nvc0: always pull

Hello all, I'm having a problem concerning choosing columns from a dataset in a function. I'm writing a function for data input etc., which first reads the data, and then does several data manipulation tasks. The function can be then used, with just giving the path of the .txt file where the data is being held. These datasets consists of over 20 different analytes. Though, statistical

I have a simple 1 way anova coded like summary(ANOVA1way <- aov(Value ~ WellID, data = welldata)) How can I use the BY function to do this ANOVA for each group using another variable in the dataset?? I tried coding it like this, but it doesn't seem to work. summary(ANOVA1way <- by(welldata, Analyte, function(x) aov(Value ~ WellID, data = welldata))) In SAS I would code it like this:

Hi folks, I''ve been working on the plugins system this weekend and I think I''ve nailed it down. The system will basically be able to load gems that are configured right to be plugins. It''ll do it fairly dynamically and shouldn''t require any configuration from the end user other than to install the gem. Doing this though will involve a bit of surgery on the

Hello, Has anyone out there encountered a problem like this: xyplot(Plasma ~ Serum | Analyte, data = sp.df, aspect = 1, layout = c(1, 1, 200), scales = list(relation = "free") ) Gives the error: Error in pretty(x[is.finite(x)], ...) : x must be numeric On the other hand, this works (but I don't want the default of having everything on the same

I am compiling a library with legacy code which has functions named with periods in the names - but are not S3 class functions. For example for example, summary.agriculture is not an extension of the summary function for and 'agriculture. class object - it is just poorly named. Is it possible to keep from triggering the following warning when I check the package? * checking S3

Before I receive a barrage of 'try looking in the help file' messages, I have and to no avail. For a new user of R I would like to point out that in order to be able to use the help files/manuals effectively one must know the correct question and that only comes with using R! Could someone please direct me to why I keep getting the following error message "Error: subscript out of

Apologies to the listing, the problem was with the data set and not the code Thanks Michael Griffiths, Ph.D. Chemometrician Training, Quality and Statistics Group LGC Limited Queens Road Teddington Middlesex, TW11 0LY, UK Tel: +44 (0)20 8943 7352 Fax: +44 (0)20 8943 2767 e-mail: michael.griffiths at lgc.co.uk >>> "Michael Griffiths" <Michael.Griffiths at lgc.co.uk>

HI I'm a student in chemical engineering, and i have to implement an algoritm about FIVE PARAMETERS INTERPOLATION for a calibration curve (dose, optical density) y = a + (c - a) /(1+ e[-b(x-m]) where x = ln(analyte dose + 1) y = the optical absorbance data a = the curves top asymptote b = the slope of the curve c = the curves bottom asymptote m = the curve X intercept Have you never seen

Dear R-helpers I am running a simple mixed effects model using lme(). The call looks like this: fit <- lme(Analyte~Sample, data=Data, random=~1 | Run) I am particularly interested in the estimated random effects. When I print the 'fit' object, it looks something like example below: (...) Random effects: Formula: ~1 | Run (Intercept) Residual StdDev: 3.483794 3.637523

Using a "by() statement, I am preparing ANOVA's for multiple experiments, and using simint() to generate confidence intervals. This works fine. simint.by.fit <- by(analytes.dfr, list(Assay = analytes.dfr$analyte ), function(data) (simint(value ~ tx, data = data,type='Tukey' ) ) ) I can separately prepare plots of the confidence intervals, and I can prepare separate plots

Hi, First let me thank you for the incredible help and resource that this forum is. I am trying to compare the repeated measurement of more than 100 analytes that have been take in 70 subjects at 2 time points adjusted for the time difference of sample times(TimeDifferenceDays), therefore I wanted to do it with a function that allows me to do all at once. (131 is the column difference that

Greetings. I'm teaching linear regression this semester and that means I write more functions for my regression support package "rockchalk". I'm at a point now were some fresh eyes would help, so if you are a student in a course on regression, please consider looking over my package overview document here: http://pj.freefaculty.org/R/rockchalk.pdf That tells how you can grab