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Thanks a lot to Demitris for a prompt answer some minutes ago on another tread (see below). To avoid excess mails on the list, I move onto next question: I have another small plotting problem that confuses me. I want to plot results from a field trial series, using the numbers of the trials as symbols in the plot. pch = as.character(trial_no) works fine, but truncates the trial number to the

Hello all, R2.1.1, W2k I try to make a plot of a simple regression model in this way: > with(njfA_bcd, { + plot(TC_OS.G31,Prot,cex = 2, col = "red", xlab= "TC/OS at GS32", + ylab="Grain crude protein (CP)") + }) This part works well and produces the datapoints as red circles. When I try to add a line, using a fitted linear model in a way that works perfect

R2.1.0 JGR 1.2 W2k Hello all! I??ve just installed JGR on my both R-equipped computers and am very pleased with the look and functionality. Except in one, very important, way. I can??t figure out how to get the ~ sign from the keyboard to the console. Copying it from old code works fine. Using the traditional GUI works as usual. I have a Swedish keyboard layout, where ~ shares key with ??

Hello all, R2.1.1, Wk2 I am doing some two-step plotting, first using plot() to illustrate the datapoints and then using abline() to place a trend line from a fitted model into the plot. Everything works well as long as the formula of the fitted model i of the type: m1 <- lm(Dependent ~ Independent) then abline(m1) puts the proper straight line into the plot. But if I use: m2 <-

Hello all! R2.0.1, W2k I posted this question to the list last Sunday without getting any replies on the list. I got two off the list though, suggesting me to plot "manually" as a second step, from estimable() or intervals() objects respectively. As this was not really what I wished for, I take the risk to upset somebody with a trivial question, and re-post it (just a little

Hello all! I have a problem that calls for a better understanding, than mine, of how lme() uses the random part of the call. The dataset consists of eleven field trials (Trial) with three replicates (Block) and four fertiliser treatments (Treat). Analysing for example yield with lme() is easy: m1 <- lme(Yield ~ Treat, data=data, random =~1| Trial/Block) giving estimates of

Hello everybody! I use R 1.6.2 in Windows, and have a problem controlling the na.action. In a dataset with twelve trials, one of the trials lack any readings of the variable "STS.SH" (standing power at harvest) Fitting an aov() object with the call: led1t7sts.aov <- aov(STS.SH ~ Trial/Block + Treatment + Treatment:Trial, data = led1t7, na.action=na.exclude) seems to work as it

Hello all! Consider a dataset with a grouping structure, Group (factor) Several treatments, Treat (factor) Some sort of yield, Yield (numeric) Something, possibly important, measured for each group; GroupCov (numeric) To look for fixed effects from Treat on Yield, a first attempt could be: m1 <- lm(Yield ~ Treat) which gives, in a symmetric situation, the same estimated fixed effects as:

Hello all, R2.2.1, W2k I posted a similar question last week after having problem with a group update (using update.packages()), without getting any answer. Now I have updated all packages separately, and found that the problem comes from nlme: Everything comes home, the md5 checksums are ok, but then the old version can??t be removed from my computer, leaving a "nlme" library with

Hello all! System: R2.0.1, W2k. All packages apdated with update.packages(). I use the default graphic device for my system (no active choice). I?m trying to fine-tune scatterplot() graphs (package car), for publication. I want to control plotting symbols and colours to match plots from other, less flexible, systems. I also need to make all text and symbols bigger, to enable printing of the

Hello all! I?ve been using Rcmdr for some time, as a quick way of producing graphics and basic statistics. I run R1.8.1, OS W2000. Two days ago the dataset loader stopped working. Normally, the button <No active dataset> is clickable to give you the opportunity to choose dataset to load in the Rcmdr context. Clicking on the button now produces this: Rcmdr Version 0.9-3 Error in parse(file,

Dear all, W.XP I recently upgraded to R2.10.0 and did what I usually do: copied all "special" libraries from the old installation and then used "update.packages()" on the command line. Nothing happened. Then I clicked on the same command on the droplist which generated "update.packages(ask='graphics')", but still with no effect. When I tried to load the

Hello all, I posted a similar question recently but I suspect that it was not well enough formulated to trigger any answers. So I try again: Is there a way to produce boxplots (or something similar) that uses the estimated fixed effects of an lme{nlme} object? When I want to know the mean, I use estimable{gmodels} together with an appropriate matrix. I did look for a similar tool to estimable in

Dear all, W2k, R 2.5.1 I am trying out the AMMI function in the agricolae package, to analyse the dependence of environment for a certain cultivar. The function responds to four basic variables: ENV Environment GEN Genotype REP Replication Y Response My question concerns the 'REP' term. When I normally do an analysis of variance, the replication would refer to repeated observations

Hello all! R2.0.1, W2k. All packages updated. I?m heavily dependant on using mixed models. Up til?now I have used lme() from nlme as I have been told to. Together with estimable() from gmodels it works smooth. I also often run Rcmdr, mostly for quick graphics. After using Rcmdr, on reopening the R workspace all help libraries for Rcmdr (22 !) loads, among them nlme, but not Rcmdr itself. Why?

Dear all! W2k, R 2.5.1 I am working with an ongoing malting barley variety evaluation within Sweden. The structure is 25 cultivars tested each year at four sites, in field trials with three replicates and 'lattice' structure (the replicates are divided into five sub blocks in a structured way). As we are normally keeping around 15 varieties from each year to the next, and take in 10 new

Hello all! When you are working with a little more complicated models in SAS PROC MIXED, you often use the /ddfm=satterth call to make sure the df decomposition is done the best way possible. Running the same models in lme, without any special calls, results in warning messages about the df handling. Is anybody out there working with something like the /ddfm=satterth? It would be handy, or

Hello everybody! I?m working with a dataset from eleven field trails on barley fertilization. I use R 1.6.2 (Windows) It is quite easy to fit aov() objects to the dataset. The call: > (l1t4y.aov <- aov(Yield ~ Trial + Treatment, data=led1t4b)) Results in an object with this anova table: Df Sum Sq Mean Sq F value Pr(>F) Trial 10 121423585 12142358 63.499

Hi Björn, 1) The pass pipeline syntax is documented here: https://github.com/llvm-project/llvm/blob/master/include/llvm/Passes/PassBuilder.h#L378 -die is not implemented, since the new pass manager does not support BasicBlock passes. But you can use dce instead: "-passes=dce" 2) I don't have a qualified answer here, but if I recall correctly, the trouble to correctly update the

Dear all R2.10 WinXP I have a dataset dealing with the way different wheat cultivars build their yield. Wheat ears are organised in spikelets where the spikelets can be numbered from the bottom, with even numbers on one side and odd on the other. I know how many kernels there were in each spikelet after some months spent counting them... Now I want to illustrate the differences between the