similar to: Referencing to an object within a function

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

Ted Harding said: > I can get the estimated RRs from > RRs <- exp(summary(GLM)$coef[,1]) > but do not see how to implement confidence intervals based > on "robust error variances" using the output in GLM. Thanks for the link to the data. Here's my best guess. If you use the following approach, with the HC0 type of robust standard errors in the

This is a question regarding the 'regTermTest' function in the 'survey' package. Imagine Z as a three level factor variable, and code ZB and ZC as the two corresponding dummy variables. X is a continuous variable. In a 'glm' of Y on Z and X, say, how do the two test specifications test.terms = c("ZB:X","ZC:X") # and test.terms = ~ ZB:X + ZC:X in

Problems with predict and lines in plotting binomial glm Dear R-helpers I have found quite a lot of tips on how to work with glm through this mailing list, but still have a problem that I can't solve. I have got a data set of which the x-variable is count data and the y-variable is proportional data, and I want to know what the relationship between the variables are. The data was

Dear all, I am comparing logistic regression models to evaluate if one predictor explains additional variance that is not yet explained by another predictor. As far as I understand Baron and Li describe how to do this, but my question is now: how do I report this in an article? Can anyone recommend a particular article that shows a concrete example of how the results from te following simple

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

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual

Hello, I use a type III anova ("car" package) to analyse an unbalanced data design. I have two factors and I would have the effect of the interaction. I read that the result could be strongly influenced by the contrasts. I am really not an expert and I am not sure to understand indeed about what it is... Consequently, I failed to properly used the fit.contrast function (gregmisc

This is a question about removing the intercept in a binomial glm() model with categorical predictors. V&R (3rd Ed. Ch7) and Chambers & Hastie (1993) were very helpful but I wasn't sure I got all the answers. In a simplistic example suppose I want to explore how disability (3 levels, profound, severe, and mild) affects the dichotomized outcome. The glm1 model (see below) is

Hi all, In a simulation context, I'm applying some my function, "myfun" say, to a list of glm obj, "list.glm": >length(list.glm) #number of samples simulated [1] 1000 >class(list.glm[[324]]) #any component of the list [1] "glm" "lm" >length(list.glm[[290]]$y) #sample size [1] 1000 Because length(list.glm) and the sample size are rather large,

Dear Listers, If I have several glm objects with names glm1, glm2.... and want to apply new data to these objects. Instead of typing "predict(glm1, newdata)..." 100 times, is there way I could do so in a loop? Thank you so much! wensui [[alternative HTML version deleted]]

Dear R-List users, Can anyone explain exactly the difference between Weights options in lm glm and gls? I try the following codes, but the results are different. > lm1 Call: lm(formula = y ~ x) Coefficients: (Intercept) x 0.1183 7.3075 > lm2 Call: lm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.04193 7.30660 > lm3 Call:

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~ ns(dd$age, 4)) pred1 <-

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Estimados amigos, Estoy familiarizándome con los modelos lineales generalizados en R. Estoy interesado en realizar un análisis lig linear y me gustaría saber cuáles son o como extraer los valores correspondientes al chi cuadrado en el análisis para cada grupo y para las interacciones. Desde ya muchas gracias y disculpas si la pregunta es muy básica, adjunto los comandos que estoy utilizando. Si

Dear all, I am running a logistic regression and this is the output: glm(formula = educationUniv ~ brncntr, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max # ???? ????? ?? ???????? -0.8825 -0.7684 -0.7684 1.5044 1.6516 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.06869 0.01155 -92.487 <2e-16 *** brncntrNo

I am getting a repeated error when I try to run a logistic regression in R 2.8.1 >(glm(prop1~x1,data=glm1,family=binomial("logit"),weights=nt1)) Error in model.frame.default(formula = prop1 ~ x1, data = glm1, weights = nt1, : invalid type (list) for variable 'x1' x1 is multistate categorical (3 categories). 2 of the categories have 12 observation, one has 9. Is this what

Hello, does anybody know how to implement a "Did you mean ...?" like Google with act_as_ferret? I think this is a possible way: 1. Generate a keyword-list (this is my difficulty. I don''t know how to build such a list from the index) with no stop-words from the first index. e. g. (car, ship, plant, house) 2. Build a second index from this word-list where we store the word in

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),