similar to: problem with interaction in lmer even after creating an "interaction variable"

HI everyone, I try to get some bootstrap CIs for coefficients obtained by quantile regression. I have influencial values and thus switched to quantreg.. The data is clustered and within clusters the variance of my DV = 0.. Is this sensible for the below data? And what about the warnings? Thanks in advance for any guidance, Kay > dput(d) structure(list(Porenfläche = c(4990L, 7002L, 7558L,

Hi, I am trying to write a for loop with if else statements to calculate biomass density estimates for different types of sampling gear. My code is: bmd=for (i in 1:length(Gear)){ if (Gear==20) {bioden=Biomass/141} else {if (Gear==23) {bioden=Biomass/68}} else {if (Gear==160) {bioden=Biomass/4120}} else {if (Gear==170) {bioden=Biomass/2210}} else {if (Gear==300)

Hi there, I have a data frame as listed below: > Ca.P.Biomass.A P Biomass 1 334.5567 0.2870000 2 737.5400 0.5713333 3 894.5300 0.6393333 4 782.3800 0.5836667 5 857.5900 0.6003333 6 829.2700 0.5883333 I have fit the data using logistic, Michaelis?Menten, and linear model, they all give significance. > fm1 <- nls(Biomass~SSlogis(P, phi1, phi2, phi3), data=Ca.P.Biomass.A)

I was surprised to find that just changing the base level of a factor variable changed the number of significant coefficients in the solution. I was surprised at this and want to know how I should choose the order of the factors, if the order affects the result. Here is the small example. It is taken from 'The R Book', Crawley p. 365. The data is at

Evening folks, I'm trying to print a series of graphs to .jpeg using a variable as the title, but run into the difficultly that I can't find a way to append the file extension to the .jpeg (in this case extensionless!) files. Example: ---- species.name="CussoniaHolstii" dia<-10:100 biomass = -21.4863 + 0.5797 * (dia ^ 2) biomass jpeg(species.name) plot (biomass,

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep

Hi, I have a situation here. I try this update: mmaa <- update(mma,biomass~qvartemp) but I have this message: Error in eval(expr, envir, enclos) : object "qvartemp" not found but this object exist: [1] "cont" "i" "levelsord" "mma" "qvar" "qvarmma" [7] "qvartemp" "test"

Hi Ahmed, Your problem appears trivial as you have already specified the form of the calculation. Learn how to "extract" specified elements from a data structure: # first value sum(dataset1$NPP[dataset1$date >= date1 & dataset1$date <= date2]) # second value dataset2$biomass[dataset2$date == date2] - dataset2$biomass[dataset2$date == date1] # third value

Dear gracious R community, I would like to produce charts of phytoplankton biomass changes through time. Each species has a line, and the biomass varies in mirror form along the line for each species along the X time axis. Here is an example of what I'd like to do: http://www.er.uqam.ca/nobel/r30240/Succession.jpg Any suggestions? Thanks David Bird UQAM, Montreal

Hi everyone, I am trying to analyse my data from a small plant experiment (for a meeting tomorrow afternoon) and am a beginner to R so I apologise if this is a very basic question. I carried out a plant experiment examining plant interactions between two species (A and B) under different watering treatments. I had: - 7 watering treatments (7 different watering frequencies labelled 1-7) - 3

Hello! I urgently need to know if it is possible (and if so, how to do) to get the "Estimatet marginal means" from an ANOVA. Or explained more coplicated: It's about plants sewn in 2 densities & 2 patterns. The Variable is biomass and I also use a Covariate. There are highly significant differences between density & pattern but I don't know which of the 2 densities

Hi R users, I need your help to write a code in r that does the following calculation from three different datasets; ac = 1/sum (NPP from date 1 to date 2, dataset=1) * (biomass at date 2 -biomass at date 1, dataset = 2) + (littfall at date 2, dataset=3). all the dates are in yr-month-day format. Which library or function Should I use to tell R do these calculations of these variables at

Hello, I'm working on a problem using nested for-loops and I don't know if it's a problem with the order of the loops or something within the loop so any help with the problem would be appreciated. To briefly set up the problem. I have 259 trees (from 11 different species, of unequal count for each species) of which I am trying to predict biomass. For each tree species I have 10000

Hi all, I hope that i've posted this in the correct place. if not, please accept my apologies (where should this go?) I have carried out experimental removal of bivalves at 2 intertidal shores. Bivalves were removed by raking of surface sediments. I wish compare the biomass values of for a total of 8 species between the 2 shores My 3 treatments are: Undisturbed Controls (Cont), Procedural

Hi all This is probably a simple problem but somehow I am having much trouble with finding a solution, so I seek your help! I have a data-set with continuous response variables. The explanatory variably is 4xpH treatments (so 8.08, 7.94, 7.81 and 7.71) so also continuous and not technically factorial. However I have decided to do Anova's (as well as regression) to explore the effect of

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 The lmer() function in the lme4 package has some code of the form mc <- match.call() mc[[1]] <- as.name("lFormula") lmod <- eval(mc, parent.frame(1L)) this is a fairly common idiom in R, found e.g. in lm(), used when one wants to pass all of the arguments of a function to a different function (in the case of lm() it's

Sorry for the confusion, here is the edited question. The data= Stand_Height (attached) is recorded from 12/1/2009 to 12/31/2015 (25 observations) and the other dataset (leafbiom) is recorded from 10/7/2009 to 12/29/2016 (daily observations). I want to use the 25 observations of stand height to predict the daily stand height from 10/7/2009 to 12/29/2016. The daily stand height will be multiplied

There are a bunch of things wrong here, although without a reproducible example I can't really fix most of them. - You're overwriting SH within the loop. - You're running the regression 2641 times, even though the result never changes. - You're never predicting from your linear model using the other data not in the regression. - Leaf biomass data is never used for anything. I

Once again, you are over-writing your variable. This time, you are overwriting the entirety of Stand_Height with the timeseries of height. Perhaps you should spend some time with one of the good introductory R resources out there, and think a bit more about your procedure. Sarah On Fri, Jun 30, 2017 at 11:23 AM, Ahmed Attia <ahmedatia80 at gmail.com> wrote: > Sorry for the confusion,

Hello, I am relatively new to R and I am working through the code that is provided in the book Numerical Ecology with R and I have run across an error message that I can't seem to figure out. I am using the vegan, ade4, gclus and cluster packages. The code is as follows: # Ordered community table # Species are ordered by their weighted averages on site scores or <- vegemite(spe,