![]() However, this code is from a different project and I don't understand how the m.lim was defined (neither does my colleague). Summary(get.models(dredge_CO2, subset = 1)]) Nullmodel_CO2 <- MuMIn.nullFitRE(CO2.null)ĭredge_CO2 <- dredge(CO2.null, m.lim = c(0,5), rank = "AIC", extra =list(R2 = function(x) ))ĭredge_CO2 # this is the most parsimonious (=best) model, thus the one I will use ![]() + (1|Chamber) + (1|Date), data= data_CO2_noNA, REML = FALSE) I have received the following code from a colleague, where I build a saturated model with all explanatory variables: CO2.null <- lmer(log(CO2_flux) ~ Temperature + Moisture + Inclination I thus run the models separately for "Inclination" and "Distance" (the following code shows Inclination). I cannot include both inclination and distance into the code, and because I only have 4 distances, I can also not include it as a random effect. Chambers are a tool to measure GHG, and we had 16 chambers in 4 distances from a stream (= 4x4 replicates), with differing topography (inclination). My fixed effects are soil temperature, soil moisture and topography, whereas my random effects are chambers and date. ![]() According to my hypotheses, I look at each GHG seperately with mixed models including random effects. I am working on a large dataset looking at effects of soil temperature, soil moisture and topography on greenhouse gases (GHG CO2, CH4 and N2O). ![]()
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