similar to: gam questions

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

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

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

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,

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",

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 +

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 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?

Hello everyone. I am doing a logistic gam (package mgcv) on a pretty large dataframe (130.000 cases with 100 variables). Because of that, the gam is fitted on a random subset of 10000. Now when I want to predict the values for the rest of the data, I get the following error: > gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1, +

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

Hi, I'm using a loop to evaluate several models by taking adjacent variables from my dataframe. When i try to get predictions for new values, i get an error message about a missing variable in my new dataframe. Below is an example adapted from ?gam in mgcv package library(mgcv) set.seed(0) n<-400 sig<-2 x0 <- runif(n, 0, 1) x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) x3 <-

Dear R users, I'm new to both R and to this list and would like to get advice on how to build generalized additive models in R. Based on the description of gam, which I found on the R website, I specified the following model: model1<-gam(ST~s(MOWST1),family=binomial,data=strikes.S), in which ST is my binary response variable and MOWST1 is a categorical independent variable. I get the

Dear R-hep, I'm trying to get the first derivative of a smooth from a gam model like: model<-gam(y~s(x,bs="cr", k=5)+z) and need the derivative: ds(x)/dx. Since coef(model) give me all the parameters, including the parameters of the basis, I just need the 1st derivative of the basis s(x).1, s(x).2, s(x).3, s(x).4. If the basis were generated with the function

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]

Hi folks, I originally tried R-SIG-Mixed-Models for this one (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q3/004170.html), but I think that the final steps to a solution aren't mixed-model specific, so I thought I'd ask my final questions here. I used gamm4 to fit a generalized additive mixed model to data from a AxBxC design, where A is a random effect (human participants in

>>>>> 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

Dear R-users: Im trying (using gam package) to develop a stepwise analysis. My gam object contains five pedictor variables (a,b,c,d,e,f). I define the step.gam: step.gam(gamobject, scope=list("a"= ~s(a,4), "b"= ~s(b,4), "c"= ~s(c,4), "d"= ~s(d,4), "e"= ~s(e,4), "f"= ~s(f,4))) However, the result shows a formula containing the whole

I fitted a GAM model with Poisson distribution to a data with about 200 observations. I noticed that the plot of the residuals versus fitted values show a trend. Residuals tend to be lower for higher fitted values. Because, I'm dealing with count data, I'm thinking that this might be due to overdispersion. Is there a way to account for overdispersion in any of the packages MGCV or GAM?

Hi list, I'm working with gamm models of this sort, using Simon Wood's mgcv library: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group in order to test whether tensor product smooths vary across factor levels. I was wondering if comparing those two

I have a question about how to compare a GLM with a GAM model using anova function. A GLM is performed for example: model1 <-glm(formula = exitus ~ age+gender+diabetes, family = "binomial", na.action = na.exclude) A second nested model could be: model2 <-glm(formula = exitus ~ age+gender, family = "binomial", na.action = na.exclude) To compare these two GLM