search for: glms

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...

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" "residuals" "fitted.values" "effects" [5] "R" "rank"...

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...

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

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 data<file:///D:/²©Ê¿¿ÎÌâ/Prediction%20model%20of%20Snails/1997/Model%20Selection%20with%20Overdispersed%20D...

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...

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 v...

...ivory category, I initially fitted a GLMM using the 'glmmPQL' command from the MASS library(after using the 'cbind()' command on the two columns with total number of leaves per seedling and number of leaves attacked by that herbivory category) - and then compared these models to GLMs without the random effect. ## model example1: leaf mines GLMM proportion.leafmines <- cbind(leaves.affected, total.leaves - leaves.affected) leafminesGLMM <- glmmPQL(proportion.leafmines ~ PLATEAU * DISTANCE, random=~1| TreeID, family=binomial(link=logit)) ##AIC(leafminesGLMM) = 474.773...

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 and anyother one (even with...

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 coefficient path at important junctions. These junctions are at the "knots" in |beta| where variables enter/leave the active set; i.e. nonzero/zero values....

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 pretty well. My next task is to validate/verify/test these models using an independen?t dataset collected for this purpose (no, really!) It seems obvious that I sh...

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 coefficient path at important junctions. These junctions are at the "knots" in |beta| where variables enter/leave the active set; i.e. nonzero/zero values....

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]]

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

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

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 =

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.

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(

...Similarly, using the functions/classes from the relatively new MatrixModels package: library(MatrixModels) str(trial <- data.frame(counts=c(18,17,15,20,10,20,25,13,12), outcome=gl(3,1,9,labels=LETTERS[1:3]), treatment=gl(3,3,labels=letters[1:3]))) glmS <- glm4(counts ~ 0+outcome + treatment, poisson, trial, verbose = TRUE, sparse = TRUE) str(glmS) str(X <- glmS at pred@X) str(QR <- qr(X)) I'm not very familiar with matrix decomposition, but is the 'p' slot from a sparseQR object the same as $pivot from...

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