Re: [R] Urgent - I really need some help lme4 model avg Estimates
Thank you Mitchell, I will try that. So I presume that the initial paper where they showed the estimates AND the intercept from a model averaging procedure may have been done using a different method? Would it still be prudent to use a global model and then perhaps show the top so many, perhaps those with a delta2 and then show their weights? Would it also be okay to just do a model average and then perhaps show the weights of each covariate and factor within these models to show their relative importance? I think the way the paper presented the results of extremely similar research, using only models using A+B+C+(1|D) etc and then model average, and able to come up with an Intercept and then much smaller comparable estimates made me think that this was probably the correct way to present the results and that getting these values must be something that I just didn't know how to code. They were even able to compare the Estimate differences among the variables whereas when I used -1 to remove the intercept the distance between the variables differed (although within stayed the same). Thank you again for your kind reply. Rachel -- View this message in context: http://r.789695.n4.nabble.com/Urgent-I-really-need-some-help-lme4-model-avg-Estimates-tp4511178p4512504.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.
[R] two lmer questions - formula with related variables and output interpretation
Hello, I have been attempting to set up a lme and have looked at numerous posts including 'R's lmer cheat-sheet' as well as reading a number of papers and other resources including R help, but I am still a little confused on how to write my model (I thought I had it). I have asked a number of questions on different forums; most of which have been resolved. My main concern right now is whether my model is correct. I studied broods of precocial chicks and watched each chick every other day for five minutes if possible. As chicks on the same day are completely non-independent the mean was found for each brood for each day. Variables that were recorded were the behaviours during that time and the habitats used. There were seven broods. Three at one site and four at the other site. Only one site had a brood that consistently used mudflats rather than oceanfront habitats. As none of the data within a brood is truly independent, along with the very small number of broods, it became impossible to use conventional statistics to test the hypotheses and so it was suggested that mixed-effects models would be the best option as it would not only allow for all data to be used with a random effect of Brood ID to negate the pseudo-replication but also let me look at partial use of mudflats in one of the other broods that only used it periodically. So, for this part of the analysis I would like to see which factors affect the amount of time feeding. I set up a global model with ten fixed variables plus (1|Brood). Site, tide.h.l, tide.inc.out, MF.vs.OF, Human Disturbance Rate (HDr), Human Disturbance proportion of time(HDp), non-Human Disturbance (two variables as for Human Disturbance) and Age and mean.foraging.rate. As so: gm1-lmer(Feeding~Site+tide.level+MF.vs.OF+HDr+HDp+NHDr+NHDp+Age+mean.for.rate+(1|Brood), data=AllBrood, REML=TRUE) I wished to put all the factors together to explore which ones really did influence the time spent feeding and used 'dredge' command to run all possible combinations and then averaged the models with an AICc Delta2. I was expecting that the proportion of time being disturbed (HDp and NHDp) would be the most relevant as by default the greater time in other behaviours the less time for feeding. However, MF.vs.OF had a larger effect than HDp and NHDp but this may be because MF observations did not experience HDp at all so this may push the effect of this habitat. Surprisingly non-human disturbance rates rather than time had a greater effect (but these are quite even among habitats. The results of the model.avg are as follows: Estimate Std. Error z value Pr(|z|) (Intercept) 102.7190 5.5300 18.575 2e-16 *** HDr-1.5495 0.3451 4.490 7.11e-06 *** MF.vs.OF2 -7.6780 3.7507 2.047 0.04065 * NHDp -0.5145 0.2909 1.769 0.07695 . NHDr -1.4164 0.4663 3.037 0.00239 ** Site2 6.1477 2.7400 2.244 0.02485 * tide.h.l2 -7.2546 2.6914 2.695 0.00703 ** tide.inc.out2 -5.8486 2.6187 2.233 0.02553 * HDp-0.3773 0.2732 1.381 0.16731 mean.for.rate -0.3966 0.3220 1.232 0.21807 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Full model-averaged coefficients (with shrinkage): (Intercept)HDr MF.vs.OF2 NHDp NHDr Site2 tide.h.l2 tide.inc.out2HDp 102.718962 -1.549499 -5.734171 -0.239550 -1.416373 5.336532 -7.254627 -5.848553 -0.044795 mean.for.rate -0.081734 Relative variable importance: (Intercept) Age HDp HDr mean.for.rate MF.vs.OF NHDp NHDr 1.00 0.00 0.12 1.00 0.21 0.75 0.47 1.00 Site tide.h.l tide.inc.out 0.87 1.00 1.00 I was wondering whether there would be a better way to formulate the model to allow for this effect, or could I just keep it as is and just infer that it may be partly affected by the amount of disturbance within these habitats but as it has a greater effect that other factors are at play which would then lead me onto the next model which is going to explore observations that do not include disturbance which would allow me to tease the natural factors affecting feeding behaviour? I was going to run this second model with site still as a fixed effect and then run it with (1|Site) to remove site effect (if one is found). I would prefer to keep it simple as I really want to use a lme, but don't have the understanding for more complex interactions. I has also asked a question, which is yet to be answered on stats stack exchange, in regards to the output of the model.avg. as follows: I have seen the Estimates described as the effect of the variable and this is discussed in results sections as an important value to report (in regards to the size of them and their direction (+ve/-ve). (the paper I
Re: [R] Using MuMIn - error message
Hello Mike, I don't think I did, but I fixed the issue by loading each package before use. The second issue was solved by removing a variable that was used to create two other categorical variables. I think it must have been recognising this. Thanks for the help. -- View this message in context: http://r.789695.n4.nabble.com/Using-MuMIn-error-message-tp4500236p4508901.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.
Re: [R] two lmer questions - formula with related variables and output interpretation
I realised that I removed the link to the question but forgot to remove the text regarding it. Sorry. I am not sure if I am supposed to link to other forums, but I can add the links as needed (as the format is clearer). I actually have one more question though in regards to which data to use. If it is better to just report the estimates and CIs then should I use those with shrinkage instead, and if so, does anyone know how I can get the CIs for these rather than just the regular CIs. I apologise if I am asking too many questions within one post. Rachel -- View this message in context: http://r.789695.n4.nabble.com/two-lmer-questions-formula-with-related-variables-and-output-interpretation-tp4508876p4509334.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.
[R] Urgent - I really need some help lme4 model avg Estimates
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 delta2. 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.
Re: [R] Urgent - I really need some help lme4 model avg Estimates
I understand where you are coming from, but the issue is that some exploration of the data through graphs and the like, showed that patterns could be seen. However with only 7 means it is extremely difficult to get any kind of statistical evidence and as some mean values are the same some of the tests that I wanted to use such as a Mann-Whitney would not even run so I had to resort to a one-sample Wilcoxon with a set mu value. (minimum p-value that was even possible was p=0.280). I asked a couple of forums in December about the issues at hand and they suggested that I look into mixed-effect models so I read some chapters on them and got very excited, but at the time still thought of them as some test that could give me means. However it all clicked and I realise that they can be more useful as a tool to illustrate which factors and covariates best fit to the response variable. I understand the concepts of fitting an intercept and slope somewhat but the paperwork on it can be a little confusing, however the way they were used in the paper (of which I attached one of the tables) seemed a very straightforward method of teasing the intricate factors of habitat, age and other factors that could be affecting behaviour such as time feeding and foraging rate. Believe me, if i could have survived with Kruskal-Wallis then I would have had my thesis written up three months ago with a lot less stress. I am not looking for pretty as I don't even want it published, but I did hope to be able to give the time that I spent collecting data justice. I have come really far, thanks to some great people, but I do not have anyone near me who can help and my adviser is 3000 miles away too and is not a statistician either. All I would like to know is how could Maslo et al. have calculated estimates for all categories AND an intercept and is there a method to do this in R. I have spent months trying to find these answers and so I would greatly appreciate an answer to this question. Thank you again. -- View this message in context: http://r.789695.n4.nabble.com/Urgent-I-really-need-some-help-lme4-model-avg-Estimates-tp4511178p4511396.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.
Re: [R] Using MuMIn - error message
This was solved by loading each package right before using it (I thought you
loaded both at the beginning). However I now have an additional issue. I ran
a small model with just three factors to try it out and it worked
beautifully, but when I tried to run it with all the factors this is what I
got when I tried to run the model:
ABmodel-read.csv(file.choose(), header=T)
ABmodel$Brood-as.factor(ABmodel$Brood)
ABmodel$Site-as.factor(ABmodel$Site)
ABmodel$Age.class-as.factor(ABmodel$Age.class)
ABmodel$Tide-as.factor(ABmodel$Tide)
ABmodel$tide.h.l-as.factor(ABmodel$tide.h.l)
ABmodel$tide.inc.out-as.factor(ABmodel$tide.inc.out)
ABmodel$MF.vs.OF-as.factor(ABmodel$MF.vs.OF)
local({pkg - select.list(sort(.packages(all.available =
TRUE)),graphics=TRUE)
+ if(nchar(pkg)) library(pkg, character.only=TRUE)})
fullABmodel1-lmer(Feeding~Site+Age.class+tide.h.l+tide.inc.out+Tide+HDp+NHDp+Foraging.Eff+MF.vs.OF+Age+HDr+NHDr+AllDr+(1|Brood),
data=ABmodel, REML=FALSE)
Error in mer_finalize(ans) : Downdated X'X is not positive definite, 7.
fullABmodel1-lmer(Feeding~Site+Age.class+tide.h.l+tide.inc.out+Tide+HDp+NHDp+Foraging.Eff+MF.vs.OF+Age+HDr+NHDr+AllDr+(1|Brood),
data=ABmodel, REML=FALSE)
Error in mer_finalize(ans) : Downdated X'X is not positive definite, 7.
fullABmodel1-lmer(Feeding~Site+Age.class+tide.h.l+tide.inc.out+Tide+HDp+NHDp+Foraging.Eff+MF.vs.OF+Age+HDr+NHDr+AllDr+(1|Brood),
data=ABmodel, REML=FALSE)
Error in mer_finalize(ans) : Downdated X'X is not positive definite, 7.
summary(fullABmodel)
Error in summary(fullABmodel) :
error in evaluating the argument 'object' in selecting a method for
function 'summary': Error: object 'fullABmodel' not found
I am attaching my raw .csv file. Note those above that are as.factors. The
file has more factors than I am using because I am also going to be looking
at different issues with it. Note that broods are nested within site but I
want to see if site has an effect. I ran a smaller model and it was weighted
and had a delta below 2 but not sure if that is only because so few factors
were being compared, so thought I would run it as a fixed then maybe as a
random later. http://r.789695.n4.nabble.com/file/n4500740/AllBroodmodel.csv
AllBroodmodel.csv
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[R] Using MuMIn - error message
Hello, I hope that you can bare with me. I am new to models, but I think I have a pretty godd understanding of how to run them now, including how to use AICc and Anova. The issue is that I have many factors that I wish to compare so doing each one at a time would take forever. I came across the MuMIn package and I was so excited, however I am getting an error message and i don't know why. Firstly it is a mixed model that I am running with lme4. The .csv file that it comes from has more factors than I will probably use but I only listed the ones that I wanted to test. I had also coded the ones that are labels and not continuous data using the as.factor command and I ran the most complex model and ran summary() and it seemed to have worked fine. My model was: fm2test-lmer(Feeding~MF.vs.OF+Age.class+tide.h.l+Site+HDp+(1|Brood), data=ABMtest.df) and then I wanted to use the dredge command as so: dd-dredge(fm2test, trace=TRUE, rank=AICc, REML=FALSE) I got an error: Error in UseMethod(fixef) : no applicable method for 'fixef' applied to an object of class mer I have no idea how to fix this. I have looked at ?dredge but cannot find anything there and I am very new to R so any help would be greatly appreciated. I want to run all the possible models using the factors and then want to identify those models which best explain Feeding. I also have other models to run and in addition I have more factors to use but wanted to do this first as a test. Could someone also advise me on a way to list all models in order and if each model is accessible in order to compare with Anova? Thank you so much in advance. Rachel -- View this message in context: http://r.789695.n4.nabble.com/Using-MuMIn-error-message-tp4500236p4500236.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.
