similar to: step function for glm()

After reading a data.frame using, such as a <- read.spss("data.sav") I want to give the column index 'i', or a[i] to a function, which after some calculation, should print out the results to the standard output. I am struggling how to access the data itself, as e.g. sum(a[i]) does not work in this context. In addition I need to know the name of the variable within the

I am using stepAIC for stepwise regression modeling. Is there a way to change the entry and exit alpha levels for the stepwise regression using stepAIC ? Many thanks, Berthold Berthold Stegemann Bakken Research Center Maastricht The Netherlands [[alternative HTML version deleted]]

Hello, I am using the maps library for plotting maps. To build new geograhical sets such as Europe I use europe = c('Germany','Netherlands',...) Is there a list of county codes used by the map function. Thanks, Berthold -- Berthold Stegemann Brunssumstrasse 21a 52074 Aachen Germany Tel: +49 241 401 8909 Mobile: +31 6 2041 4824

In an experimental setup we obtain z-data samples at equidistant grid points. The surf.gls (Kriging) algorithm produces an error under this circumstance when performing the Choleski decomposition. A workaround is to dither the grid coordinates using (x <- rnorm(length(x)) ; y<- rnowm(length(y))). Question: Is this an expected behaviour of the surf.gls function ? Regards, Berthold

Is there a statistical test implemented within R to compare the area under two ROC curves ?? Thanks, Berthold -------------- next part -------------- A non-text attachment was scrubbed... Name: berthold.kramm.vcf Type: text/x-vcard Size: 214 bytes Desc: Card for Dr. Berthold Kramm Url : https://stat.ethz.ch/pipermail/r-help/attachments/20011120/dc0f7bf1/berthold.kramm.vcf

Hello, I estimaded two logit models via glm(). A null model (called glm00) and "full" model with all accessible covariates and interactions between them (glm1). Then I tried to get even better model by step procedure. I tried the following code: > step(glm00, scope=formula(glm1), method="both") and another one: > step(glm00, scope=formula(glm1),

Hello! I like to PXE boot the 'Dell 32 Bit Diagnostics' tool. I can create a harddisk image with the linux download package. The README of the Package gives an example how to create an memdisk entry for the pxelinux menu system: <quote> Create a Bootable HDD Image File Use of the resulting image to perform a Preboot Execution Environment (PXE) boot into the diagnostics

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

Hi, Suppose a binomial GLM with both continuous as well as categorical predictors (sometimes referred to as GLM-ANCOVA, if I remember correctly). For the categorical predictors = indicator variables, is then there a suggested minimum frequency of each level ? Would such a rule/ recommendation be dependent on the y-side too ? Example: N is quite large, a bit > 100. Observed however are

Hi I am using R version 2.7.2. on a windows XP OS and have a question concerning an analysis of covariance with count data I am trying to do, I will give details of a scaled down version of the analysis (as I have more covariates and need to take account of over-dispersion etc etc) but as I am sure it is only a simple problem but I just can't see how to fix it. I have a data set with count

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

Hi: I am using step.glm() as follows: form1 <- as.formula(haspdata ~ 1) lg.mod1 <- glm ( formula=form1, data=st.mtx, family=binomial , na.action=na.omit ) upper <- as.formula( haspdata ~ ( c5+childnm+educ1+incpov1+marital+msa+racekid+racemom+sex+shotcard )^2) lower <- as.formula(haspdata~1) lst <- list( upper=upper , lower=lower ) form1a.step <- step(lg.mod1, scope=lst

Hello, I am currently working with the betareg package, which allows the fitting of a variable dispersion beta regression model (Simas et al. 2010, Computational Statistics & Data Analysis). I was wondering whether there is any package in R that allows me to fit variable dispersion parameters in the standard logistic regression model, that is to make the dispersion parameter contingent upon

Hello I am running a project but I encounter a problem . I would be happy to receive help : problem: I have a binary dependent variable and some covariates logit(y)=a+bx+cz . I want to estimate the score vectors and their covariance by the usage of logit function and so glm in R .The vlaue of one of the coefficient ( like b) is known previously and I want to extract a and c and covariance

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

Hi everyone, I'm running the following command: > step(mydata.glm,directions="both",trace=T) which returns the model with the lowest AIC. Now I'd like to get the list of all the models (or at least the formulas) that were used in between. I've noticed the "keep" option, but couldnt find any help on how to use it. Would anyone here be able to help me with

Hi volks, i have a question about the step() fkt. Is there a possibility to save the last model generated from this method. I have a loop and so i generate 100 different models with the step fkt and i want to know which model is the most common. CODE: ... missStep -> numeric(100) for (j in 1:100) { trainindex <- sample(c(1:462),300) train <- data[trainindex,] test <-

Dear R-helpers, I'm considering two methods of selecting a poisson regression model within R: 1. Using the step() function (stats package) to find the best model by a stepwise algorithm and AIC 2. Using the bic.glm() function (BMA package) to find the best model by Bayesian Model Averaging and BIC Are these both reasonable methods for model selection or is one clearly more appropriate than

R 2.8.1 windows XP I am getting an error message that I don't understand when I try to run GLM. The error only occurs when I have all independent variables in the model. When I drop one independent variable, the model runs fine. Can anyone help me understand what the error means and how I can correct it? Thank you, John > fit11<-glm(AAMTCARE~BMI+BMIsq+SEX+jPHI+jMEDICAID+factor(AgeCat)+

In species distribution modeling where one uses a large sample of background points to capture background variation in presence\pseudo-absence or use\available models (0\1 response) it is frequently recommended that one weight the data so the sum of the absence weights is equal to the sum of presence weights so that the model isn?t swamped by an overwhelming and arbitrary number of background