Dear Mariano, The logit transformation will fail in case the damage is 0% or 100%. The correct distribution for ratios between 0 and 1 is a beta distribution. When 0 or 1 is present you'll need a zero and/or one-inflated beta distribution. This is currently non available in lme4. It looks like you measure the damage in steps of 5%. So you use the binomial distribution as a workaround. That is: assume that each leave has 20 'trials' (20 * 5% = 100%), each 5% damage is one successful trial.
glmer(cbind(dam / 5, 20 - dam / 5) ~ treat + day + (1|pair/plant), data=gaston, family = binomial) Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium 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 2017-03-06 21:17 GMT+01:00 Mariano Devoto <[email protected]>: > Hello again. This is a query which follows up on the very useful answer > provided by Drew Tyre who suggested creating new variables and adding the > random term you'll see in the model below. > Quick reminder: the analysis aims at understanding how the presence of > "bodyguard" ants regulates leaf damage caused by lepidopteran herbivores on > a focal plant species. > For this, I set up fourteen pairs of plants in a nature reserve and then > assigned each plant in each pair to one of two treatments, either "with > ants" or "without ants", by applying a physical barrier at the base of the > plant. > In the following nine weeks I measured four response variables once a week > on a subsample of ten randomly chosen leaves of each plant. I thus have > nine repeated measures on each plant. > > >From analyses done over the weekend I know excluding ants from the plants > (variable "treat" in the dataset) has a positive effect on the number of > butterfly eggs and larvae found on plants. > Now I want to know if this results in a reduced damage to the leafs > (variable "dam" in the data set), which is measured as the percentage of > the area eaten by herbivores. > > My response variables are: > ants: number of ants (this was measured just to check the physical barrier > had worked OK) > eggs: number of butterfly eggs > larve: number of butterfly larvae > dam: percent leaf damage (percentage eaten by larvae) > > My explanatory variables are: > treat: treatment (two levels: "con" and "sin" mean with and without ants, > respectively) > pair: plant pair > date > > #I provide a workable example below > require(lme4); require(tidyverse); require(lubridate) > > #read data from Google drive > id <- "0Bzd8I1jr8z_iU0h6R0hxaElseDA" # google file ID > gaston <- read.table(sprintf(" > https://docs.google.com/uc?id=%s&export=download";, id), head=T) > > #create variables following suggestion by Drew Tyre > gaston <- mutate(gaston, > plant = paste0(pair,treat), > date = dmy(date), > day = as.numeric((date - min(date)))) > > #After a few hours of scavenging through books, articles, blogs and R-lists > I ended up with the following model which turns the % damage into a > proportion and then logit transforms it so I can use lmer. > > M1 <- lmer(logit(dam/100) ~ treat + day + (1|pair/plant), data=gaston) > summary(M1) > > #Am I headed in the right direction? Any advice would be greatly > appreciated. > > Best, > > Mariano > > *Dr. Mariano Devoto* > > Profesor Adjunto - Cátedra de Botánica General, Facultad de Agronomía de la > UBA > Investigador Adjunto del CONICET > > Av. San Martín 4453 - C1417DSE - C. A. de Buenos Aires - Argentina > +5411 4524-8069 > http://www.agro.uba.ar/users/mdevoto/ > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-ecology mailing list > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
