CALCULATE DEGREES OF FREEDOM IN R CODE

In that chapter we did find a significant effect of drug, but at the end of the chapter we also ran an analysis to see if there was an effect of therapy. Another example appears in Chapter 14, in which we were looking at the effect of different drugs on the mood.gain experienced by each person. I gave one example of how this kind of design might arise above. In this section, I’ll discuss a broader class of experimental designs, known as factorial designs, in we have more than one grouping variable. When we discussed analysis of variance in Chapter 14, we assumed a fairly simple experimental design: each person falls into one of several groups, and we want to know whether these groups have different means on some outcome variable. > Correo Yahoo!, el mejor correo web del mundo > Biología y Manejo de Recursos Acuáticos > another list, s-news at, for questions about S-PLUS. > Technically this email list is for questions about R. > nearly 15 years and I still don't understand. I have been studying mixed-effects models for > this list to give you an explanation of why there should be different > I will defer to any of the 'degrees of freedom police' who post to > you need to know is that it is 'large'. > actual number of degrees of freedom is irrelevant in this case. > t-distribution with 402 df and a t-distribution with 2549 df so the > I would point out that there is effectively no difference between a > Models in S and S-PLUS' published by Springer. > The algorithm is described in Pinheiro and Bates (2000) 'Mixed-effects

> Why are the DF of Linf and K different? I would apreciate if you could point me to a reference > Nonlinear mixed-effects model fit by maximum likelihood > na.action= na.include, naPattern = ~!is.na(dLt)) > when runing nlme() on the following model: > So do I and I'm one of the authors of the package :-) > Hi, I' m having a hard time understanding the computation of degrees of freedom > On 1/27/06, gabriela escati peñaloza wrote: > Cc: R-help at > Emne: Re: how calculation degrees freedom > Fra: r-help-bounces at på vegne af Douglas Bates I don't think the 'degrees of freedom police' would find that to be a The anova function could then print that 'these p-values are large sample ones and hence only approximate'. It could be nice, however, if anova() produced even an approximate anova table which can be obtained from Wald tests.

> Along similar lines, I've noticed that the anova() function for lmer models now only reports the mean squares to go into the numerator but 'nothing for the denominator' of an F-statistic probably in recognition of the degree of freedom problem. P-values the HPDinterval function from the coda package can create If you are interested in intervals rather than Posterior distribution of the parameters using Markov Chain MonteĬarlo sampling. > Of course, Monte Carlo p-values have their problems, but the world is not perfect.Īnother approach is to use mcmcsamp to derive a sample from the

CALCULATE DEGREES OF FREEDOM IN R CODE

It is fairly fast to and takes about 10 lines of code to program. 2 log Q) in an empirical distribution based on simulations under the model that is to calculate a Monte Carlo p-value. So one way to 'get around the problem' could be to evaluate the test statistic (e.g. simulate() is very fast - just like lmer(). > However, I'll just point out that for lmer models, there is a simulate() function which can simulate data from a fitted model. > Degrees of freedom for mixed models is a delicate issue - except in certain orthogonal designs.

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Calculate degrees of freedom in r