... perhaps also worth mentioning: "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
-- Bert On Tue, Mar 27, 2012 at 7:55 PM, Dragonwalker <dragonwalker...@hotmail.com> wrote: > 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. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ 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.
