similar to: Find and remove matching parentheses

Dear R-help, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value

Dear all, I'm analysing a split-plot experiment, where there are sometimes one or two values missing. I realized that if the data is slightly unbalanced, the effect of the subplot-treatment will also appear and be tested against the mainplot-error term. I replicated this with the Oats dataset from Yates (1935), contained in the nlme package, where Variety is on mainplot, and nitro on

When we call a lattice function such as xyplot, to what extent does the "data" designation cause the function to look inside the "data" for variables? In the examples below, the "subset" argument understands that "Variety" is a variable in the data. But the "scales" argument does not understand that "nitro" is a variable in the data.

Dear List, I have been using R to create an xyplot using the panel function within lattice libraries. This plot is based on the data supplied in R named 'Oats'. The graph represents oat yield by nitro level with an overlay of each variety of oats for each nitro level. I have three questions regarding this graph: 1) I cannot seem to specify the type of symbol used by the plot, even though

https://bugs.freedesktop.org/show_bug.cgi?id=103689 Bug ID: 103689 Summary: there is an exploitable page fault that can be reliably triggered from the chromium sandbox can possibly lead to remote attackers causing a denial of service condition or possibly running system code. Product: xorg

The following code produces a legend ("key") that mentions the unused levels of Block. library(MEMSS) xyplot(yield~nitro, subset=(Block=="I" | Block=="II"), data=Oats, group=Block, auto.key=T) and adding "drop.unused.levels=T" does not fix it. And in fact even the following does not solve the problem: xyplot(yield~nitro,

Dear all, Consider a completely randomized block design (let's use data(Oats) irrespoctive of the split-plot design it was arranged in). Look: library(nlme) fit <- lme(yield ~ nitro, Oats, random = ~1|Block, method="ML") fit2 <- lm(yield ~ nitro + Block, Oats) anova(fit, fit2) gives this: Model df AIC BIC logLik Test L.Ratio p-value fit 1 4 624.3245

Ben, I would like to test the sulfur on the clover field, nitrogen on the clover field and then test for the presence of interaction. Sorry about the last email, seems it really screwed itself over, here it is again, hopefully nicer: Nitrogen(0) Nitrogen(20) Sulfur(0) 4.54 5.73 Sulfur(3) 4.64

Hi, what is the preferd way to configure/start mongrel? [ ] with Mongrel::Configurator => HttpServer [ ] @var = HttpServer.new(...) @var.run I ask because the only debug methode i have found was in Mongrel::Configurator ;-) @Zed: are you also subscribed on nitro list?! regards Alex

Dear R users, this is a follow up of this message http://tolstoy.newcastle.edu.au/R/e6/help/09/05/13897.html I'm reproducing the core of it for convenience. > // > / data(Oats, package = "MEMSS") / > / tp1.oats <- xyplot(yield ~ nitro | Variety + Block, / > / data = Oats, / > / panel = function(x, y, subscripts, ...) { /

# 2.2.12: /etc/dovecot/dovecot.conf # OS: Linux 3.15.4-x86_64-linode45 x86_64 openSUSE 12.3 (x86_64) Short version: 1) I filter out spam by using a dovecot sieve filter to act based on a header inserted by bogofilter 2) In some cases, sieve fails to correctly file based on this header 3) Testing with sieve-test indicates that it's testing the wrong header 4) Suggestions? Am I doing

Hi, i''m currently looking into a problem with the tests for ActionPack. 127 of them are failing on my windows machine. On Linux everything is working fine so far. I just wasted a good part of an hour trying to find the problem on windows...the solution is: Nitro and not Windows. I''m using Edge Rails and have no rails gem installed. On Windows I have the Nitro/Glue/Og gems

Dear R developers, I find the which.min function very handy. However, maybe you could consider a tiny addition to the example data in the the help text. By adding another zero to the vector x... x <- c(1:4, 0, 0:5,11) ...the example would more explicitly show that which.min actually 'giv[es] the index of the /first/ minimum or maximum respectively of x'. This will also more

I''ve recently upgraded my box from Ubuntu Breezy to Dapper, and for the life of me can''t figure out why I''m getting this exception thrown. I''ve switched from Switchtower to Capistrano, and when I run rake deploy I get this output: ** [update_code] exception while rolling back: NameError, uninitialized constant Net::SSH::Transport::OSSL

Hi, Thanks for all the effort that has gone into making RSpec available. I''m not a professional developer and I''ve found the approach articulated has helped me a lot in writing my code, and understanding that of others. To cut my teeth, I''m trying to write an db adapter for the og project. I''ve being iterating with RSpec and it''s exposed some

We have extracted parameters from physiological measurements by fitting SSlogis-like curves with nlsList and nlme. We presented residuals plot in a paper, but a referee argues that these cannot be included (too technical), and r-square values should be given instead to compare the goodness of fit with those of other authors. I remember that 30 years ago in my stat 101, I learned that r-square is

I can't get the PERL subexpression translated to R. Following, for example, B. Ripley's http://finzi.psych.upenn.edu/R/Rhelp02a/archive/58984.html I am using sub, but it looks like an ugly substitute. Assume I want to extract the first alpha part and the first numeric part, but only if they are in sequence. Do I really have to use the sub twice, first extracting the first variable, then

Hi there, I hope I can get advice regarding the calculation of leverage values or studentized residual values of a non-linear regression model. It seems like rstudent() does not work on a nls object. Many thanks in advance! Best regards, Xingli

Dear R and lme4 users- I am trying to fit a mixed-effects model, with the glmer function in lme4, to right-skewed, zero-inflated, non-normal data representing understory grass and forb biomass (continuous) as a function of tree density (indicated by leaf-area). Thus, I have tried to specify a Gamma distribution with a log-link function but consistently receive an error as follows: >

Dear List, creating factors in a given non-default orders is notoriously difficult to explain in a course. Students love the ifelse construct given below most, but I remember some comment from Martin M?chler (?) that ifelse should be banned from courses. Any better idea? Not necessarily short, easy to remember is important. Dieter data = c(1,7,10,50,70) levs =