search for: lot2

> d = data.frame(gender=rep(c('f','m'), 5), pos=rep(c('worker', 'manager', 'speaker', 'sales', 'investor'), 2), lot1=rnorm(10), lot2=rnorm(10)) > d gender pos lot1 lot2 1 f worker 1.1035316 0.8710510 2 m manager -0.4824027 -0.2595865 3 f speaker 0.8933589 -0.5966119 4 m sales 0.4489920 0.4971199 5 f investor 0.9246900 -0.7531117 6 m worker 0.2777642 -0.33...

Hi, I have below GLM fit clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100), lot1 = c(118,58,42,35,27,25,21,19,18), lot2 = c(69,35,26,21,18,16,13,12,12)) summary(glm(lot1 ~ log(u), data = clotting, family = gaussian)) Is there any direct function to extract estimate of Error standard deviation?

...axes at the parameter estimate. I am not sure where I find those likelihood values? I may just be misinterpreting the theory behind this. Thanks for any help you can give. Max clotting <- data.frame( + u = c(5,10,15,20,30,40,60,80,100), + lot1 = c(118,58,42,35,27,25,21,19,18), + lot2 = c(69,35,26,21,18,16,13,12,12)) glm(lot2 ~ log(u), data=clotting, family=Gamma) prof<-profile(glm2) plot(prof) [[alternative HTML version deleted]]

...his). Cheers, Bert On Tue, Dec 24, 2024 at 5:45?AM Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > > Hi, > > I have below GLM fit > > clotting <- data.frame( > u = c(5,10,15,20,30,40,60,80,100), > lot1 = c(118,58,42,35,27,25,21,19,18), > lot2 = c(69,35,26,21,18,16,13,12,12)) > summary(glm(lot1 ~ log(u), data = clotting, family = gaussian)) > > Is there any direct function to extract estimate of Error standard deviation? > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSU...

vcov(). ? On Tue, Dec 24, 2024, 8:45 AM Christofer Bogaso <bogaso.christofer at gmail.com> wrote: > Hi, > > I have below GLM fit > > clotting <- data.frame( > u = c(5,10,15,20,30,40,60,80,100), > lot1 = c(118,58,42,35,27,25,21,19,18), > lot2 = c(69,35,26,21,18,16,13,12,12)) > summary(glm(lot1 ~ log(u), data = clotting, family = gaussian)) > > Is there any direct function to extract estimate of Error standard > deviation? > > ______________________________________________ > R-help at r-project.org mailing list -- To...

...4, 2024, 8:45 AM Christofer Bogaso <bogaso.christofer at gmail.com> wrote: >> >> Hi, >> >> I have below GLM fit >> >> clotting <- data.frame( >> u = c(5,10,15,20,30,40,60,80,100), >> lot1 = c(118,58,42,35,27,25,21,19,18), >> lot2 = c(69,35,26,21,18,16,13,12,12)) >> summary(glm(lot1 ~ log(u), data = clotting, family = gaussian)) >> >> Is there any direct function to extract estimate of Error standard deviation? >> >> ______________________________________________ >> R-help at r-project.org...

...63 ... - attr(*, "names")= chr [1:9] "1" "2" "3" "4" ... I got from: # A Gamma example, from McCullagh & Nelder (1989, pp. 300-2) clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100), lot1 = c(118,58,42,35,27,25,21,19,18), lot2 = c(69,35,26,21,18,16,13,12,12)) model <- glm(lot1 ~ log(u), data=clotting, family=Gamma) res <- predict(model, clotting) I want to transfer the probabilities "0.00814 0.01877 0.025 0.02941 0.03563 ..." to a separate vector, how do I do this?

....christofer at gmail.com> wrote: > >> > >> Hi, > >> > >> I have below GLM fit > >> > >> clotting <- data.frame( > >> u = c(5,10,15,20,30,40,60,80,100), > >> lot1 = c(118,58,42,35,27,25,21,19,18), > >> lot2 = c(69,35,26,21,18,16,13,12,12)) > >> summary(glm(lot1 ~ log(u), data = clotting, family = gaussian)) > >> > >> Is there any direct function to extract estimate of Error standard deviation? > >> > >> ______________________________________________ > &g...

...quot;glm", but I could not get the "optim" function to work. Would you please point out where I did wrong? Thanks a lot. The following is the code: # Step 1: fit the glm clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100), lot1 = c(118,58,42,35,27,25,21,19,18), lot2 = c(69,35,26,21,18,16,13,12,12)) fit1 <- glm(lot1 ~ log(u), data=clotting, family=Gamma) # Step 2: use optim # define loglikelihood function to be maximized over # theta is a vector of three parameters: intercept, cofficient for log(u) and dispersion parameter loglik <- function(theta,data){...