Dear list, i have already posted once about this dataset, however now with a different approach. My dataset consists of six sampling dates (several months apart) with 60 sampling stations each (within 100 square meters). Initially, i wondered if i can calculate Tukey contrasts by sampling dates if they are possibly both fixed and random. This time, my approach is fairly basic. I would like to model the influence of some environmental predictors (e.g. pH) on my outcome. I dont think my stations (specified with x,y coordinates) have random intercepts (as they are close to each other), but they likely feature spatial autocorrelation. This time, i treat time as random, and since the sampling dates are months apart, and the sampling grid was always different, i assume there is no temporal autocorrelation or effects of repeated measures. So, i would then fit a model like this: model1 <- lmer(Outcome ~ Var1+Var2+...+(1|sampling date), correlation=corXXXX(1,form=~x+y), data=data, REML=false) (alternatively also as interaction between the fixed effect). Assuming that i have normally distributed outcomes (which i dont), is this a proper approach? Alternatively, i could fit a model for each of the six sampling dates independently, and not use random effects at all. Thank you!
Is my model correct (1 random effect + spatially structured outcome) ?
2 messages · trichter m@ili@g off u@i-breme@@de, Thierry Onkelinx
Dear Tim, lmer() from lme4 cannot handle correlation functions. lme() form nlme can. But there the correlation is only within the most detailed level of the random effects. Observations from different levels (here sampling dates) are assumed to be independent. However they will share the same parameters for the correlation function. Another option would be to fit the model without spatial correlation structure and then make a variogram of the residuals. It might be harder to get a stable variogram with only 60 locations. If the variogram indicates spatial correlation, then you will have to model it. Also provide sensible starting values for the correlation function. The default value for the range is very small, often resulting in a very small fitted range. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be Havenlaan 88 bus 73, 1000 Brussel www.inbo.be /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// 2018-07-22 21:26 GMT+02:00 <trichter at uni-bremen.de>:
Dear list, i have already posted once about this dataset, however now with a different approach. My dataset consists of six sampling dates (several months apart) with 60 sampling stations each (within 100 square meters). Initially, i wondered if i can calculate Tukey contrasts by sampling dates if they are possibly both fixed and random. This time, my approach is fairly basic. I would like to model the influence of some environmental predictors (e.g. pH) on my outcome. I dont think my stations (specified with x,y coordinates) have random intercepts (as they are close to each other), but they likely feature spatial autocorrelation. This time, i treat time as random, and since the sampling dates are months apart, and the sampling grid was always different, i assume there is no temporal autocorrelation or effects of repeated measures. So, i would then fit a model like this: model1 <- lmer(Outcome ~ Var1+Var2+...+(1|sampling date), correlation=corXXXX(1,form=~x+y), data=data, REML=false) (alternatively also as interaction between the fixed effect). Assuming that i have normally distributed outcomes (which i dont), is this a proper approach? Alternatively, i could fit a model for each of the six sampling dates independently, and not use random effects at all. Thank you!
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