similar to: self-defined distance function to be computed on matrix

Hello, I am using names function to name an array. It works first time when I use *as.numeric(names(myVar1)* However, at a place later, when I tried to use a very similar line of code *as.numeric(names(myVar2)*, it always returned 'numeric(0)' (or if I only type 'names(myVar2), it gave me NULL'. Both myVar1 and myVar2 are type of integer. The only difference that I can tell is

Hello, a. is there a possibility to obtain a distance matrix with the cosine between vectors?? hclust, hierclust, dist will not work and seem to be hard to extend. b. if there is not: Is the cosine between vectors implemented somewhere? Thanks for all hints and advice! Petra Steiner -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hello folks, Any ideas how to do this? data.frame is a data frame with column names "x1",...,"xn" y is a response variable of length dim(data.frame)[1] I want to write a function function(y, data.frame){ lm(y~x1+...+xn) } This would be easy if n was always the same. If n is arbitrary how could I feed the x1+...+xn terms into lm(response~terms)? Thanks Richard -- Dr.

Hi all, a user contacted me about a segfault when installing WGCNA package dowloaded from CRAN. I also see a segfault like that on certain installs of R. The package passes all CRAN checks, so presumably this has something to do with the R installation or environment. The R versions here are not the newest but I would guess that this is not an R version issue. I'm attaching two

Hi all, I'm trying to set up the Windows Rtools toolset for building packages with compiled code. I installed for Windows R-3.4.3 from CRAN and installed Rtools-3.4 in a custom location M:\R\R-3.4.3 and M:\R\Rtools-3.4 Following the instructions, in shell, I set Path=M:\R\Rtools-3.4\bin;M:\R\Rtools-3.4\gcc-4.6.3\bin;M:\R\R-3.4.3\bin;... (the ... are other paths irrelevant for R/Rtools).

Dear all, I am running a panel regression with time and location fixed effects: ### reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2 , data=mydata, na.action="na.omit") ### My goal is to use the estimation for prediction. However, I have 8,500 IDs, which is resulting in very slow computation. Ideally, I would like to do the following: ### reg2 <-

Dear all , I would like to reconstruct coexpression networks from proteomic count data having integer values. Some internal function doesn't like to work well with integers. How can this error be rectified? > adjacency = adjacency(datExpr, power = softPower, type = "signed"); Error in cor(datExpr, use = "p") : REAL() can only be applied to a 'numeric', not a

Hi, I'm running a tutorial ("Meta-analyses of data from two (or more) microarray data sets"), which use wgcna package. I have an error in the function modulePreservation (it is below). I'm using R2.13 Can you help me? Do you know, what is happens? Thanks Raquel multiExpr = list(A = list(data=t(badea)),B = list(data=t(mayo))) # two independent datasets (dim = 13447 x 36) mp =

Hello, I am trying to "paste" together a formula to use in the mob function of party. This means the formula will be of the form y ~ x1+ ...+xM | z1+..zN. I am doing some preliminary fits of y ~ x1+ ...+xM, then want to add the conditional part of the equation using update(). Here's the test code: var1 <- 1:78 x1 <- paste("x", var1, sep="") f1 <-

Hello fellow R users, I am currently learning to use R, so please forgive me if there is an obvious explanation for the following problem. My goal is to perform WGCNA on a dataset of 19776 genes, so I opted to follow the block-wise network construction (Section 2c) in the WGCNA R Tutorial by Peter Langfelder and Steve Horvath. I ended up with 10 blocks and am able to plot the dendrograms and

I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf. Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors. Also, your use of I() is

Hi, I am have a data set of around 43000 probes(rows), and have to calculate correlation matrix. When I run cor function in R, its throwing an error message of RAM shortage which was obvious for such huge number of rows. I am not getting a logical way to cut off this huge number of entities, is there an alternative to pearson correlation or with other dist() methods calculation(euclidean) that

i m using a package NbClust for cluster analysis. in the following algorithm ->NbClust(m, diss="NULL", distance = "euclidean", min.nc=2, max.nc=15, method = "ward", index = "all", alphaBeale = 0.1) i want to define my own dissimilarity matrix of dimension 38*38. my original data "m" is a matrix of 365*38. whenever i define my own dissimilarity

Dear all, I am trying to find a solution satisfying the below equations in R. Set up the problem 9 X1+ X2 + X3 = 2 X1+ X2 + X3 = 1 which is subjected to 0 < X1 < X2 < X3 < 2. I have downloaded the packages \'linprog\' and \'lpSolve\' but can not see how to solve the question. Thank you for your help. With

Hey Parth, Thanks for the reply. I am considering implementing a cosine distance metric too, along with euclidian distance because of the dimensionality issue that comes in with K-Means and euclidian distance metric. That does help when we deal with sparse vectors for documents. The particular problem I'm having is representing centroids in an efficient way. For example, when we find the mean

I am trying to find the distance between a vector and each row of a dataframe. I am using the function "distancevector" in the package "hopach" as follows: mydata<-as.data.frame(matrix(c(1,1,1,1,0,1,1,1,1,0),nrow=2)) V1 V2 V3 V4 V5 1 1 1 0 1 1 2 1 1 1 1 0 vec <- c(1,1,1,1,1) d2<-distancevector(mydata,vec,d="euclid") The Euclidean distance

Dear friends, I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on proportion data. I use glm(y~x1+,family=binomial) y is a proportion in (0,1), and x is a real number. I get the error: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! But that is exactly what was suggested in the book, where there is no mention of a similar warning. Where am I

Hi: I am trying to implement interval arithmetic through llvm. I have a problem with the rounding mode with llvm.experimental.constrained.cos I have two pieces of codes: ; Function Attrs: norecurse nounwind readnone ssp uwtable define double @cosine_down(double returned) local_unnamed_addr #0 {   ; call the llvm intrinsic to perform downward cosine

This shouldn't be hard, but it's just not coming to me: Given a vector, e.g., v <- c(20, 134, 45, 20, 24, 500, 20, 20, 20) how can I get the index of the last value in the vector having a value greater than n, in this case, greater than 20? I'm looking for an efficient function I can use on very large matrices, as the FUN argument in the apply() command. Confidentiality

Dear All, I am using the plotrix library to plot some matrices. I have a problem: some of my data are outliers, hence using a linear color scale does not work very well (you would see too many cells having a similar, indistinguishable color). See the code snipped at the end of the email. Plotting the logarithm of the data gets the job done, but my problem is that I would like to write in every