Re: [R] quantreg::rq.fit.hogg crashing at random

Dear all, I am using the "rq.fit.hogg" function from the "quantreg" package. I have two problems with it. First (less importantly), it gives an error at its default values with error message "Error in if (n2 != length(r)) stop("R and r of incompatible dimension") : argument is of length zero". I

Re: [R] Chi square value of anova(binomialglmnull, binomglmmod, test="Chisq")

Thank you all, This was exactly the sort of help I hoped to get. -- View this message in context: http://r.789695.n4.nabble.com/Chi-square-value-of-anova-binomialglmnull-binomglmmod-test-Chisq-tp4632293p4632568.html Sent from the R help mailing list archive at Nabble.com. _

Re: [R] Chi square value of anova(binomialglmnull, binomglmmod, test="Chisq")

David Winsemius wrote > > This is making me think you really have multiple observation on the > same individuals (and that persons make transitions from one state to > another as a result of the passage of time. That needs a more complex > analysis than "simple" logistic regression. You mig

Re: [R] Chi square value of anova(binomialglmnull, binomglmmod, test="Chisq")

Thank you for your commentaries and suggestions. Site 0 and site 1 are interpretable like events. In fact these data come from a simultaneous observations of individuals in two different sites (so they are independent observations: while one individual is observed in one site it can't be in anoth

Re: [R] Chi square value of anova(binomialglmnull, binomglmmod, test="Chisq")

So sorry, My response variable is "site" (not "gender"!). The selection process was: > str(data) 'data.frame': 1003 obs. of 5 variables: $ site : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... $ sex : Factor w/ 2 levels "0","1": NA NA NA NA 1 NA NA NA NA NA ... $ age : Factor w/ 2

[R] Chi square value of anova(binomialglmnull, binomglmmod, test="Chisq")

Hi all, I have done a backward stepwise selection on a full binomial GLM where the response variable is gender. At the end of the selection I have found one model with only one explanatory variable (cohort, factor variable with 10 levels). I want to test the significance of the variable "cohort"

Re: [R] Can't find the error in a Binomial GLM I am doing, please help

Thank you Peter for showing me the error. I did not realize it. Now I have removed that cohort (there was just one observation!) and checked the numbers for each of the other cohorts. I have re-run the model and now it seems to make much more sense to me. I am going to use one specific cohort, 20

Re: [R] Binomial GLM, chisq.test, or?

Hi Tal, Thanks for replying. (1) I am going to use cohort as a factor and (2) no, there are no strong correlation between "cohort" and the other predictors. I am using a binomial GLM and the lack of significance of "cohort" seems it was due to one of the 11 levels (the base level) of this factor

Re: [R] Can't find the error in a Binomial GLM I am doing, please help

Perhaps I haven't explained it that well as I would have liked to. To me this was an R issue because I didn't understand why the binomial GLM is getting these results and I believed this was something due to the way I am implementing it in R, not to the binomial GLM itself. If I was wrong and this

[R] Can't find the error in a Binomial GLM I am doing, please help

Hi all, I can't find the error in the binomial GLM I have done. I want to use that because there are more than one explanatory variables (all categorical) and a binary response variable. This is how my data set looks like: > str(data) 'data.frame': 1004 obs. of 5 variables: $ site : int 0 0

Re: [R] Binomial GLM, chisq.test, or?

Thanks Tal for answering, Anyway I still have no idea on why the binomial GLM is missing the relationship between the response variable and the explanatory variable "cohort". Is there anyone who might help me to understand this? -- View this message in context: http://r.789695.n4.nabble.com/Bi

[R] Binomial GLM, chisq.test, or?

Hi, I have a data set with 999 observations, for each of them I have data on four variables: site, colony, gender (quite a few NA values), and cohort. This is how the data set looks like: > str(dispersal) 'data.frame': 999 obs. of 4 variables: $ site : Factor w/ 2 levels "1","2": 1 1 1 1 1 1

[R] Global model more parsimonious (minor QAICc)

Hi all, I know this a general question, not specific for any R package, even so I hope someone may give me his/her opinion on this. I have a set of 20 candidate models in a binomial GLM. The global model has 52 estimable parameters and sample size is made of about 1500 observations. The global mo

Re: [R] Resampling with replacement on a binary (0, 1) dataset to get Cis

Thanks. Anyway, it is not homework and I was not told to do that. My question has not been answered yet, I'll try to reformulate it: Does it make (statistical) sense to resample with replacement in this situation to get an estimate of the CIs? In case it does, how could I do it in R? Some further

Re: [R] Resampling with replacement on a binary (0, 1) dataset to get Cis

Thanks. So, suppose for one specific year (first year over 10) the percentage of successes deriving from 100 trials with 38 successes (and 62 failures), its value would be 38/100=0.38. I could calculate its confidence intervals this way: > success<-38 > total<-100 > prop.test(success,total,p=0.5,a

[R] Resampling with replacement on a binary (0, 1) dataset to get Cis

...is it possible to do that? I apologize for something that must be a very trivial question for most of you but, unfortunately, it is not for me. A binary variable is measured, say, 50 times each year during 10 year. My interest is focused on the percentage of 1s with respect to the total if each

Re: [R] binomial GLM quasi separation

#Uwe: I have realized that in the firstly linked post ( http://r.789695.n4.nabble.com/OT-quasi-separation-in-a-logistic-GLM-td875726.html#a3850331 OT-quasi-separation-in-a-logistic-GLM ) I have told something misleading: in fact my independent variables are not log-normally distributed since the

Re: [R] binomial GLM quasi separation

As you suggested I had a further look at the profile by changing default values of stepsize (I tried to modify the others but apparently there was any change). Here they go the scripts I have used: > dati<-read.table("simone.txt",header=T,sep="\t",as.is=T) > glm.sat<-glm(sex~twp+hwp+hcp+hnp,binomi

[R] binomial GLM quasi separation

Hi all, I have run a (glm) analysis where the dependent variable is the gender (family=binomial) and the predictors are percentages. I get a warning saying "fitted probabilities numerically 0 or 1 occurred" that is indicating that quasi-separation or separation is occurring. This makes sense given

Re: [R] OT: (quasi-?) separation in a logistic GLM

I know that this is a quite old post but I am dealing with a similar warning message and, also after reading all the posts here, I am still in doubt with what I should do with my analysis. I have a dataset where the binary response variable is sex, and the predictors are several variables (they ar

Re: [R] comparing mixed binomial model against the same model without random effect

Thank you very much for answering, I have just tried it and these are the results: > random.model<-glmer(sex~hwp+hcp+(1|colony),family=binomial) Mensajes de aviso perdidos glm.fit: fitted probabilities numerically 0 or 1 occurred > no.random.model<-glm(sex~hwp+hcp,family=binomial) Mensajes de a

Re: [R] comparing mixed binomial model against the same model without random effect

Any answer to this? I really need to compare a mixed model with binomial error against the same model without the random effect. I would use anova() but I don't know how to specify both models in order to make them comparable. Thanks for any answer Simone -- View this message in context: http: