similar to: combing list objects

Hi All, I am writing a simulation that examines the effects of species extinctions on ecological communties by sequentially removing individuals of a given species (sometimes using weighted probabilities) and replacing the lost individuals with species identities randomly sampled from the remaining individuals. Thus I use two dataframes. One contains all the individuals and their species

Hello, I am really new to R and it's still a challenge to me. Currently I'm working on my Master's Thesis. My supervisor works with SAS and is not familiar with R at all. I want to run an Anova, a tukey-test and as a result I want to have the tukey-grouping ( something like A - AB - B) I came across the HSD.test in the agricolae-package, but... unfortunately I do not get an output

Hello, Im working on a 50933 point count bird abundance dataset. I've succeeded in calculating a distance matrix for this entire set, but I don't have sufficient memory to convert this to a matrix, as below... abun.dist <- dist(abun.mat[1:50993,1:235) test <- rowMeans(as.matrix(abun.dist)) Error in matrix(0, size, size) : too many elements specified ive been able to run a hclust()

Greetings, I am new to R and am hoping to get some tips from experienced R-programmers. I have a dataset that I've read into R as a dataframe. There are 5 columns: Plot location,species name, a species number code (unique to each species name), abundance, and treatment. There are 272 plots in each treatment, but only the plots in which the species was recorded have an abundance value. For

All, I'm trying to put an xyplot and a plot produced via curve() on the same page. It seems that mfrow and layout don't work, and now I'm trying print but that does not work either. Any suggestions much appreciated. Cheers, David p.11 = xyplot(1:10 ~ 1:10) curve(sin, 0,3, ylim=c(-3,3), ylab="sin and cos", lty = 1) curve(cos, 0,3, ylim=c(-3,3), lty = 5, add=TRUE)

Hi all, I want to combine two data sets (ZA and ZB to get ZAB). The common variable between the two data sets is ID. Data ZA ID F M 1 0 0 2 0 0 3 1 2 4 1 0 5 3 2 6 5 4 Data ZB ID v1 v2 v3 3 2.5 3.4 302 4 8.6 2.9 317 5 9.7 4.0 325 6 7.5 1.9 296 Output (ZAB) ID F M v1 v2 v3 1 0 0 -9 -9 -9 2 0 0 -9 -9 -9 3 1 2 2.5 3.4 302 4 1 0 8.6 2.9 317 5 3 2 9.7

Hi All, I am trying to combine two forest plots on the same page using the "forestplot" function in the rmeta package. Once I use the par() function to combine my plots on the same page, I find that my two plots are overlaying each other. Does anyone have any suggestions on how to fix this? Thanks! ________________________________ PHRI DISCLAIMER This information is directed in

Hi, When looking into the bug in https://llvm.org/bugs/show_bug.cgi?id=21421, I found a regression test in Transforms/InstCombine/malloc-free-delete.ll against me to directly fix it. The test is, define i1 @foo() { ; CHECK-LABEL: @foo( ; CHECK-NEXT: ret i1 false %m = call i8* @malloc(i32 1) %z = icmp eq i8* %m, null call void @free(i8* %m) ret i1 %z } According to

Dear members of the list, I'm fitting ordinal regressions using polr, and in some models I get the error copied below. Dependent variable is an ordered factor of bird abundance categories, and predictors are continuous habitat variables. > ro6 <- polr(formula = abun ~ InOmbrot + Oliva.OC + ToCultAr + DivCulArb + AltitMax + COORXY) > summary(ro6) Re-fitting to get Hessian

Hello, I have two tab-delimited files which I would like to combine. In the first one I have gene IDs (Unique) on column 1 and than various experimental results from microarray analysis (see attached files list1 ) the second arrays have the same genes IDs (more and in a different order, some are double) (see attached files list2 ) What I would like to do is to search in the second list for gene

> I think we can do such optimization with operator new, because new never returns null. This is incorrect in the case of `new (std::nothrow) ...` - the whole point of `(std::nothrow)` is to tell new that it should return NULL in case of failure, rather than throw an exception (bad_alloc). But the point here is not the actual return value, but the fact that the compiler misses that the

Hi Mats, I think Kevin's point is malloc can return 0, if malloc/free pair is optimized way, the semantic of the original would be changed. On the other hand, malloc/free are special functions, but programmers can still define their own versions by not linking std library, so we must assume malloc/free always have side-effect like other common functions, unless we know we will link std

Can anyone point me in the right direction of figuring out what downweight() is doing? I am using vegan to perform CCA on diatom assemblage data. I have a lot of rare species, so I want to reduce the influence of rare species in my CCA. I have read that some authors reduce rare species by only including species with an abundance of at least 1% in at least one sample (other authors use 5% as a

On 7 July 2015 at 06:17, Renato Golin <renato.golin at linaro.org> wrote: > ARM+AArch64 up. All gree. Just to note there was a bug in test-release.sh where the script would not exit if the build/tests failed making failures less noticeable. I've fixed it in r241599. It would be a good idea to use that fix unless you are happy combing through the logs for failures.

Dear friends, suppose my dataset is the following data: id<-1:9 x<-c(1,2,3,1,2,3,1,2,3) y<-c(1,1,1,2,2,2,3,3,3) data<-data.frame(id,x,y) id x y 1 1 1 1 2 2 2 1 3 3 3 1 4 4 1 2 5 5 2 2 6 6 3 2 7 7 1 3 8 8 2 3 9 9 3 3 i want to do sampling like this:say the sample size is 3. First: random sampling from x; Next ,random sampling from

Hi, I am trying to understand how the biplot.prcomp is constructed so I can manipulate it to emphasise particular observations and reduce the number of variables shown. The prcomp model I have ran has cor=TRUE and scale=TRUE I have worked out from looking at str(prcomp.model) that... prcomp.model$x = the observations ploted in the biplot prcomp.model$rotation = the variables that form the

Hi I have dataframe with different plot numbers in and different subplots as letters at the end of the plot number i.e. 1a, 1b 2-1a etc. I want to delete all rows that end in a specific letter eg... treat<-c("1a","1b","1c","2a","2b","2c","2-1a","2-1b","2-1c") a1<-1:9 b1<-9:1

Hi, I am trying to put together a biplot using symbols and different colours instead of text as points. Someone has previously suggested using this code: PC <- prcomp (iris[,1:4]) lambda <- PC$sdev * sqrt(nrow(PC$x)) plot (t(t(PC$x)/lambda),pch=16,col=as.numeric(iris[,5])) par (new=T) Rot <- t(t(PC$rotation)*lambda) XLIM <- c(-max(abs(Rot[,1])),max(abs(Rot[,1]))) XLIM <-

Hi everyone, I am working with the dbFD function of the FD package, and there's something funny happening with the value of sing.sp in the output. Basically, I have a species-function matrix and a community matrix. One site in particular has 6 species, 4 of which have identical functional coding. I thus expect nbsp = 6 and sing.sp = 2 for this site. However, nbsp = sing.sp = 6. This

Hello I'm currently using the dbFD function of the FD package and i'm having some things that I can't do. Is there any way to check the relations between dbFD indexes? Function cor for example? I can't manage to put the informations correctly dbFD function gives a lot of output (indexes - nbsp, sing.sp, FRic, FEve, FDiv, FDis and RaoQ). I want to see the relationships between