similar to: plot.gam for glm objects.

Hi all, I'm having trouble connecting to an oracle database using RODBC under winXP. Unfortunately I can't really send a reproducable error as the initial call to odbcConnect seems to hangs R and I have to kill the session. I have been using RODBC to sucessfully connect to an MS Access DB that has tables linked through to the oracle database in question and that seems to work OK but it

Is there a way to add residuals to plots produced by plot.gam in the mgcv package? I'm looking for something like what you get using resid=T in Splus plot.gam. Thanks in advance Toby -----Original Message----- From: Simon Wood [mailto:snw at mcs.st-and.ac.uk] Sent: 23 January, 2002 8:14 PM To: Toby.Patterson at csiro.au Cc: r-help at stat.math.ethz.ch Subject: Re: [R] multiple surfaces in

Full_Name: Toby Patterson Version: 1.6.0 OS: winXP Submission from: (NULL) (140.79.2.3) I seem to be able to cause a predictable crash (i.e. the application terminates abnormally) in R using the library lattice. If I have x<-cbind(1:10,c(rep(1,7),rep(2,3)),rnorm(10)) library(lattice) xyplot(x[,1]~x[,3]|x[,2]) # works oK but reversing the ylim entries i.e.

Hi all (and particularly Deepayan), A while back Deepayan helped me with the query in the text below (thanks again). Specifically it was about changing the way that dates plotted on the axes of lattice plots. While this works using xyplot, everything comes apart when I use levelplot. The axis labels on the date axis are shown as the integer representation of the date (number of seconds since

All, Does anyone know if there is an option I can set to turn screen-buffered output on and off with the win32 rgui? (Apart from the point and click method). I am running some simulations where it is useful to watch output but it gets mildly tiresome having to manually switch things on and off via the gui. Thanks Toby. > version _ platform i386-pc-mingw32 arch

Dear R users, Using the library(mgcv) and running R under MacOSX, I have fitted a generalised additive model with binomial errors in order to check the linearity of two continuous variables ap2mm and diffdaysm in a glm: > mymodel.gam <- gam(diedhos~ s(ap2mm) + Dweekm + s(diffdaysm) + Dweekm:diffdaysm + ap2mm:Dweekm, binomial) I would like postscript gam plots for the two smoothed

All, When I try and compile a shared library (on WinXP) I get the following error: E:\data\proj>R CMD SHLIB toy_dll.c Makevars:1: *** missing separator. Stop. Has someone else had this error and fixed it? This code compiles and works fine on Linux (fedora core 2). Everything was working fine under R1.8.1 but I've broken something when I upgraded to R1.9.1. So I am assuming

Question: I am trying to impliment a function in R that we use quite regularly in Splus, and it fails due to a lack of the "terms.inner" function in R. The substitute is? Part question and part soapbox: Why remove terms.inner from R? It's little used, but rather innocuous. Mostly soapbox: I figured it was no big deal, as I originally discovered the use of terms.inner from

Hello, I have used the functions agnes and cutree to cluster my data (4977 objects x 22 variables) into 8 clusters. I would like to refine the solution using a k-means or similar algorithm, setting the initial cluster centres as the group means from agnes. However my data matrix has NA's in it and the function kmeans does not appear to accept this? > dim(centres) [1] 8 22 > dim(data)

How can I obtain the values of confidence intervals from gam anf glm objects? Thanks in advance -- David Nogu?s Bravo Functional Ecology and Biodiversity Department Pyrenean Institute of Ecology Spanish Research Council Av. Monta?ana 1005 Zaragoza - CP 50059 976716030 - 976716019 (fax)

I have an additive model of the following form : zmdlfit <- lm(z~ns(x,df=6)+ns(y,df=6)) I can get the fitted values and plot them against z easily enough, but I also want to both obtain and plot the two additive components (the estimates of the two additive terms on the RHS) I've been looking at manuals and searching on the internet and searching the archives, but I'm apparently

Hi, I am fitting two models, a generalized linear model and a generalized additive model, to the same data. The R-Help tells that "A generalized additive model (GAM) is a generalized linear model (GLM) in which the linear predictor is given by a user specified sum of smooth functions of the covariates plus a conventional parametric component of the linear predictor." I am fitting the GAM

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

Hi, I have a variable (current speed direction) which is circular (0=360 degrees), and I'd like my GLM to include the variable as a circular variable. Can I do this? And what is the code? I'm actually doing a GLM-GEE using the 'geepack' package, so want to use it in that, but also interested in whether it can also be used in GLMs and GAMs (I use the 'mgcv' package for

Hello I'm analyzing a dichotomous dependent variable (dv) with more than 100 measurements (within-subjects variable: hours24) per subject and more than 100 subjects. The high number of measurements allows me to model more complex temporal trends. I would like to compare different models using GLM, GLMM, GAM and GAMM, basically do demonstrate the added value of GAMs/GAMMs relative to

Hi all, I am currently trying to compare different plant occurrence prediction maps generated in R and exported into GRASS. One of these maps was generated from a glm fitted to some data, and subsequently applying this glm model to a wider region using predict.glm. The outcome here was a probability of occurrence. The second map I generated using a gam (mgcv), however, this map seems to have

Hi, I'm running gam (mgcv version 1.3-29) and glm (logit) (stats R 2.61) on the same models/data, and I got error messages for the gam() model and warnings for the glm() model. R-help suggested that the glm() warning messages are due to the model perfectly predicting binary output. Perhaps the model overfits the data? I inspected my data and it was not immediately obvious to me (though I

Hello, I'm interested in correcting for and measuring unobserved heterogeneity ("missing variables") using R. In particular, I'm searching for a simple way to measure the amount of unobserved heterogeneity remaining in a series of increasingly complex models (adding additional variables to each new model) on the same data. I have a static database of 400,000 or

Dear R-friends, How do you test the goodness of prediction of a model, when you predict on a set of data DIFFERENT from the training set? I explain myself: you train your model M (e.g. glm,gam,regression tree, brt) on a set of data A with a response variable Y. You then predict the value of that same response variable Y on a different set of data B (e.g. predict.glm, predict.gam and so on).

Does anyone have, or has anyone ever considered making, a version of 'termplot' that allows the user to specify that all plots should have the same y-limits? This seems a natural thing to ask for, as the plots share a y-scale. If you don't have the same y-axes you can easily misread the comparative contributions of the different components. Notes: the current version of termplot