search for: biomass

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) {bioden=Biom...

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, p...

Dear all I have just had the question from a colleague. I know that it is not directly related to R (I will probably use R to do the analysis), but I hope someone can give us some insight: Thanks, AJ Smit I sampled populations of a seaweed in the intertidal in order to estimate the standing biomass of that seaweed at that site. Due to clumped distribution patterns, I chose a stratified sampling system, as follows. In each of three subjectively defined biomass classes (low, medium and high biomass density), four quadrats (usually) were haphazardly placed, and the biomass in those quadrats har...

Dear all, I have a problem with interactions in lmer. I have 2 factors (garden and gebiet) which interact, plus one other variable (home), dataframe arr. When I put: / lmer (biomass ~ home + garden:gebiet + ( 1|Block), data = arr)/ it writes: /Error in lme4::lFormula(formula = biomass ~ home + garden:gebiet + (1 | : rank of X = 28 < ncol(X) = 30/ In the lmer help I found out that if not all combination of the interaction are realized, lmer has problems and one should...

...small example. It is taken from 'The R Book', Crawley p. 365. The data is at http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/competition.txt In R > comp<-read.table("C:\\Temp\\competition.txt", header=T) > attach(comp) Data has dependent variable 'biomass' and different types of 'clipping' that were done: Control (none), n25, n50, r10, r5: > summary(comp) biomass clipping Min. :415.0 control:6 1st Qu.:508.8 n25 :6 Median :568.0 n50 :6 Mean :561.8 r10 :6 3rd Qu.:631.8...

...g 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, main=species.name, xlab="dbh in cm", ylab="biomass in kg") ----- The output is CussoniaHolstii, but I want CussoniaHolstii.jpg. The help file for jpeg() specifies that the name include the extension (e.g....

...bles with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep (c(1.23,2.4,1.26,1.78,1.97,1.35,1.23,2.4,1.26,1.78), 20) Roughness <- rep(c(3.45,2.56,1.32,5.67,3.73,3.57,2.66,1.52,7.67,2.73),20) Biomass <- rep(c(8,5.3,3.5,12,25.4,10.1,9.8,2.4,5.6,5.3),20) myData <- data.frame (fReserve ,DivBoulders ,Roughness ,Biomass ) #glm glm1 <- glm (Biomass ~ fReserve * Roughness + fReserve * DivBoulders , family= Gamma, data= myData) #glmm: library (nlme) lme1 <- lme (Biomass ~ fReserve ,...

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 different dates. I appreciate your help. Ahmed Attia, Ph.D. Agronomist & Soil S...

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"...

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

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 gives the higher biomass values. I would like to get something analoge to the "Estimatet Marginal Means" you get with SPSS. Who can help me a...

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$date == date1] # third value dataset3$littfall[dataset3$date == date2] Note that you may have to convert character strings to dates to do the above - see a function like "as.Date". Obviously I do not know the actual names of your datas...

...e 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 pH. What I don't understand is why the anova done in such a way: summary(aov(BioMass~pH)) ... gives me completely different p-values if I define the pH as factor or not. And what would be the correct approach? Help on this subject would be much appreciated! Hronn -- View this message in context: http://r.789695.n4.nabble.com/ANOVA-define-as-factor-or-not-tp3846861p3846861.html...

Hello R Community, Subject: Bayes Factor A Bayesian ANOVA of the form: competitionBayesOut <- anovaBF(biomass ~ clipping, data = competition) Returns the following Error message: Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ?compare? for signature ?"BFlinearModel", "missing", "tbl_df"? My guess the problem is in the ?class?,...

...need to use lme in R. At each watering treatment, I had 5 different combinations of plants. I have shown how I have labelled these in brackets for species A: A in isolation (Aiso) A+A monoculture (Amono) A+B interspecific competition (Amix) .. and the same combinations for species B. I have final biomass data for each of the plants. My first step is to check species A for interspecific competition, for which I will use: Response variable: Amix (continuous - biomass measurement) Random effect: Block (factor - replicates labelled as 1,2,3) Main effect: watering treatment (wt) (factor...

...ops 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 iterations for the regression coefficients, which were estimated previously in WinBUGS, and what I'm trying to do is for each tree I want to predict the biomass using each species-specific iteration of the regression coefficients, so that ultimately each tre...

...ng the model grace.model <- specify.model() salinity -> soil_salinity, NA, 1 flooding -> soil_high_flooding, NA, 1 flooding -> soil_low_flooding, flooding_low_flooding, NA infertility -> soil_organic, NA, -1 infertility -> soil_carbon, inf_carbon, NA disturbance -> dstb, NA, 1 biomass -> masslog, NA, 1 light -> light_effect, NA, 1 lightlog -> light_effect, lightlog_light_effect, NA richness -> species_count, NA, 1 salinity -> richness, salinity_richness, NA salinity -> light, salinity_light, NA flooding -> richness, flooding_richness, NA flooding -> bioma...

...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 by leaf biomass to produce a new variable. I agree that a loop is not needed, would the forecast library help or should I use predict library. Stand_Height=ts(Stand_Height$height,start=2009,end = 2016, frequency =365) plot(forecast(ets(Stand_Height),10)) a=seq(as.Date("2009-12-01"),by=...

...r errors with three separate datasets that I'm using. At a guess it might be something to do with the very large number of variables (e.g. 23 variables, 250 or so data points) but I was wondering if anyone had any other ideas. It works fine for regular variable importance calculation. Code: biomass.cf<-cforest(Total.biomass ~ ., data=biomass, control=cforest_unbiased(ntree=2500, mtry=8)) biomass.cf.vi<-varimp(biomass.cf, conditional=TRUE) Error: Error in if (node[[5]][[1]] == variableID) cp <- node[[5]][[3]] : argument is of length zero In addition: Warning messages: 1: In matrix...

...th and I have a statistical problem that seems to not be able to handle. Recently, I conducted an experiment about plant growing in three different nutrient-level sediments. I harvested these every three week (three harvests in all). Some growth traits of these plants were recorded (e.g. total biomass, leaf biomass and stem biomass). In addition, I found that the total plant biomass may also influence these traits. So, I am going to use total biomass as covariate to do the statistical analysis. Meanwhile, this experimental design is repeated design, I guess I have to do the repeated-measurem...