similar to: F tests for glms with binomial error

Displaying 20 results from an estimated 110 matches similar to: "F tests for glms with binomial error"

2003 Apr 04
2
Bug in %in% (match)
Hi, Am I hitting some limit in match? Consider the following example: > tst<-seq(100,125,by=.2)%in%seq(0,800,by=.1) > sum(tst) [1] 76 > seq(100,125,by=.2) [1] 100.0 100.2 100.4 100.6 100.8 101.0 101.2 101.4 101.6 101.8 102.0 102.2 [13] 102.4 102.6 102.8 103.0 103.2 103.4 103.6 103.8 104.0 104.2 104.4 104.6 [25] 104.8 105.0 105.2 105.4 105.6 105.8 106.0 106.2 106.4 106.6 106.8
2003 Apr 04
2
Bug in %in% (match)
Hi, Am I hitting some limit in match? Consider the following example: > tst<-seq(100,125,by=.2)%in%seq(0,800,by=.1) > sum(tst) [1] 76 > seq(100,125,by=.2) [1] 100.0 100.2 100.4 100.6 100.8 101.0 101.2 101.4 101.6 101.8 102.0 102.2 [13] 102.4 102.6 102.8 103.0 103.2 103.4 103.6 103.8 104.0 104.2 104.4 104.6 [25] 104.8 105.0 105.2 105.4 105.6 105.8 106.0 106.2 106.4 106.6 106.8
2011 Nov 15
1
averaging between rows with repeated data
*The situation (or an example at least!)* example<-data.frame(rep(letters[1:10])) colnames(example)[1]<-("Letters") example$numb1<-rnorm(10,1,1) example$numb2<-rnorm(10,1,1) example$numb3<-rnorm(10,1,1)
2006 Oct 12
0
Is there a function in R to evaluate the adjusted AIC or other statistc where overdispersion existed in GLMs?
Dear friends, As we all know, the usual model selection criteria(e.g.deviance,AIC...) in GLMs isn't very good for selecting the best model when overdispersion exist, so we need to adjust the corresponding statistic,see(Fitzmaurice,G.M. (1997) Model selection with overdispersed
2009 Apr 04
1
summary for negative binomial GLMs (PR#13640)
Full_Name: Robert Kushler Version: 2.7.2 OS: Windows XP Submission from: (NULL) (69.246.102.98) I believe that the negative binomial family (from MASS) should be added to the list for which dispersion is set to 1.
2003 Jan 12
1
likelihood and score interval estimates for glms
G'day list! I'm thinking about programming likelihood and score intervals for generalized linear models in R based on the paper "On the computation of likelihood ratio and score test based confidence intervals in generalized linear models" by Juha Alho (1992) (Statistics in Medicine, 11, 923-930). Being lazy, I thought that I would ask if anyone else on the list has
2004 Jun 29
1
strucchange-esque inference for glms ?
hello R-world, according to the strucchange package .pdf, "all procedures in this package are concerned with testing or assessing deviations from stability in the classical linear regression model." i'd like to test/assess deviations from stability in the Poisson model. is there a way to modify the strucchange package to suit my purposes, or should i use be using another
2005 Jan 30
0
Testing Poisson GLMs with independent data: what's the Right Thing To Do?
Folks, my question is not R-specific, but I've struck out twice on sci.stat.consult, so I'm turning to the R community. Even if it's a silly question, I expect that someone present will probably tell me so... I have been using multiple Poisson GLMs and similar count-re?gression models to analyse forest songbird abundance data. Many of the spe?cies-level models seem to fit the data
2005 Nov 28
0
glmpath: L1 regularization path for glms
We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the
2005 Nov 28
0
glmpath: L1 regularization path for glms
We have uploaded to CRAN the first version of glmpath, which fits the L1 regularization path for generalized linear models. The lars package fits the entire piecewise-linear L1 regularization path for the lasso. The coefficient paths for L1 regularized glms, however, are not piecewise linear. glmpath uses convex optimization - in particular predictor-corrector methods- to fit the
2006 Jul 08
0
which model (GLMs)is the best?
Dear friends, I used R to analyze my data with the models of generalized linear models, and found three models were relatively good, but i can't decide which is the best,how should i do ? *Model1:* glm(formula = snail ~ grass + gheight + humidity + altitude + soiltem + airtem + grass:altitude, *family = Gamma(link = inverse*), data = model, na.action = na.exclude, control =
2007 Sep 22
0
How to explain the meaning of mu in the variance function of GLMs?
Dear R friends, When fitting GLMs in R, we may need to specify the variance function to do our analysis. I had thought it's the mean value, but it seems not. Could anybody expain the correct meaning of *mu* in the variance function of GLMs? The following content is from the R-hlep. variance for all families other than quasi, the variance function is determined by the family. The quasi
2009 Aug 26
2
GLMs
Hi, I am starting to work with R. I need to performe a General linear model and a Generalized mixed model, what are the package I have to use for? what is the difference between them? thanks letizia _________________________________________________________________ [[alternative HTML version deleted]]
2010 Jun 03
1
compare results of glms
dear list! i have run several glm analysises to estimate a mean rate of dung decay for independent trials. i would like to compare these results statistically but can't find any solution. the glm calls are: dung.glm1<-glm(STATE~DAYS, data=o_cov, family="binomial(link="logit")) dung.glm2<-glm(STATE~DAYS, data=o_cov_T12, family="binomial(link="logit")) as
2005 Nov 04
1
Plotting Factorial GLMs
Hello all, I'm attempting to plot the functions from a generalized linear model while iterating over multiple levels of a factor in the model. In other words, I have a data set Block, Treatment.Level, Response.Level So, the glm and code to plot should be logit.reg<-glm(formula = Response.Level ~ Treatment.Level + Block, family=quasibinomial(link="logit"))) plot(
2006 Oct 12
2
how to get the variance-covariance matrix/information of alpha and beta after fitting a 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"
2010 Nov 20
1
How to produce a graph of glms in R?
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
2004 Jan 14
2
Binomial glms with very small numbers
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
2001 Mar 21
2
LR-based CIs for GLMs
We are using glm() to models to counts of deaths due to rare causes using a log link and Poisson error distribution, with population as the offset. Approximate confidence intervals for the parameter estimates are easy to calculate using a standard normal deviate, but obviously when the counts of deaths are small (which is why we are using Poisson regression), these intervals are very approximate
2005 Apr 05
2
GLMs: Negative Binomial family in R?
Greetings R Users! I have a data set of count responses for which I have made repeated observations on the experimental units (stream reaches) over two air photo dates, hence the mixed effect. I have been using Dr. Jim Lindsey's GLMM function found in his "repeated" measures package with the "poisson" family. My problem though is that I don't think the poisson