glm

Can you tell me what is returned by glm$residuals and resid(glm) where glm is a quasipoisson object. e.g. How would I create them using glm$y and glm$linear.predictors. glm$residuals n missing unique Mean .05 .10 .25 .50 .75 .90 .95 37715 10042 2174 -0.2574 -2.7538 -2.2661 -1.4480 -0.4381 0.7542 1.9845 2.7749 lowest : -4.243 -3.552 -3.509 -3.481 -3.464 highest: 8.195 8.319 8.592 9.089 9.416 resid(glm) n missing unique Mean .05 .10 .25 37715 0 2048 -2.727e-10 -1.0000 -1.0000 -0.6276 .50 .75 .90 .95 -0.2080 0.4106 1.1766 1.7333 lowest : -1.0000 -0.8415 -0.8350 -0.8333 -0.8288 highest: 7.2491 7

I want to do a regression in R using glm , but is there a way to do it since I get the contrasts error. mydf <- data.frame(Group=c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12), WL=rep(c(1,0),12), New.Runner=c("N","N","N","N","N","N","Y","N","N","N","N","N","N","Y","N","N","N","Y","N","N","N","N","N","Y"), Last.Run=c(1,5,2,6,5,4,NA,3,7,2,4,9,8,NA,3,5,1,NA,6,10,7,9,2,NA)) mod <- glm(formula = WL~New.Runner+Last.Run, family = binomial, data = mydf) #Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) : # contrasts can be applied only to factors with 2 or more levels Using

I did a glm and I just want to extract the standard errors of each coefficient. I saw on the internet the function se.coef() but it doesn't work, it returns "Error: could not find function "se.coef"" . The information you're after is stored in the coefficients object returned by summary() . You can extract it thusly: summary(glm.D93)$coefficients[, 2] #Example from ?glm counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) print(d.AD <- data.frame(treatment, outcome, counts)) glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) #coefficients has the data

In glm in R, the default link functions for the Gamma family are inverse , identity and log . Now for my particular question, I need to use gamma regression with response Y and a modified link function in the form of log(E(Y)-1)) . Thus, I consider modifying some glm -related functions in R. There are several functions that may be relevant, and I am seeking help for anyone who had previous experience in doing this. For example, the functions Gamma is defined as function (link = "inverse") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c(

I have created two generalised linear models as follows: glm1 <-glm(Y ~ X1 + X2 + X3, family=binomial(link=logit)) glm2 <-glm(Y ~ X1 + X2, family=binomial(link=logit)) I then use the anova function: anova(glm2,glm1) but get an error message: "Error in anova.glmlist(c(list(object),dotargs), dispersion = dispersion, : models were not all fitted to the same size of dataset" What does this mean and how can I fix this? I have attach ed the dataset at the start of my code so both models are working off of the same dataset. The main cause of that error is when there are missing values in one or more