similar to: Retrieve indexes of the "first occurrence of numbers" in an effective manner

Hi, I am trying to normalize some data. First I fitted a principal curve (using the LCPM package), but now I would like to apply a transformation so that the curve becomes a "straight diagonal line" on the plot. The data used to fit the curve would then be normalized by applying the same transformation to it. A simple solution could be to apply translations only (e.g., as done after a

Hi, I've had this problem for a while and tackled it is a quite dirty way so I'm wondering is a better solution exists: If we have two vectors: v1 = c(0,1,2,3,4) v2 = c(5,3,2,1,0) How to remove one instance of the "3,1" / "1,3" double? At the moment I'm using the following solution, which is quite horrible: v1 = c(0,1,2,3,4) v2 = c(5,3,2,1,0) ft <-

Thanks for pointing me out "aggregate", that works fine! There is one complication though: I have mixed types (numerical and character), So the matrix is of the form: A 1.0 200 ID1 A 3.0 800 ID1 A 2.0 200 ID1 B 0.5 20 ID2 B 0.9 50 ID2 C 5.0 70 ID1 One letter always has the same ID but one ID can be shared by many letters (like ID1) I just want to keep track of the ID, and get

Hi, I have a three dimensional array, e.g., my.array = array(0, dim=c(2,3,4), dimnames=list( d1=c("A1","A2"), d2=c("B1","B2","B3"), d3=c("C1","C2","C3","C4")) ) what I would like to get is then a dataframe: d1 d2 d3 value A1 B1 C1 0 A2 B1 C1 0 . . . A2 B3 C4 0 I'm sure there is one function to do

Dear All, I have a large data frame ( 2700000 lines and 14 columns), and I would like to extract the information in a particular way illustrated below: Given a data frame "df": > col1=sample(c(0,1),10, rep=T) > names = factor(c(rep("A",5),rep("B",5))) > df = data.frame(names,col1) > df names col1 1 A 1 2 A 0 3 A 1 4 A

Hi, I was wondering the what would be the (most efficient) way to generate a sequence of sequences, i mean: if I have 1,2 and 3. I'd like to generate a sequence of length N*3 (N ~ 1,000,000 or more) Where random permutations of the sequence 1,2,3 follow each other. i.e 1,2,3,1,3,2,3,2,1 /!\ The thing is that there should never be twice the same number of in the same sub-sequence,

Dear All, I would like to generate random protein sequences using a HMM model. Has anybody done that before, or would you have any idea which package is likely to be best for that? The important facts are that the HMM will be fitted on ~3 million sequential observations, with 20 different states (one for each amino acid). I guess that 2-5 hidden states should be enough, and an order of 3 would

Hello, The heatmap function conveniently has a "reorder.dendrogram" function so that clusters follow a certain logic. It seems that the hclust function doesn't have such feature. I can use the "reorder" function on the dendrogram obtained from hclust, but this does not modify the hclust object itself. I understand that the answer should be within the "heatmap"

Hi, I'm wondering which function would allow fitting this type of data: tmp=rnorm(2000) X.1 = 5+tmp Y.1 = 5+ (5*tmp+rnorm(2000)) tmp=rnorm(100) X.2 = 9+tmp Y.2 = 40+ (1.5*tmp+rnorm(100)) X.3 = 7+ 0.5*runif(500) Y.3 = 15+20*runif(500) X = c(X.1,X.2,X.3) Y = c(Y.1,Y.2,Y.3) plot(X,Y) The problem with loess is that distances for the "goodness of

Dear list, perusing the GMane archive shows that the issue with XML 3.4.x still bugs odfWeave users. I just checked that the newer XML 3.4.2 version still give the same problem. Using it to weave a bit of documentation written with LibreOffice 3.3.3 (current in Debian testing) leads me to a 192 Kb, 590 pages document, whereas reinstalling XML 3.2.0 gives me a 4 pages, 24Kb file (not

Dear list members, I am using Mclust in order to deconvolute a distribution that I believe is a sum of two gaussians. First I can make a model: > my.data.model = Mclust(my.data, modelNames=c("E"), warn=T, G=1:3) But then, when I try to plot the result, I get the following error: > mclust1Dplot(my.data.model, parameters = my.data.model$parameters, what = "density")

Dear All, I cannot find a solution to the following problem although I imagine that it is a classic, hence my email. I have a vector V of X values comprised between 1 and N. I would like to get random samples of X values also comprised between 1 and N, but the important point is: * I would like to keep the same distribution of distances between the X values * For example let's say N=10 and

Hi, The pdf function would not let me change the paper size and gives me the following warning: pdf("figure.pdf", width="6", height="10") Warning message: ?mode(width)? and ?mode(height)? differ between new and previous ==> NOT changing ?width? & ?height? If I use the option paper = "a4r", it does not give me a warning but still prints on a

Hello, I was wondering if anyone can think of a straightforward way (without loops) to do the following shuffling: Let's imagine a vector: c(1,1,1,2,2,3,3,3) I would like to derive shuffled vectors __where the same digits are never separated__, although they can be at both ends (periodicity). So the following shuffled vectors are possible: c(2,2,1,1,1,3,3,3) c(2,1,1,1,3,3,3,2)

Dear R help, Apologies for the less than informative subject line. I will do my best to describe my problem. Consider the following matrix: mdat <- matrix(c(1,0,1,1,1,0), nrow = 2, ncol=3, byrow=TRUE, dimnames = list(c("T1", "T2"), c("sp.1", "sp.2", "sp.3"))) mdat In my actual data I have time

Dear All, I'd like to compare exponential or power-law distributions. To do so, people are often referred to the ks.test. However, I imagine ks.test wouldn't be as powerful as a test specifically designed for a distribution type. So my question is, is there a more specific test for each of these distribution? (exponential or power-law) Thanks for your hints! E

Hi, I was looking for a function like "which()" but only returns the first argument. Compare: x <- c(1,2,3,4,5,6) y <- 4 which(x>y) returns: 5,6 which(x>y)[1] returns: 5 which(x>y)[1] is exactly what i need. I did use this but the dataset is too big (~18 mio. Points). That's why i need a more effective way to get the first element of a vector which is

Hi, I posted a message earlier entitled "How to fit a line through the "Mountain crest" ..." I figured loess is probably the best way, but it seems that the problem is the robustness of the fit. Below I paste an example to illustrate the problem: tmp=rnorm(2000) X.background = 5+tmp; Y.background = 5+ (10*tmp+rnorm(2000)) X.specific = 3.5+3*runif(1000);

Hello everyone, I am currently working with some data where I need to select the first occurrence of a value after the occurrence of another value. The data has two columns, one with a time and one with occurence of certain events. The column of data I want to select from looks like this (and each of these events have a corresponding time in another column). Stat71 OutMag FirstResp InMag

Hi, Premises: I have a database which contain the list of events and their time stamps (This is a Unix time stamps) What I want to do : I want know how much is the maximum occurrence of this in any a time period of 7 days or does a event occur es more than "N" (say 5) times in a period of 7 days. This time period is not fixed with "week