[R] partial autocorrelation plots ACF type=p

Dear users, I have two continuous variables which are two different measures taken each year from 1975 to 2005. I want to see if the two variables are correlated but need to take into account the fact that they are a time series. I have been following an example from 'The R Book' where you plot t

[R] negative binomial lmer

Hi I am running an lmer which works fine with family=poisson mixed.model<-lmer(nobees~spray+dist+flwabund+flwdiv+round+(1|field),family="poisson",method="ML",na.action=na.omit) But it is overdispersed. I tried using family=quasipoisson but get no P values. This didnt worry me too much as i thin

[R] na's and continuous variables

categorical not continuous and i was wondering if there is a way to overcome this? Thanks Tonker ps i do use the na.action=na.omit in my model so if possible i would like to not have to delete all the records with na in in these two explanatory factors. -- View this message in context: http://www.na

[R] Grouping Data for Repeated Measure Mixed Model

Hi I am trying to run a mixed effects model taking into account that i sampled in the same locations four times i.e. temporal repeated measures. From what i gathered i need to group my data by my repeated measure - time - and state the structure of my random variables so i tried this: mixedmodel<