similar to: R 2.7.2 is released

Dear R Help, I have noticed some inconsistent behaviour of add1 and drop1 with a weighted linear model, which affects the interpretation of the results. I have these data to fit with a linear model, I want to weight them by the relative size of the geographical areas they represent. _________________________________________________________________________________________ > example

Dear List, recently tried to reproduce the results of some custom model selection function after updating R, which unfortunately failed. However, I ultimately found the issue to be that testing with pchisq() in drop1() seems to have changed. In the below example, earlier versions (e.g. R 2.4.1) produce a missing P-value for the variable x, while newer versions (e.g. R 2.7.1) produce 0 (2.2e-16).

I'd like to fit and explore a collection of hierarchical loglinear models that might range from the independence model, ~ 1 + 2 + 3 + 4 to the saturated model, ~ 1 * 2 * 3 * 4 I can use add1 starting with a baseline model or drop1 starting with the saturated model, but I can't see how to get the model formulas or terms in each model as a *list* that I can work with further. Consider

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

If you have the following dataframe: > x <- data.frame(x=I(rep('"', 3))) then > as.matrix(x)[1,1] == x[1,1] [1] FALSE which seems to be WRONG to me. Also I don't understand > x x 1 \\\" 2 \\\" 3 \\\" Details ======= As expected > unclass(x) $x [1] "\"" "\"" "\"" attr(,"class") [1]

Hi, I'm trying to build a hierarchical logistic regression model with lme4 package, but I have a problem on selecting the variables to include in this model. In a simple logistic regression, using Forward selection, i use a likelihood ratio test to check which variables i should include in the model, using the function add1(). The problem is that this function doesn't work with the

> Hello, > > I have a question about a discrepancy between the > reported F statistics using anova() and add1() from > adding an additional term to form nested models. > > I found and old posting related to anova() and > drop1() regarding a glm with a dispersion parameter. > > The posting is very old (May 2000, R 1.1.0). > The old posting is located here. >

David, Thanks. The reason add1.lm (and drop1.lm) do not support offsets is that lm did not when they were written, and the person who added offsets to lm did not change them. (I do wish they had not added an offset arg and just used the formula as in S's glm.) That is easy to add. For the other point, some care is needed if 'x' is supplied and the upper scope reduces the number

Hello all, I have a question on manova in R: I'm using the function "manova()" from the stats package. Is there anything like a stepwise (backward or forward) manova in R (like there is for regression and anova). When I enter: step(Model1, data=Mydata) R returns the message: Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, : no 'drop1'

Dear all, I am fitting an nonlinear glm() using optim() by first minimising glm(resp~ var1 + var2, family=binomial, data=data)$deviance where var1= exp(-a1*dist1), and var2= exp(-a2*dist2), where a1 and a2 are parameters and dist1 and dist2 are independent variables. Next, I calculate the value of var1 (and var2) by plugging in the value of al1 (and al2) that minimises deviance, and fit

Is there a way to automate fitting and assessing loglinear models for several nominal variables . . . something akin to step or drop1 or add1 for linear or logistic regression? Thanks. --Chris -- Christopher W. Ryan, MD SUNY Upstate Medical University Clinical Campus at Binghamton 40 Arch Street, Johnson City, NY 13790 cryanatbinghamtondotedu "If you want to build a ship, don't drum

Dear all, I have been trying to investigate the behaviour of different weights in weighted regression for a dataset with lots of missing data. As a start I simulated some data using the following: library(MASS) N <- 200 sigma <- matrix(c(1, .5, .5, 1), nrow = 2) sim.set <- as.data.frame(mvrnorm(N, c(0, 0), sigma)) colnames(sim.set) <- c('x1', 'x2') # x1 & x2 are

Howdy! I'm a clinical psychologist desperately trying to get rid of SPSS. I just discovered R and like it quite a lot. The main reason why we're still using SPSS is the hierarchical regression where you enter bundles of variables into a linear model and get an R-sqare increase tested with an F-test. I already found add1 and drop1 but would rather need addn and dropn. Is there

Dear R-Users, I have a question to the GLMM via the lme4 package. I have 160 nest-boxes which are placed at 8 different localities. "Count" is the number of animals which were found inside the boxes during the observation time. The independent variables are factors which are supposed to influence the occurence of the animal. 1. Is the formula right like this? Here are my outcomes of

Full_Name: Madeleine Yeh Version: 1.9.1 OS: AIX 5.2 Submission from: (NULL) (151.121.225.1) After compiling R-1.9.1 on AIX 5.2 using the IBM cc compiler, I ran the checks. One of them failed. Here is the output from running the check solo. root@svweb:/fsapps/test/build/R/1.9.1/R-1.9.1/tests/Examples: ># ../../bin/R --vanilla < stats-Ex.R R : Copyright 2004, The R

I'm using the package geepack to fit GEE models. Does anyone know of methods for add1 and drop1 for a 'geeglm' model object, or perhaps a method for extractAIC based on the QIC of Pan 2001? I see there has been some mention of this on R-help a few years ago (RSiteSearch("QIC")). The package does provide an anova method for its model objects, and update() seems to work:

>?step .... 'step' uses 'add1' and 'drop1' repeatedly; it will work for any method for which they work, and that is determined by having a valid method for 'extractAIC'. When the additive constant can be chosen so that AIC is equal to Mallows' Cp, this is done and the tables are labelled appropriately. so my question is :what constant

I am hoping to get some advise on the following: I am looking for an automatic variable selection procedure to reduce the number of potential predictor variables (~ 50) in a multiple regression model. I would be interested to use the forward stepwise regression using the partial F test. I have looked into possible R-functions but could not find this particular approach. There is a function

I've rolled up R-2.8.0.tar.gz a short while ago. This is a development release which contains a number of new features. Also, a number of mostly minor bugs have been fixed. See the full list of changes below. You can get it (later today) from http://cran.r-project.org/src/base/R-2/R-2.8.0.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. In case of impatience, try

I've rolled up R-2.8.0.tar.gz a short while ago. This is a development release which contains a number of new features. Also, a number of mostly minor bugs have been fixed. See the full list of changes below. You can get it (later today) from http://cran.r-project.org/src/base/R-2/R-2.8.0.tar.gz or wait for it to be mirrored at a CRAN site nearer to you. In case of impatience, try