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hello list! I'm sorry, I just stumbled over this strange behaviour (at least I am not able to explain the behaviour, therefore I assume it to be a strange behaviour): attach(water) # I know, this is not recommended names(water[3:10]) [1] "temp" "pH" "DO" "BOD" "COD" "no3" "no2" "po4" for (i in

Hello! I am doing an analysis on a questionnaire of hunters taken in 4 different districts of some mysterious foreign country. The aim of the study was to gather info on the factors that determine the hunting success of a peculiarly beautiful bird in that area. All variables are factors, i.e. they are variables such as "Use of Guns - yes / no", "Use of Dogs - yes / no" and

Hi R-users I would like to extract the random effects ("1|SITE", "1+SPECIES|SITE" and "BA|SITE") from this model formula: Full_model <- formula (VAR ~ (1|SITE) + (1+SPECIES|SITE) + (BA|SITE) + HEIGHT + COND + NN_DIST) I tried: terms(Full_model) labels(terms(Full_model)) but I could not distinguish between random and fixed effects. thanks

Hello there! I am still struggling with a binomial response over all categorical variables (some of them with 3 levels, most with 2 levels). After initial struggles with glm's (struggle coming from the data, not the actual analysis) I have decided to prefer contingency tables. I have my data such as: response:

dear list! I KNOW this has been answered a million times before. But, as some might remember from their "freelance" times as a statistic consultant, sometimes you're blinded by the facts. I KNOW I have seen this problem solved, but due to the mental blockade i have been suffering from the last 3 hours, I can neither find the answer on the mailing list nor on google. I wrote a

i'm sorry to bother but... sometimes theres an attachment in the posts on the list, a single file called: Teil 1.2 whys that? i dont want to open it to find out, as i have used computers before ;) therefore i suspect it to be either some security problem OR to be the alternative HTML version that is claimed to have been deleted. any light-shedding would be appreciated!

hello dear list! since we want to do a model analysis and some people would like to see pseudo-R^2 values for different types of glm of a logistic regression, i've decided to write a function that computes either nagelkerkes normed pseudo-R or cox & snells pseudo-R. however, i am not clear as in the decisive step, i need to calculate the log of (maximum likelihood estimates of model

hello dear list! as I am currently helping someone with their statistical analysis of a count survey, I stumbled upon a very basic question upon model optimization: when fitting a model like: model<-lmer(abundance~(x+y+z)^3,family=poisson,...) in which x,y,z are continuous abiotic parameters such as po4 concentration, no2-concentration, which terms / interaction terms would you recommend

hello! i'm dealing with the following: i've collected a factor covariable at irregularly placed sampling points along a line with spatial informations, i.e.: dataset<-c(x-coordinates, y-coordinates, level-of-factor) the factor describes the density of vegetation between 0 (no ground cover) and 5 (almost complete cover). id like to produce a map similar to the ones the akima package

dear list! i have run several glm analysises to estimate a mean rate of dung decay for independent trials. i would like to compare these results statistically but can't find any solution. the glm calls are: dung.glm1<-glm(STATE~DAYS, data=o_cov, family="binomial(link="logit")) dung.glm2<-glm(STATE~DAYS, data=o_cov_T12, family="binomial(link="logit")) as