similar to: xyplot gets overplotted by plot

Dear all, I'm analyzing a binary outcome using glm() with a binomial distribution and a logit link, and have now reached the point where I'd like to do some model checking. Since my data are in binary form I'd like to collapse over the cross-classification of the factors before the model checking. Are there any nice and simple ways doing this? If so, how? If not, I'd be

Dear all, I'm a fairly new R user having two questions regarding gam: 1. The prediction example on p. 38 in the mgcv manual. In order to get predictions based on the original data set, by leaving out the 'newdata' argument ("newd" in the example), I get an error message "Warning message: the condition has length > 1 and only the first element will be used in: if

Thank you for your help! I gave you an example, you could run it in R . Maybe you will understand my meaning clearly. x <- data.frame(main.name="AAA", x.name=rep(c("Apply","Watermelon","Lemon","Banana",

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?

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 +

Dear all, I've now spent a couple of days trying to learn R and, in particular, the gam() function, and I now have a few questions and reflections regarding the latter. Maybe these things are implemented in some way that I'm not yet aware of or have perhaps been decided by the R community to not be what's wanted. Of course, my lack of complete theoretical understanding of what

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",

Hi, I've noted that not all `predict' methods are hidden in the namespace: > methods("predict") [1] predict.ar* predict.Arima* [3] predict.arima0* predict.glm [5] predict.HoltWinters* predict.lm [7] predict.loess* predict.mlm [9] predict.nls* predict.poly [11] predict.ppr* predict.prcomp* [13]

>>>>> Henric Winell <nilsson.henric at gmail.com> >>>>> on Tue, 7 Feb 2017 13:37:42 +0100 writes: > Hi, On 2017-02-07 13:12, Benjamin Tyner wrote: >> Hello >> >> When loading a package, I'm wondering if it's frowned >> upon for the package to alter the state of the random >> number

Hi, The added support for semi-transparent colours in `windows' under R-2.6.0 for Windows is much appreciated. However, I've discovered that issuing a `layout' (or `par' with arguments `mfcol'/`mfrow') call and then trying to plot several figures with semi-transparent colour on the same page results in only the first one being fully drawn. E.g. > x <-

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

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,

Hello, Inline. Em 12-04-2017 16:40, Henric Winell escreveu: > (Let's keep the discussion on-list -- I've added back R-devel.) > > On 2017-04-12 16:39, Ulrich Windl wrote: > >>>>> Henric Winell <nilsson.henric at gmail.com> schrieb am 12.04.2017 >>>>> um 15:35 in >> Nachricht <b66fe849-bb8d-f00d-87e5-553f866d57e0 at gmail.com>:

Marius Hofert-4------------------------------ > Den 2015-10-09 kl. 12:14, skrev Martin Maechler: > I think so: the code above doesn't seem to do the right thing. Consider > the following example: > > > x <- c(1, 1, 2, 3) > > rank2(x, ties.method = "last") > [1] 1 2 4 3 > > That doesn't look right to me -- I had expected > > >

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

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

The last line of the example in droplevels' manual page seems to be incorrect to me. I think it should read: "table(droplevels(aq$Month))". Amazingly (I don't understand) both variants seem to produce the same result (R 3.3.3): --- > aq <- transform(airquality, Month = factor(Month, labels = month.abb[5:9])) > aq <- subset(aq, Month != "Jul") >

Dear all, I had a R segmentation fault, and then invoked debug mode and ran step by step. When I reached "terms(Y~X1*X2*...*X16)", I would then have "segmentation" fault. However, if I just ran this under regular "R interactive" mode, it would be fine though taking long time. My questions are: 1. Is there a known limit of terms for a formula? 2. Why does the

Dear all, I had a R segmentation fault, and then invoked debug mode and ran step by step. When I reached "terms(Y~X1*X2*...*X16)", I would then have "segmentation" fault. However, if I just ran this under regular "R interactive" mode, it would be fine though taking long time. My questions are: 1. Is there a known limit of terms for a formula? 2. Why does the