similar to: how to create data.frame with dynamic count of values

After importing a table with "M" variables and "N" records, I'd like to calculate chi-square statistics, say, between N1, N2; N1, N3, ..., N1, Ni, and then N2, N3, ... N2, Ni, ..., Ni-1, Ni. Two loops should be ok but the manual & online help don't show a systematic way to do so but instead show hard-code examples so users have to type in the names themselves one

Dear Bert, thank you for your response. here it is the piece of R code : given 3 data frames below --- N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"),

I'm stumped, can anyone find my error in this sequence. for(j in 1:(varsize[4]-1)) temp <- get.var.ncdf(nc=input, varid="p_foraging",c(1,1,j),c(varsize[1],varsize[2],1)) filled.contour(x, y, temp, color = terrain.colors, plot.title = title(main = paste("Everglades Wood Stork Foraging Potential \nYear", (2000+j)), xlab =

Hi Bogdan, Kinda messy, but: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400))

> On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > Hi Bogdan, > Kinda messy, but: > > N <- data.frame(N=c("n1","n2","n3","n4")) > M <- data.frame(M=c("m1","m2","m3","m4","m5")) > C <-

Greetings R users! I am working with the ENSEMBLE climate data (10 min resolution daily temperatures images for all of Europe 1950-2006). The data comes packaged in a single netCDF file. I would like to read the data in and export a subset (2002-2006) as geotiffs (one image per day). So far, I can successfully read in the data and view the images within an R display window. However, I have yet to

Hi, how can I create an array of lists of three components? This approach does not work: n1 <- 2 n2 <- 4 n3 <- 5 res <- array(rep(vector("list",3), n1*n2*n3), dim = c(n1,n2,n3)) res[1,1,1] # is not a list with three components... The goal is that res[1,1,1] is a list with three components. Also, appending the components didn't work. For example, I tried: component

Dear R-users, Is there an R function to compute the multinomial beta function? That is, the normalizing constant that arises in a Dirichlet distribution. For example, with three parameters the beta function is Beta(n1,n2,n2) = Gamma(n1)*Gamma(n2)*Gamma(n3)/Gamma(n1+n2+n3) Thanks in advance for any assisstance. Regards, Greg [[alternative HTML version deleted]]

A strange problem with sem: I downloaded the sem library and then, I specified my simple measurement model (below). I highlighted it and ran it. It ran, but it did NOT tell me "22 lines read". And nothing works after that - it looks like it runs, but it does not produce anything... Did I make a mistake somewhere in the model? (notice, TIME has only 1 indicator - t1, and I fixed t1's

I have a file like this: id n1 n2 n3 n4 n5 n6 1 3 4 7 8 10 2 2 4 1 2 4 3 10 3 7 0 0 0 0 8 4 10 1 0 0 2 3 5 11 1 0 0 0 5 what I want to do is: only if n2=0 and n3=0 and n4=0 and n5=0 then delete the row. how can I do that? thank you, karena -- View this message

Thank you David. Using xtabs operation simplifies the code very much, many thanks ;) On Tue, Jun 6, 2017 at 7:44 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > > On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > > > Hi Bogdan, > > Kinda messy, but: > > > > N <-

foo <- rnorm(30*34*12) dim(foo) <- c(30, 34, 12) I want to make a data.frame out of this three-dimensional array. Each dimension will be a variabel (column) in the data.frame. I know how this can be done in a very slow way using for loops, like this: x <- rep(seq(from = 1, to = 30), 34) y <- as.vector(sapply(1:34, function(x) {rep(x, 30)})) month <- as.vector(sapply(1:12,

Simple matrix indexing suffices without any fancier functionality. ## First convert M and N to character vectors -- which they should have been in the first place! M <- sort(as.character(M[,1])) N <- sort(as.character(N[,1])) ## This could be a one-liner, but I'll split it up for clarity. res <-matrix(NA, length(M),length(N),dimnames = list(M,N)) res[as.matrix(C[,2:1])] <-

Hi, I run this code to get the power of the test for modified bartlett's test..but I'm not really sure that my coding is right.. #normal distribution unequal variance asim<-5000 pv<-rep(NA,asim) for(i in 1:asim) {print(i) set.seed(i) n1<-20 n2<-20 n3<-20 mu<-0 sd1<-sqrt(25) sd2<-sqrt(50) sd3<-sqrt(100) g1<-rnorm(n1,mu,sd1) g2<-rnorm(n2,mu,sd2)

Dear Contributors, thanks for collaboration. I am trying to reorganize data frame, that looks like this: n1.Index Date PX_LAST n2.Index Date.1 PX_LAST.1 n3.Index Date.2 PX_LAST.2 1 NA 04/02/07 1.34 NA 04/02/07 1.36 NA 04/02/07 1.33 2 NA 04/09/07 1.34 NA 04/09/07

Here's another approach: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) # Rebuild the factors using M and N C$m <-

I've got configuration which in simple includes combination of afrs and unify - servers exports n[1-3]-brick[12] and n[1-3]-ns and client got cluster configuration: volume afr-ns type cluster/afr subvolumes n1-ns n2-ns n3-ns option data-self-heal on option metadata-self-heal on option entry-self-heal on end-volume volume afr1 type cluster/afr subvolumes n1-brick2

I am trying to solve one of my graduate level physics problems with ruby... Here is what I have so far... a6=0.0 for n1 in -10..10 for n2 in -10..10 for n3 in -10..10 if n1!=0 and n2!=0 and n3!=0 p=Math.sqrt(n1**2+n2**2+n3**2+n1*n2/1.414+n1*n3/1.41+n2*n3/1.414) a6+=(1/p)**6 end end end end puts a6 What I''ve got here is a 10x10x10 face-centered

Hi all, I'm working on a target whose registers have equal-sized subregisters and all of those subregisters can be named (or the other way round: registers can be grouped into super registers). So for instance we've got 16 registers W (as in wide) W0..W15 and 32 registers N (as in narrow) N0..N31. This way, W0 is made by grouping N0 and N1, W1 is N2 and N3, W2 is N4 and N5, ..., W15 is

Dear all, please could you advise on the R code I could use in order to do the following operation : a. -- I have 2 lists of "genome coordinates" : a list is composed by numbers that represent genome coordinates; let's say list N : n1 n2 n3 n4 and a list M: m1 m2 m3 m4 m5 2 -- and a data frame C, where for some pairs of coordinates (n,m) from the lists above, we have a