similar to: How to explain the meaning of mu in the variance function of GLMs?

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

Dear friends, As we all know, the usual model selection criteria(e.g.deviance,AIC...) in GLMs isn't very good for selecting the best model when overdispersion exist, so we need to adjust the corresponding statistic,see(Fitzmaurice,G.M. (1997) Model selection with overdispersed

Dear all, Following the help from gam(mgcv) help page, i tried to analyze my dataset with all the default arguments. Unfortunately, it can't be run successfully. I list the errors below. #m.gam<-gam(mark~s(x,y)+s(lstday2004)+s(slope)+s(ndvi2004)+s(elevation)+s(disbinary),family=binomial(logit),data=point)

Dear Rusers, I have some data in .csv file like "2004-8-1" and "2004-10-1", and i need to convert them into days from the origin (January 1, 1960). I have tried the function date.mmddyyyy(), but cannot get it. Anybody can show me how to handle the date data? Thanks very much! My dataset like: time 2004-8-1 2004-10-1 2001-9-1 2002-9-1 -- With Kind Regards,

Dear friends, The following is an example to explain my question. I want to get a legend which will show the z-values according to different colors in image() function. x<-sort(runif(10)) #x-coordinates y<-sort(runif(10)) #y-coordinates z<-matrix(runif(100),nrow=10) #attributes values image(x,y,z,col=gray((6:3)/6)) # legend(x,y,legend=z,col=gray((6:3)/6)) #error. the colors should

Dear R users, I hope to explain the concepts of skewness and kurtosis by generating series of distributions with same skewness and different kurtosis or with same kurtosis and different skewness, but it seems that i cannot find the right functions. I have searched the mailing list, but no answers were found. Is it possible to do that in R? Which function could be used? Thanks a lot. --

Dear friends, My dataset is like the following: x y mcpvalue 0.4603578 0.6247629 1.001 0.4603715 0.6247788 1.001 0.4603852 0.6247948 1.001 0.4110561 0.5664841 0.995 The x and y variables are unsorted. I use the function image(x,y,mcpvalue) to generate a plot, but the error is that "increasing 'x' and

Dear friends, In S-PLUS, we can use the following argument, but not in R. mode12 <- gam(score1 ~ lo(latitude) + lo(longitude)) I searched the help in S-PLUS, it says lo() Allows the user to specify a Loess fit in a GAM formula, but i didn't find the correponding function in R. Anybody knows how to do the similar task in R? Thanks very much. -- With Kind Regards, oooO:::::::::

Dear list, I'm trying to mimic the analysis of Wedderburn (1974) as cited by McCullagh and Nelder (1989) on p.328-332. This is the leaf-blotch on barley example, and the data is available in the `faraway' package. Wedderburn suggested using the variance function mu^2(1-mu)^2. This variance function isn't readily available in R's `quasi' family object, but it seems to me

Dear R users, We have some programs for the specific task in our research, but they were very commonly used. We want to make some functions for them, anybody can recommend any good tools to facilitate us to create R functions even without going deep into the theories of R functions ? Any suggestions or help are greatly appreciated. -- With Kind Regards, oooO::::::::: (..):::::::::

Dear all, I have two variables, y and x. It seems that the relationship between them is Piecewise Linear Functions. The cutpoint is 20. That is, when x<20, there is a linear relationship between y and x; while x>=20, there is another different linear relationship between them. How can i specify their relationships in R correctly? # glm(y~I(x<20)+I(x>=20),family = binomial, data =

Dear friends, Anybody has ever met the problem to add a legend to a figure generated by image()? I have three variables,x,y and z. x and y are the coordinates, and z is the third values. we can use image(x, y, z,...) to generate a figure according to the z-values, but the problem is the figure legend. How can the legend be added to a figure generated by image()? Note that filled.contour() can

Dear all, Some functions like 'ROC(Epi)' can be used to perform ROC analyssi, but it needs us to specify the fitting model in the argument. Now i have got the predicted p-values (0,1) for the 0/1 response variable using some other approach, see the following example dataset: id mark predict.pvalue 1 1 0.927 2 0 0.928 3 1 0.928 ..................

Dear friends, I need the get the 2.5% and 97.5% quantile from each row of a matrix, how should i get it? BTW, i can get the min/max value from each row of a matrix, using the following programs, is there an easy function to do it? simmin<-matrix(NA,nrow=47,ncol=1) for (i in 1:47) { simmin[i,]<-min(datas[i,]) } Thanks for your help. -- With Kind Regards, oooO:::::::::

Dear Rusers, When i start R, there always the following work to do first, how should i cancel it? *--- Please select a CRAN mirror for use in this session ---* I don't know why it does so, maybe i have done something unintentionally. Thanks. -- With Kind Regards, oooO::::::::: (..)::::::::: :\.(:::Oooo:: ::\_)::(..):: :::::::)./::: ::::::(_/:::: :::::::::::::

Dear Rusers, I'd like to take the cases of cancer of the larynx in chorley(spatstat) to explain my question. I want to join the points of cancer of the larynx with the disused industrial incinerator to generate lines, and then calculate the angles of these line comparing the horizontal line? How can i get it? It seems to be difficult to get the angles Thanks a lot. -- With Kind

Dear friends, Now, when i use the argument return(x=x,y=y,prob=prob) , R displays the waring message: Warning message: The return value for multiple variables wasn't used in: return(x = x, y = gy, prob = prob) I used the methods of "help.search("return")" and "?return" to get some help, but didn't find info on it. Anybody knows how it should be used

Dear all, I am thinking to fit a multilevel dataset with R. I have found several possible packages for my task, such as glmmPQL(MASS),glmm(repeated),glmer(lme4), et al. I am a little confused by these functions. Could anybody tell me which function/package is the correct one to analyse my dataset? My dataset is as follows: the response variable P is binary variable (the subject is a patient

Dear all, I run the GAMs (generalized additive models) in gam(mgcv) using the following codes. m.gam <-gam(mark~s(x)+s(y)+s(lstday2004)+s(ndvi2004)+s(slope)+s(elevation)+disbinary,family=binomial(logit),data=point) And two repeated warnings appeared. Warnings$B!'(B 1: In gam.fit(G, family = G$family, control = control, gamma = gamma, ... : Algorithm did not converge 2: In gam.fit(G,

Dear friends, My dataset have many zeros, so i must use fisher exact test . Unfortunately, the fisher.test(stats) function fail to do it. Anybody knows how to do the fisher exact test with many zeros in the dataset? My dataset is: a<-matrix(c(0,1,0,0,0,0,1,0,1,0,0,0,0,1,0,1,1,0,2,1,5,1,1,6,4,4,1,17,2,8,5,7,1,1,24,3,6,1,1,3,2,16,7,4,0,2,4,0,17,0,1,0,0,0,1,2),nrow=8,byrow=TRUE) data.frame(a)