similar to: tweedie.profile error message

Hello and thanks in advance I am running a glm in R the code is as follows with residual diagnostic code below model4<-glm(Biomass~(Treatment+Time+Site)^2, data=bobB, family=quasi(link="log", variance="mu")) par(mfrow=c(2,2)) plot(model2) to test the effect of grazing exclusion of feral horses for a Phd with following factors: Treatment - 3 levels which are grazed

Sorry, I just posted the email below but realised I did not give a name or details, would it be possible to adjust before posting and send what is below, sorry again, first time user... From: Joanne Lenehan [mailto:jlenehan@une.edu.au] Sent: Tuesday, 1 December 2009 3:51 PM To: 'r-help@r-project.org' Subject: GLM Repeated measures test of assumptions: e.g. test for sphericity e.g.

Dear R-users, I'm trying to model some data using a tweedie GLM approach. My response variable is the number of pupae that are the offspring of a subordinate wasp on a wasp's nest. However, they're not count data- for each nest, I only know the mean number of pupae per subordinate, which is continous. The data also contain a high proportion of zeros. I'm not very experienced at

Dear R users; I am modelling densities of some species of birds, so I have a problem with a great ammount of zeros. I have decided to try GLMs with the tweedie family, but in all the models I have tried I got an NA for the AIC value. Just to check the problem I've compared the a glm using the Gaussian family with the identity link and a glm using the tweedie family with var.power=0 and

Hello all, I have count data with about 36% of observations being zeros. I found in some of the examples of the r-help mail archives that a tweedie family of distributions could be used to fit a model with random effects. Upon installing the tweedie package and attempting to fit the following model: lmer(SUS ~ 1 + (1|

Dear Catarina, I prefer to leave the AIC value as NA for the tweedie GLM family because it takes extra time to compute and is only occasionally wanted. It's easy to compute the AIC yourself using the dtweedie() function of the tweedie package. Best wishes Gordon At 03:05 AM 14/08/2007, Catarina Miranda wrote: >Dear Gordon; > >I have also sent this email to R help mailing list,

Post-hoc test for a zero inflated continuous data set with a tweedie distribution? I have a zero inflated continuous data set of aphid feeding duration on 10+ species of plant. I have fitted a glm model with a tweedie distribution and used anova() function to show that there is significance between the plant species. However, I would now like to perform of post-hoc test, ideally a Tukey-Kramer

Hi there, I've been wanting to fit a GLMM and I'm not completely sure I'm doing things right. As I said in a previous message my response variable is continuous with many zeros, so I was having a hard time finding an appropriate error distribution. I read some previous help mails given to other people advising them to use the Tweedie distribution. I'm still not sure if this

Hi there, I'm using the tweedie distribution package and I cant figure out how to run a model using an identity link. I know I can use a log link by having link.power=0 and I think identity would be link.power=1, but I'm not sure. Furthermore when I try running it with link.power=1 it requires starting values which I cant manage to give appropriately so I'm not sure if its

hi all i have a function that uses two inputs, say xdata and ydata. An example is the following, simple1<-function(xdata,ydata) { ofit<-lm(ydata~xdata) list(ofit) } say i use arbitray number for xdata and ydata such that D = x1 x2 y 1 1 10 2 6 6 3 10 7 x<-D[,1:2] and y<-D[,3] if one uses these inputs and rund the program we get the following: >simple(xdata=x,ydata=y)

Hi there, I don't have much experience with fitting at all and I'd like to get some advice how to use the "weights"-argument with nls correctly. I have created some data with a sigmoidal curve shape. Each y-Value was generated by the mean of three values. A standard deviation was calculated too. Now, I'd like to weight the data points respective to its standard

Dear R-users and experts I want to create to analyse my data which looks like follows:( I have show only 8 variables but original variables much more number >1000) *sub* *ydata* *X1* *X2* *X3* *X4* *X5* *X6* *X7* *X8* 1 12 1 1 1 2 1 1 1 1 2 13 2 2 1 2 2 1 1 1 3 11 1 1 1 2 1 2 1 2 4 12 1 1 2 1 1 2 2 2 5 14 1 2 2 1 1 2 2 2 6 12 2

Hello all, I have been using R for about 3 weeks and I am frustrated by a problem. I have read R in a nutshell, scoured the internet for help but I either am not understanding examples or am missing something completely basic. Here is the problem: I want to plot data that contains dates on the x axis. Then I want to fit a line to the data. I have been unable to do it. This is an example of

Hi! When running the following code I get the message: Error in eval(expr, envir, enclos) : object "A" not found fm <- function(p,ydata, env = parent.frame()) { #fit model y ~ (b0+b1x1+...+bpxp)*exp(g2plus*z2plus) where bi and g2plus are parameters #create design matrix for linear part fo00 <-

I have a set of data in the form x1, y1, z1 x1, y2, z2 ... x1, yN, zN x2, y1, z(N+1) x2, y2, z(N+2) ... x2, yN, z(2N) ...and so on... xM, yN, val(M*N) I have been trying to figure out how to get R to use this data in a persp plot. So far the only thing that I can figure out to do is to break the data file into three different files. The first file contains the x-coordinate data: x1 x2

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

Hi everyone! I have a simple model that i would like to plot with 95% CIs. It is like follows: m1<-lmer(Richness~Grazing+I(Grazing^2)+(1|Plot),family=poisson) By using the effects package I get two plots, one for the linear term and one for the squared term. Q1: Can I get all in one? I.e. with one line for the whole model? Q2: Can I also visualize the random effects? I would be very happy for

Dear R- Community, to learn i reanalysed some data provided and analysed by Zuur et. al. in their book "Mixed effect models and Extensions in Ecology with R". When i run the last command i get a warning message i dont understand. Loyn<- read.table(file = "loyn.txt",header = TRUE) Loyn$L.AREA<- log10(Loyn$AREA) fGRAZE <-factor(Loyn$GRAZE) M0<- lm(ABUND~ L.AREA

Airon, I used R2.4.0 on a Windows XP (SP2) (not Chinese :-)) and it still works: > data = read.csv("u:/airon.csv") > xdata = data$Adj..Close > modwt.la8 = modwt(xdata, "la8", n.level=6) > summary(modwt.la8) Length Class Mode d1 1467 -none- numeric d2 1467 -none- numeric d3 1467 -none- numeric d4 1467 -none- numeric d5 1467 -none- numeric d6 1467

Hi all I've been getting some emails from the R package maintainers that I need to update some code in a CRAN packge that uses FORTRAN, to comply with (not so recent) changes. I've been a little busy... I'm having trouble adjusting my code. I hope someone can help. The package was working fine, and a few R functions in my package had lines like this: tmp <- .Fortran(