similar to: How to produce glm graph

I'm very new to R and modeling but need some help with visualization of glms. I'd like to make a graph of my glms to visualize the different effects of different parameters. I've got a binary response variable (bird sightings) and use binomial glms. The 'main' response variable is a measure of distance to a track and the parameters I'm testing for are vegetation

Dear r heper, How can I change the strip text, for example (16,23] in the following example, to other more informative text such as "high level" on the fly? library(effects) Cowles$ex2 <- cut(Cowles$extraversion,3) mod.cowles <- glm(volunteer ~ sex+neuroticism*ex2,data=Cowles, family=binomial) eff.cowles <- allEffects(mod.cowles) plot(eff.cowles,

I have a process where I am creating a effects plot similar to the cowles effect example. I would like to add the point estimates to the effects plot, can someone show me the correct syntax. I have included the "R" effects example, so you can show me the correct syntax. Thanks mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial)

Hello, I am not having success in a simple task. Using the effects package, I would like to add reference lines at probability values of 0.1 – 0.6 on a plot of the effects. The plot command works, but following up with an abline command produces the message “plot .new has not been called yet”, and of course the reference lines were not added. Looking through past R help lists, there was a

Dear members, here is my trouble: My data consists of counts of trapped insects in different attractive traps. I usually use GLMs with a poisson error distribution to find out the differences between my traitments (and to look at other factor effects). But for some dataset where one traitment contains only zeros, GLM with poisson family fail to find any difference between this particular traitment

Dear R-helpers, Im trying to fit a Log-linear model on a dataset with bird counts from 60 sites over 14 years for 12 months each (factors for the models). One of the aims is to predict the missing values in this dataset with model predictions. Ive first tried to work with GLM's, that worked fine except for models with one or more interaction-terms. The GLMs run, run .. for hours. So I

Dear list, Can anyone tell me how to obtain the rank of a sparse Matrix, for example from package Matrix (class dgCMatrix)? Here is an example of QR decomposition of a sparse matrix (from the sparseQR class help). library(Matrix) data(KNex) mm <- KNex$mm str(mmQR <- qr(mm)) Similarly, using the functions/classes from the relatively new MatrixModels package: library(MatrixModels)

Dear All, I am analysing a dataset on levels of herbivory in seedlings in an experimental setup in a rainforest. I have seven classes/categories of seedling damage/herbivory that I want to analyse, modelling each separately. There are twenty maternal trees, with eight groups of seedlings around each. Each tree has a TreeID, which I use as the random effect (blocking factor). There are two

Hello, I have a question about the add1() function and quasilikelihoods for GLMs. I am fitting quasi-Poisson models using glm(, family = quasipoisson). Technically, with the quasilikelihood approach the deviance does not have the interpretation as a likelihood-based measure of sample information. Functions such as stepAIC() cannot be used. The function add1() returns the change in the scaled

I've been poking around with GLMs (on which I am *not* an expert) on behalf of a student, particularly binomial (standard logit link) nested models with overdispersion. I have one possible bug to report (but I'm not confident enough to be *sure* it's a bug); one comment on the general inconsistency that seems to afflict the various functions for dealing with overdispersion in GLMs

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

Hi, I've done some searching where others have had trouble with this error (or "warning" actually), but I'm unable to solve my problem. I have a data sheet with 13 columns and 36 rows. Each column has exactly the same number of rows. I've created glms and now want to do cross-validation on 2 of them. Please be gentle-- I'm new to R (and statistics, too, for that

V&R describes binomial GLMs with mortality out of 20 budworms. Is it appropriate to use the same approach with mortality out of numbers as low as 3? I feel reticent to do so with data that is not very continuous. There are one continuous and one categorical independent variables. Would it be more appropriate to treat the response as an ordered factor with four levels? If so, what family

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

Dear all, Is there any function for calculating confidence limits for coefficients in an lm() object? I know of the confint() function in the MASS library working very well on my binomial GLMs and I have tried it (using glm () , family=gaussian) but it gives NAs according to below. Does the confint() function not accept gaussian GLMs? Could there be convergence problems in the GLM? Note the

Hi everyone! I've recently begun to learn R for my job as the IT department suffers from lack of funding for new software. I was talking to the guy in charge of Requisitions and have found out the budget for books is in great shape. So, I'm curious what books people know of that have R examples and are good for: 1.) Uni and Multivariate Time Series Analysis/Forecasting 2.) GLMs (at

I'm trying to join a Samba 3.0.4 (compiled from source on Debian) to an Active Directory as a member server. I believe Kerberos is configured correctly as kinit creates a ticket for the realm. Executables appear to have support for Kerberos and LDAP (smbd -b | grep KRB and grep LDAP) return OK. When I try to join the AD with net ads join -U myadminusername I'm prompted for my

Dear list, I am using glm's to predict count data for a fish species inside and outside a marine reserve for three different methods of monitoring. I run glms and figured out the best model using step function for each methods used. I predicted two values for my fish counts inside and outside the reserve using means of each of the covariates (using predict() ) therefore I have only one value

I have spent a fair amount of time looking for a package that is automated to run glm (binomial) regression models with all possible subsets of my independent variables. Something akin to Lumley's "leaps" package, but can be applied to glms, not just lms; or something similar to Stata's brute force "tryem" function? If anyone can point me in the right direction I

Dear all, I have question regarding GLMs: I have a discrete response variable and a continuous explaining variable. Like this: http://www.myimg.de/?img=example1db0f.jpg I want to use a GLM to investigate. I have to specify the "familiy of the distribution of the response variable" - or, maybe more precise, the "family of the distribution of the residuals of the response