Hello all, If someone could take a little time to help me then I would be very grateful. I studied piping plovers last summer. I watched each chick within a brood for 5 minutes and recorded behaviour, habitat use and foraging rate. There were two Sites, the first with 4 broods and the second with 3 broods. http://r.789695.n4.nabble.com/file/n4511178/Table_PP_Maslo_et_al.png As the data within a brood is non-independent and the fact that there were so few, then conventional statistical tests were of little use. I therefore spent a couple of months looking at mixed-models to allow me to use all the data for each day and use (1|Brood) as a random effect.
At first i struggled with what models meant, but last week they 'sort of ' clicked and realised how to run them and how to weigh which models were the best (using AICc). As I had a number of factors/covariates that I wanted to look at I learned to use the dredge command in the MuMIn package from an a priori global model and decided to model average the models with a delta<2. I have two main questions: I was looking at similar research that also looked at models and they also came up with model average estimates and CIs for each variable and factor. They ended up with one table showing the top so many models with their AICc, delta and weights and then another table showing the model average Estimates and CIs for each factor and co-variate and also the Intercept. Each category within each variable was shown (I have attached an image of the table - the heading does not seem to match what is shown however). Their explanation of the variables was as follows: "A second model including these variables and wind speed reported a DAICc score <2; therefore, we model- averaged the parameter estimates included in these 2 best models (Table 3). Of the 5 habitats in which we observed plovers feeding, effect size was highest at artificial tidal ponds (5.52), followed by the intertidal zone (3.97). Positive effects of ephemeral pools (2.65) and bay shores (2.32) on adult foraging rates were 48% and 42% lower than artificial ponds, respectively. Conversely, sand flats (-2.30) had an equal but opposite effect on foraging rate, when compared to bay shores. The results also indicated that foraging rate was highest for adults during the post-breeding stage. In addition, vehicles had a 2.3 times larger effect on foraging adults than people. Finally, foraging rates during low tide were higher than at high tide by a factor of 2.5, as would be expected." As you can see, their explanation seems to suggest that all values are comparable e.g. vehicles and people. When I ran the model average I also got an Intercept estimate but only the second and beyond categorical Estimates were shown (e.g. if one factor was high tide, low tide, then only the estimate for low tide was shown, obviously an estimate of difference between the two). I asked on stats.stackexchange and they suggested just adding -1 to the end of the model, but although this worked, the estimates became much bigger to compensate for there being no intercept and although the difference between the Estimates were the same for 'within factor', the 'among factor' variables seemed to change (bigger differences between), along with the p-values for each group. In addition there was, of course, no intercept. I am therefore wondering whether anyone knows how I may be able to preserve the initial Estimates but still get the missing values (obviously the other researchers seemed to have done this as they still have an intercept and comparable estimates). This is my most important issue right now, but if someone has a moment, could you also tell me whether I should use the p-values as well, or should i just stick with explaining the magnitude of the effects, their direction and their Relative Importance. i want to keep it at a level that I can understand. Thank you in advance. I know everyone is busy but I would be very grateful for a prompt response if at all possible. Sincerely. -- View this message in context: http://r.789695.n4.nabble.com/Urgent-I-really-need-some-help-lme4-model-avg-Estimates-tp4511178p4511178.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
