similar to: Is there a /ddfm=satterth for R?

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

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! 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! 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,

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 recently switched mail program to Mozilla Thunderbird, running on W2k. Everything works fine, except the MIME-digests from this list. The decoding doesn??t work properly. I had contact with Martin Maechler some time ago, and he suggested a try on this list for ideas on how to do the decoding in Windows, even if it??s not a proper R-question. Outlook do the proper job on the MIME,

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! R2.0.1, 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! 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?

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

Hi, I am trying to parameterize the following mixed model (following Piepho and Ogutu 2002), to test for a trend over time, using multiple sites: y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij] where: y[ij]= a response variable at site i and year j mu = fixed intercept Beta=fixed slope w[j]=constant representing the jth year (covariate) b[j]=random effect of jth year, iid N(0,sigma2[b]) a[i]=random

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 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

I have one fixed effect, sor, with two levels. I have eight lots and three wafers from each lot. I have included the data below. I would like to fit a mixed model that estimates a covariance parameter for wafer, which is nested in lot, and two covariance parameters for lot, one for each level of sor. The following command fits the model that I want, except for it estimates the correlation

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

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