search for: fity

On 21 May 2015, at 12:49 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: >>>>>> peter dalgaard <pdalgd at gmail.com> >>>>>> on Thu, 21 May 2015 11:03:05 +0200 writes: > >> On 21 May 2015, at 10:35 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: > >>>> >>>> I noticed that

I think there are two bugs in aov() that shows up when the right hand side of `formula' contains both `-1' and an Error() term, e.g., aov(y ~ a + b - 1 + Error(c), ...). Without `-1' or `Error()' there is no problem. I've included and example, and the source of aov() with suggested fixes below. The first bug (labeled BUG 1 below) creates an extra, empty stratum inside

I believe you are right, but can you please explain why anyone would want to fit this model? It differs only in the coding from aov(y ~ a + b + Error(c), data=test.df) and merely lumps together the top two strata. There is a much simpler fix: in the line if(intercept) nmstrata <- c("(Intercept)", nmstrata) remove the condition (and drop the empty stratum later if you

Hey, I have stacked a couple of garchFit objects in a list with names $fit1, $fit2, ..., $fiti assigning objects names using a loop, i.e. after running the loop modelStack = list($fit1, $fit2,...,$fiti). Thus the following apply; a = modelStack$fit2, then a is the second garchFit object of formal class 'fGarch' with 11 slots, @call, @formula... etc. I then want to extract information in

...), data=data, na.action=na.exclude, start=st2, algorith='LM', lower=c(-0.0001,-1e-8,0.05,0.2), #upper=c(1e-6,0.003,0.08,0.35), upper=c(Inf,Inf,0.07,Inf), trace=F ) #Predict fitting values fity<-predict(fit,data$x) plot(data$x,data$y) lines(data$x,fity,col=2) text(0.4,0.08,coef(fit)['y0']) text(0.4,0.07,coef(fit)['A']) text(0.4,0.06,coef(fit)['w']) text(0.4,0.05,coef(fit)['xc']) Best regard Martin [[alternative HTML version deleted]]

>>>>> Patrick Perry <pperry at stern.nyu.edu> >>>>> on Wed, 27 May 2015 23:19:09 -0400 writes: {@PP, you forgot this part:} >>>>> peter dalgaard <pdalgd at gmail.com> >>>>> on Thu, 21 May 2015 14:36:03 +0200 writes: >> I suspect that what we really need is >> >> fitI <- lm(x ~

On 21 May 2015, at 10:35 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: >> >> I noticed that the 3.2.1 release cycle is about to start. Is there any >> chance that this fix will make it into the next version of R? >> >> This bug is fairly serious: getting the wrong variance estimate leads to >> the wrong log-likelihood and the wrong

Dear all, When analyzing data from a climate change experiment using linear mixed-effects models, I recently came across a situation where: - the summary(model) showed a significant difference between the levels of a two-level factor, - while the anova(model) showed no significance for that factor (see below). My question now is: Is the anova.lme() approach correct for that model? And why does

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