similar to: simple question on glm

Thanks for your answers Stephen and Ben, I hope I am posting on the correct list now. I managed so far to run the multinomial model with random effect with the following command: MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~ habitat:trait,random=~idh(trait):mesh,family="multinomial12", data=dataA,rcov=~trait:units) (where multiple responses are different species, Habitat

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

Hi, ?In the example you provided, it looks like the dates in Date2 happens first.? So, I changed it a bit.? DataA<- read.table(text=" ID,Status,Date1,Date2 ??? ??? ?????? 1,A,3-Feb-01,15-May-01 ??? ??? 1,B,15-May-01,16-May-01 ??? ??? 1,A,16-May-01,3-Sep-01 ??? ??? ??? ??? ??? 1,B,3-Sep-01,13-Sep-01 ??? ??? ??? ??? ??? 1,C,13-Sep-01,26-Feb-04 ??? ??? ??? ??? ???

Greetings everyone, I'm trying to add a specific table or a specific number of rows (e.g.44) to a table with no success. This is my basic table > head(dataA) year plot spp prop.B DCA1 DCA2 DCA3 DCA4 1 2000 1 a1 0.031079 -0.0776 -0.0009 0.0259 -0.0457 2 2000 1 a2 0.968921 -0.0448 0.1479 -0.1343 0.1670 3 2000 2 a1 0.029218

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),

I just had a manuscript returned with the biggest problem being the analysis. Instead of using principal components in a regression I've been asked to analyze a few variables separately. So that's what I'm doing. I pulled a feather from young birds and we quantified certain aspects of the color of those feathers. Since I often have more than one sample from a nest, I thought I

i am working with two sets of likert scale type (4 distinct values) data: dataA <- rep(1:4, c(3,2,2,4)) dataB <- rep(1:4, c(5,4,3,2)) i can now (bar)plot both of these separately and compare the distributions. plot(table(dataA), type='h') plot(table(dataB), type='h') is there a way to plot both of them in one plot, so that the bars for value "1" (dataA: 3,

R version 1.3.0 OS: SunOS 5.7, but I think the same problem occurs with Windows An incorrect formula seems to be used to calculate the pearson residuals for a generalized linear model with a binomial response. Here is a simple program which gives (a) the pearson residuals calculated directly, (b) the pearson residuals from glm, and (c) the deviance residuals from glm. The first and last

Hello all, I have a data frame dataa: newdate newstate newid newbalance newaccounts 1 31DEC2001 AR 1 1170 61 2 31DEC2001 VA 2 4565 54 3 31DEC2001 WA 3 2726 35 4 31DEC2001 AR 3 2700 35 The following gives me the balance of state AR:

Hi, I got following error in write.table() : > write.table(dataa, file="c:/data1.csv", row.names=F, col.names=T, sep=",") Error in file(file, ifelse(append, "a", "w")) : cannot open the connection In addition: Warning message: In file(file, ifelse(append, "a", "w")) : cannot open file 'c:/data1.csv': Permission denied

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, I try to ask here, because I hope someone will help me understand this problem- I have fittet a glm in R with the results > glm1 <- > glm(log(claims)~log(sum)*as.factor(grp),family=gaussian(link="identity")) > summary(glm1) Call: glm(formula = log(claims) ~ log(sum) * as.factor(grp), family = gaussian(link = "identity")) Deviance Residuals: Min 1Q

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 <-

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

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) :

I would like to merge two dataframes, but i have a condition that needs to used for the merge as well. the rows (observations) in each dataframe are identified by each person's ID and by the date of the observation. Basically I would like it to be merged based on both ID (exact match) and date (a condition where one dataframe's date must be after the other dataframe's date).

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

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms