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...el checking. Are there any nice and simple ways doing this? If so, how? If not, I'd be grateful for receiving some hints on how this can be accomplished. Many thanks, Henric --------------------------------------------------------------------------------------- Henric Nilsson, Statistician Statisticon AB, ?stra ?gatan 31, SE-753 22 UPPSALA Phone (Direct): +46 (0)18 18 22 37 Mobile: +46 (0)70 211 68 36 Fax: +46 (0)18 18 22 33 <http://www.statisticon.se>

....2)+s(x,by=a.3), where a.1--a.3 are dummy variables representing each level of the factor? Or is the s(x) term redundant? Any hints are greatly appreciated. Best wishes, Henric --------------------------------------------------------------------------------------- Henric Nilsson, Statistician Statisticon AB, ?stra ?gatan 31, SE-753 22 UPPSALA Phone (Direct): +46 (0)18 18 22 37 Mobile: +46 (0)70 211 68 36 Fax: +46 (0)18 18 22 33 <http://www.statisticon.se>

...9; anova.gam() to get an overall test. (Perhaps with the addition of a choice between Type I and Type III tests, but I guess that may be controversial). Is it possible? //Henric --------------------------------------------------------------------------------------- Henric Nilsson, Statistician Statisticon AB, ?stra ?gatan 31, SE-753 22 UPPSALA Phone (Direct): +46 (0)18 18 22 37 Mobile: +46 (0)70 211 68 36 Fax: +46 (0)18 18 22 33 <http://www.statisticon.se>

Dear R users: How can I use panel.qqmathline, in package lattice, to add straight lines onto the plots generated by qqmath? I read help pages of qqmath, panel.qqmathline, xyplot, ..., but just can't one example that shows how to make it work. For example, > data(sleep) > qqnorm(~ extra | group, data=sleep, aspect=1) how can I use panel.qqmathline? Thanks very much for your help,

...? Best Regards! Ivy Li YMS in Production & Testing Semiconductor Manufactory International(ShangHai) Corporation #18 ZhangJiang Road, PuDong New Area, Shanghai, China Tel: 021-5080-2000 *11754 Email: Ivy_Li at smics.com -----ԭʼÓʼþ----- ·¢¼þÈË: Henric Nilsson [mailto:henric.nilsson at statisticon.se] ·¢ËÍʱ¼ä: 2004Äê10ÔÂ15ÈÕ 17:39 ÊÕ¼þÈË: Ivy_Li ³­ËÍ: r-help at stat.math.ethz.ch Ö÷Ìâ: Re: [R] R plot problems At 16:17 2004-10-15 +0800, you wrote: >[...] I want to rotate the direction of x-coordinates' letter so that it >can show all. But I don't know how to write this opti...

Dear all, I've got some questions, probably due to misunderstandings on my behalf, related to fitting overdispersed binomial data using glm(). 1. I can't seem to get the correct p-values from anova.glm() for the F-tests when supplying the dispersion argument and having fitted the model using family=quasibinomial. Actually the p-values for the F-tests seems identical to the p-values for

Full_Name: Henric Nilsson Version: 2.3.0 Patched (2006-05-09 r38014) OS: Windows 2000 SP4 Submission from: (NULL) (83.253.9.137) When supplying an unavailable link to `quasibinomial', the error message looks strange. E.g. > quasibinomial("x") Error in quasibinomial("x") : 'x' link not available for quasibinomial family, available links are "logit",

Dear list, I've noticed that influence.measures and cooks.distance gives different results for non-gaussian GLMs. For example, using R-1.9.0 alpha (2003-03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome +

Hi everyone, I've tried the below on R 1.9.1 and the 2004-08-30 builds of R 1.9.1 Patched and R 2.0.0 on Windows 2000, and the results are consistent. > seq(0.5, 0, by = -0.1) [1] 0.5 0.4 0.3 0.2 0.1 0.0 > seq(0.7, 0, by = -0.1) [1] 7.000000e-01 6.000000e-01 5.000000e-01 4.000000e-01 3.000000e-01 2.000000e-01 1.000000e-01 -1.110223e-16 Is this really the intended behaviour?

Hi, I am fairly new to GAM and started using package mgcv. I like the fact that optimal smoothing is automatically used (i.e. df are not determined a priori but calculated by the gam procedure). But the mgcv manual warns that p-level for the smooth can be underestimated when df are estimated by the model. Most of the time my p-levels are so small that even doubling them would not result

All, Sorry for bombarding you with GAM related questions, but... I know a partial residual option in plot.gam() is on Simon Wood's todo list, but since I'm in the midst of a project and not yet having acquired sufficient R knowledge to code something usable myself I'll have to put my trust in you. Anybody got some code lying around for doing this? Or if someone can supply me with

Dear all, Using the quasibinomial and quasipoisson families results in no AIC being calculated. However, a quasi AIC has actually been defined by Lebreton et al (1992). In the (in my opinon, at least) very interesting book by Burnham and Anderson (1998,2002) this QAIC (and also QAICc) is covered. Maybe this is something that could be implemented in R. Take a look at page 23 in this pdf:

Hi, I'd like to remove the axes from a plot produced by stripchart(). However, when trying stripchart(..., axes = FALSE), I get the error meassage Error in stripchart(hypokvot1 ~ treatment, "jitter", pch = 1, vert = TRUE, : unused argument(s) (axes ...) using R 1.8.1 on Windows. Can it be done some other way? If not, maybe this functionality can be added to a future version of

Full_Name: Henric Nilsson Version: 2.2.0 (2005-06-20 r34776) OS: Windows 2000 Submission from: (NULL) (213.115.23.26) The help page for `weighted.residuals' states that the function can be used with both `lm' and `glm' objects. However, it's unclear what's meant by the following passage "Weighted residuals are the usual residuals Ri, multiplied by wi^0.5, where wi are

Dear all, After having set par(mfrow = c(2, 2)) and switching between plot() and xyplot() using R 2.0.0 Patched (2004-10-13) under Windows 2000, the lattice plot gets overplotted. I also tried this under 1.9.1 Patched (2004-09-22), since this is the only older version I've got installed, and it did the same thing apart from that the overplotting now starts in the opposite lower corner.

Dear list, I'm trying to mimic the analysis of Wedderburn (1974) as cited by McCullagh and Nelder (1989) on p.328-332. This is the leaf-blotch on barley example, and the data is available in the `faraway' package. Wedderburn suggested using the variance function mu^2(1-mu)^2. This variance function isn't readily available in R's `quasi' family object, but it seems to me