search for: ordglm

My attempt to test a model using ordglm code is running into problems, and I thought if you have a moment you might illucidate the situation. Here is the data: http://www.people.fas.harvard.edu/~corr/6.4.05.RData Here is the code: # I coerce tcn8 matrix data to a vector, because ordglm will not accept matrix data. y<-as.vector(tcn...

I am new to the R. I managed to download and install and got some basic summary work done. I need to do ORDERED LOGIT model. I searched web-site and mail archives and I think that I need to use "ordglm" function. However, I am not sure what package has it. I tried several (like RMUTIL, ORDINAL, REPEATED) etc. but no avail. Any help is useful. I still haven't subscribed to the list so please respond to my e-mail. Thanks in advance. Kamal Desai -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-....

I have experimented with the cheese data example from McCullagh&Nelder, page 175. With a proportional odds model they obtain a residual deviance of 20.31. Estimating the same model with polr(MASS) gives a residual deviance of 762.11 !, while using ordglm(gnlm) gives a deviance of 523.94. Can anybody explain these differences? The data frame with the data are: > cheese Cheese N Response nResponse 1 A 1 III 2 2 A 7 IV 3 3 A 8 V 4 4 A 8 VI 5 5 A 19...

Dear Peter. I guess you know that Jim Lindseys code include nordr and ordglm in library gnlm - I attach the htmls which do various linear and nonlinear ordinal regressions - exemplified with just the data mentioned, McCullagh (1980) JRSS B42, 109-142. I had it work very fine. -----Oprindelig meddelelse----- Fra: Peter Malewski <p.malewski at tu-bs.de> Til: Troels Ri...

...1|2 2|3 -1.1111584 0.7600705 Residual Deviance: 2955.49 AIC: 2961.49 - since as specified the logit for the non-carrier for being in the lower categories is higher ? as also got from Lindsey's library: library(gnlm) y <- c(0:2,0:2) #must start at zero x <- gl(2,3,6) z3 <- ordglm(y~x,weights=freq) z3 Call: ordglm(y ~ x, weights = freq) -Log likelihood 1477.747 AIC 1480.747 Deviance 0.3022138 df 3 Iterations 4 Location coefficients estimate s.e. (Intercept) -1.1120 0.2255 x2 0.6035 0.0...

What is the current state of the R-art for ordered probit models, and more esoterically is there any available R strategy for ordered cauchit models, i.e. ordered multinomial alternatives with a cauchy link function. MCMC is an option, obviously, but for a univariate latent variable model this seems to be overkill... standard mle methods should be preferable. (??) Googling reveals that spss