similar to: ignoring zeros or converting to NA

Hi everyone, I posted something similar to this in reply to another post, but there seems to be a problem getting it onto the board, so I'm starting a new post. I am trying to use conditional formatting to select non-zero and non-NaN values from a matrix and pass them into another matrix. The problem is that I keep encountering an error message indicating the ":missing value where

Hi everyone, I want to perform an operation on a matrx that outputs the product of successive pairs of rows. For example: calculating the product between rows 1 & 2; 3 & 4; 5 & 6...etc. Does anyone know of any readily available functions that can do this? Thanks, rcoder -- View this message in context: http://www.nabble.com/product-of-successive-rows-tp18681259p18681259.html

Hi everyone, I have a rollapply statement that applies a function, in steps, over a data matrix as follows: #Code start testm<-rollapply(mat, 100, by=100, min, na.rm=F) #Code end This moves down matrix 'mat' and calculates the minimum value over a 100 row range, every 100 rows (i.e. no overlaps). NAs are not removed. I want to modify this statement somehow so that the rollapply()

Hi everyone, I'm trying to create an "if" conditional statement with two conditions, whereby the statement is true when condition 1 AND condition 2 are met: code structure: if ?AND? (a[x,y] <condition1>, a[x,y] <condition2>) I've trawled through the help files, but I cannot find an example of the syntax for incorporating an AND in a conditional IF statement.

Hi everyone, I want to extract data from a data set according to dates specified in a vector. I have created a blank matrix with row names (dates) that I want to extract from the full data set. I have then performed a merge to try to o/p rows corresponding to common dates to a results matrix, but the operation did not fill the results matrix. Coulc anyone offer any advice to assist with this

Hi everyone, I am trying to apply linear regression to adjacent columns in a matrix (i.e. col1~col2; col3~col4; etc.). The columns in my matrix come with identifiers at the top of each column, but when I try to use these identifiers to reference the columns in the regression function using rollapply(), the columns are not recognised and the regression breaks down. Is there a more robust way to

Hi everyone, I'm having trouble applying the Cor() function to two matrices, both of which contain NAs. I am doing the following: a<-cor(m1, m2, use="complete.obs") ... and I get the following error message: Error in cor(m1, m2, use = "complete.obs") : no complete element pairs Does anyone know how I can apply a correlation, ignoring any NAs? Thanks, rcoder --

Hi, I am using eigen to get an eigen decomposition of a square, symmetric matrix. For some reason, I am getting a column in my eigen vectors (the 52nd column out of 601) that is a column of all NAs. I am using the option, symmetric=T for eigen. I just discovered that I do not get this behavior when I use the option EISPACK=T. With EISPACK=T, the 52nd eigenvector is (up to rounding error) a

something like ... > x<-c(1,2,3,0,5,6,0) > is.na(x[x==0])<-T > x [1] 1 2 3 NA 5 6 NA ~~~~~~~~~~~~~~~~ Robert Kinley ~~~~~~~~~~~~~~~~ [[alternative HTML version deleted]]

Hi everyone, I'm trying to calculate correlation coefficients between corresponding columns in two matrices with identical dimensions but different data. The problem is that the matrices contain NAs in different locations. I am using the following code to try to calculate correlations between complete sets of data: #Code start maxcol<-ncol(mat1) for (i in 1:maxcol) {

Hi everyone, I'm trying to use the acf() function to calculate the autocorrelation of each column in a matrix. The trouble is that I can only seem to get the function to work if I extract the data in the column into a separate matrix and then apply the acf() function to this column. I have something like this: acf(mat,lag.max=10,na.action=na.pass) ...but I would really like to apply the

Dear R-ers, I have a large data frame (several thousands of rows and about 2.5 thousand columns). One variable ("group") is a grouping variable with over 30 levels. And I have a lot of NAs. For each variable, I need to divide each value by variable mean - by subgroup. I have the code but it's way too slow - takes me about 1.5 hours. Below is a data example and my code that is too

Hi everyone, I'm running some code containing an outer and inner loop, to fill cells in a 2500x1500 results matrix. I left my program running overnight, and it was still running when I checked 17 hours later. I have tested the operation on a smaller matrix and it executes fine, so I believe there is nothing wrong with the code. I was just wondering if this is normal program execution speed

Hi everybody, I have a small question about R. I'm doing some correlation matrices between my files. These files contains each 4 columns of data. These data files contains missing data too. It could happen sometimes that in one file, one of the 4 columns contains only missing data NA. As I'm doing correlations between the same columns of each files, I get a correlation matrix with a

I would like to cumulatively sum rows in a matrix, in which each row has 1 NA value, which I do NOT want to treat as 0s. The NAs are placeholders where there is actually no data, which is not the same as a 0. The usual "na.rm=TRUE" does not seem to work with the command cumsum. Is there another way to ignore the NAs or do I need to figure out a different way to do this? Here's an

Hi, I guess this might be a FAQ or something, and there's probably a nice simple way to do it, but I can't think of it: Given a matrix, I want to remove columns that are _entirely_ filled with NAs (partial NAs are fine). How please? Thanks, Martin

Hi all! Maybe someone could help me with the following. I know this hasn't directly to do with ecology but I'm also using glm. I have a list of 16 genes and 10 samples. The samples are of two types, 4 Ctrl and 6 Diseased. If, labelInd<-as.factor(c(rep("0",4),rep("1",6))) genes.glm<-glm(labelInd ~ ., family=binomial, data=mat) beeing "mat" the 10x16

Hi This is a difference between the way matrices and data frames work I guess. I want to replace the NA values in a data frame by 0, and the code works as long as the data frame in question actually includes an NA value. If it doesn't, there is an error: df <- data.frame(c1=c(1,1,1),c2=c(2,2,NA)) df[is.na(df)] <- 0 df df <- data.frame(c1=c(1,1,1),c2=c(2,2,2)) df[is.na(df)] <-

Following the setup in Prof.Duncan Murdoch's page, I have successfully compiled the DLL for one Fortran 95 program using Gfortran and got 300 times speed boost. For the second set of fortran programs, However, I have this error message R CMD SHLIB -o jiangang kdtree2.f90 jian.f90 gang.f90 m2c -o jian.o jian.mod make: m2c: Command not found make: *** [jian.o] Error 127 Can anyone

Dear R users, For the moment, I have a script and a function which calculates correlation matrices between all my data files. Then, it chooses the best correlation for each data and take it in order to fill missing data in the analysed file (so the data from the best correlation file is put automatically into the missing data gaps of the first file (because my files are containing missing values