similar to: error in "predict.gam" used with "bam"

Hello, I'm trying to use the bam() function in the R mgcv package for a large set of grouped binary data. However, I have found that this function does not take data in the format of cbind(numerator, denominator) on the left hand side of the formula. As an example, consider the following dat1 <- data.frame(id=rep(1:6, each=3), num=rbinom(18, size=10, prob=0.8), den=rbinom(18, size=5,

Dear useRs, This is the first time I post to this list and I would appreciate any help available. I've used the excellent mgcv package for a while now to investigate geographical patterns of language variation, and it has has always worked without any problems for me. The problem below occurs using R 2.14.0 (both 32 and 64 bit versions in Windows and the 64 bit version in Unix) and mgcv (both

Dear useRs, I reran an analysis with bam (mgcv, version 1.7-17) originally conducted using an older version of bam (mgcv, version 1.7-11) and this resulted in the same estimates, but much lower standard errors (in some cases 20 times as low) and lower p-values. This obviously results in a larger set of significant predictors. Is this result expected given the improvements in the new version? Or

Hi R users, I'm using the 'bam' function in mgcv to examine trends in a remotely sensed vegetation index. I have one random effect variable, 'cons' (with six levels) which identifies different subjects within this analysis. My model is specified as follows: rm4<-bam(trend~factor(zone)+s(cons,bs="re")+s(year,

Hi all: I've got a data of ".bam" which is created from my partner under linux sysyem. My system is window xp, and I wanna know how to read the .bam file. Many thanks! My best [[alternative HTML version deleted]]

Do you have the mgcv package installed (I think it's part of the standard distro, though) /loaded? ziP is there, not in BAM. Other than that, sorry, no clue. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon, Nov 6,

Dear all, I am trying to use 'bam' to run the following generalized additive model: ### m <- bam(result ~ factor(city) + factor(year) + lnpopulation + s(lnincome_pc) + ,data=full_df,na.action=na.omit,family=ziP(theta = NULL, link = "identity",b=0)) But getting the following error: ### Error in bam(result : extended families not supported by bam The documentation for

Hi, I also had some problems using chan_oh323 together with gnugk. * <-> gnugk <-> h323-phone When I called the phone and hang up, befor the phone was picked up, the h323-phone continued ringing. The same, when the h323- and some sip-phones were called, and the sip-phone picked up the call first. (It is annoying, when you are talking to someone at the phone and the phone on the

Hello, I am trying to update the mgcv package on my Linux box and I keep getting an "Out of memory!" error. Does anyone know of a fix for this? Below is a snippet of the message that I keep getting: Thank you. Geoff ** R ** inst ** preparing package for lazy loading ** help *** installing help indices >>> Building/Updating help pages for package 'mgcv' Formats:

Hello R users, I have a quick question I was hoping to get your input on. I am new to R and the smooth statistical regression world, and am trying to wrap my mind around the issues concerning using splines for mixed effect modeling. My question is the following: in the ?gamm? function, generalized additive mixed models can be estimated by including random components. These can be explicitly

I have determined why there are many "WARNING: Duplicate symbol:" messages. *** My apologies for the length *** There is a lot of detail - but I hope the detail will help R - and others - setup correct options for shared libraries. *** As I press send, I have not stopped testing (my final trial here might not even work) - but! *** the message is that shared libraries do not need to have

Dear R-helpers, I'd like to understand how to test the statistical significance of a random effect in gamm. I am using gamm because I want to test a model with an AR(1) error structure, and it is my understanding neither gam nor gamm4 will do the latter. The data set includes nine short interrupted time series (single case designs in education, sometimes called N-of-1 trials in medicine)

On 2015-10-15 15:02, Prof Brian Ripley wrote: > On 15/10/2015 13:32, Michael Felt wrote: >> Hi. >> >> Just wanted to let you know I am getting close to packaging R for AIX in rephrase - would like to be active in keeping R binaries current for AIX. My interest in not in R per se (rather a colleague who has a project that uses R, so I hope to assist him, and others like him).

Dear useRs, I have used using the excellent mgcv package (version 1.7-12) to create a generalized additive model (gam) including random effects - represented with s(...,bs="re") - on the basis of dialect data. My model contains two random-effect factors (Word and Key - the latter representing a speaker) and I have added both random intercepts and various random slopes for these

Thanks for the reproducable example. I can confirm that it fails on my machine using survival 2-37.5, the next soon-to-be-released version, The issue is with NextMethod, and my assumption that the called routine inherited everything from the parent, including the environment chain. A simple test this AM showed me that the assumption is false. It might have been true for Splus. Working this

Readers, I have been reading 'the r book' by Crawley and think that the generalised additive model is appropriate for this problem. The package 'gam' was installed using the command (as root) install.package("gam") ... library(gam) > library(gam) Loading required package: splines Loading required package: akima > library(mgcv) This is mgcv 1.3-25 Attaching

Recently I was using GAM and couldn't help noticing the following incoherence in prediction: > data(gam.data) > data(gam.newdata) > gam.object <- gam(y ~ s(x,6) + z, data=gam.data) > predict(gam.object)[1] 1 0.8017407 > predict(gam.object,data.frame(x=gam.data$x[1],z=gam.data$z[1])) 1 0.1668452 I would expect that using two types of predict arguments

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

I'm fitting a Poisson gam model, say model<-gam(a65tm~as.factor(day.week )+as.factor(week)+offset(log(pop65))+s(time,k=10,bs="cr",fx=FALSE,by=NA,m=1),sp=c( 0.001),data=dati1,family=poisson) Currently I've difficulties in obtaining right predictions by using gam.predict function with MGCV package in R version 2.2.1 (see below my syntax).

Hi, I need further help with my GAMs. Most models I test are very obviously non-linear. Yet, to be on the safe side, I report the significance of the smooth (default output of mgcv's summary.gam) and confirm it deviates significantly from linearity. I do the latter by fitting a second model where the same predictor is entered without the s(), and then use anova.gam to compare the