similar to: Re place only first NA in column

Dear All, I wrote a function for cluster analysis to compute cophenetic correlations between dissimilarity matrices (using the VEGAN library) and cluster analyses of every possible clustering algorithm (SEE ATTACHED) http://old.nabble.com/file/p26288610/cor.coef.R cor.coef.R . As it is now, it is extremely long, and for the future I was hoping to find a more efficient way of doing this sort of

Dear R helpers, I wish to use the "sum" operator for each row of a data frame. However, it appears that the operator acts on the entire data frame, over all columns. What is the best way to obtain row- wise operation? The following code shows my attempts so far, and their problems:- test1=array(rbinom(120,1,0.5),c(20,3)) test1[,3]=NA sum(test1[,1:2]) test1[,3][sum(test1[,1:2])>=2]=1

Dear All, I am trying to create an artificial binary matrix such that each cell has a probability of 0.048 of having a 1. So far the closest I've come is us by using a random poisson distribution with a mean of 0.048, but I can't figure out how to limit the max value to 1. Otherwise that would work fine it seems. Any suggestions? The main code I've got to create said matrix so far

Dear ExpRts, I would like to perform a function with two arguments over the rows of two matrices. There are a couple of *applys (including mApply in Hmisc) but I haven't found out how to do it straightforward. Applying to row indices works, but looks like a poor hack to me: sens <- function(test, gold) { if (any(gold==1)) { sum(test[which(gold==1)]/sum(which(gold==1))) } else NA }

[Apologies to Gabor, who I sent a personal copy of the reply erroneously instead of posting to List directly] [...] > Perhaps what you really intend is to > take the average over those elements in each row of the first matrix which correspond to 1's in the second in the corresponding > row of the second. In that case its just: > > rowSums(newtest * goldstandard) /

Dear list users, let me ask you this trivial question. I worked on that for a long time, by now. Suppose to have a data frame with NAs and to sum some columns with rowSums: df <- data.frame(A = runif(10), B = runif(10), C = rnorm(10)) df[1, ] <- NA rowSums(df[ , which(names(df) %in% c("A","B"))], na.rm=T) If all the elements of the selected columns are NA, rowSums

In R 3.3.3, I observe the following on Ubuntu 16.04 (when building from source as well as for the sudo apt r-base build): > x <- c(NA, NaN) > mean(x) [1] NA > mean(rev(x)) [1] NaN > rowMeans(matrix(x, nrow = 1, ncol = 2)) [1] NA > rowMeans(matrix(rev(x), nrow = 1, ncol = 2)) [1] NaN > .rowMeans(x, m = 1, n = 2) [1] NA > .rowMeans(rev(x), m = 1, n = 2) [1] NaN >

What do you mean by "should not"? NULL means "missing object" in R. The result of the sum function is always expected to be numeric... so NA_real or NA_integer could make sense as possible return values. But you cannot compute on NULL so no, that doesn't work. See the note under the "Value" section of ?sum as to why zero is returned when all inputs are removed.

Should not the result be NULL if you have removed the NA with na.rm=TRUE ? B. > On Mar 21, 2018, at 11:44 AM, Stefano Sofia <stefano.sofia at regione.marche.it> wrote: > > Dear list users, > let me ask you this trivial question. I worked on that for a long time, by now. > Suppose to have a data frame with NAs and to sum some columns with rowSums: > > df <-

On 21/03/2018 11:44 AM, Stefano Sofia wrote: > Dear list users, > let me ask you this trivial question. I worked on that for a long time, by now. > Suppose to have a data frame with NAs and to sum some columns with rowSums: > > df <- data.frame(A = runif(10), B = runif(10), C = rnorm(10)) > df[1, ] <- NA > rowSums(df[ , which(names(df) %in%

No. The empty sum is zero. Adding it to another sum should not change it. Nothing audacious about that. This is consistent; other definitions just cause trouble. -pd > On 21 Mar 2018, at 18:05 , Boris Steipe <boris.steipe at utoronto.ca> wrote: > > Surely the result of summation of non-existent values is not defined, is it not? And since the NA values have been _removed_,

Surely the result of summation of non-existent values is not defined, is it not? And since the NA values have been _removed_, there's nothing left to sum over. In fact, pretending the the result in that case is zero would appear audacious, no? Cheers, Boris > On Mar 21, 2018, at 12:58 PM, Jeff Newmiller <jdnewmil at dcn.davis.ca.us> wrote: > > What do you mean by

Dear everyone, I have looked all over the internet but I cannot find a way to solve my problem. In my data I want to sum a couple of variables. Some of these variables have NA values, and when I add them together, the result is NA dat <- data.frame( id = gl(5,1), var1 = rnorm(5, 10), var2 = rnorm(5, 7), var3 = rnorm(5, 6), var4 = rnorm(5, 3), var5 = rnorm(5, 8) ) dat[3,3] <- NA dat[4,5]

I see: consistency with additive identity. That makes sense. Thanks. B. > On Mar 21, 2018, at 1:22 PM, peter dalgaard <pdalgd at gmail.com> wrote: > > No. The empty sum is zero. Adding it to another sum should not change it. Nothing audacious about that. This is consistent; other definitions just cause trouble. > > -pd > >> On 21 Mar 2018, at 18:05 , Boris

On Fri, Mar 31, 2017 at 10:14 PM, Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote: > From ?NA > > Numerical computations using ?NA? will normally result in ?NA?: a > possible exception is where ?NaN? is also involved, in which case > either might result. > > and ?NaN > > Computations involving ?NaN? will return ?NaN? or perhaps ?NA?: >

Hi I have a data frame df with 3 columns. Some rows are NA across all 3 columns. How can I remove rows with NA across all columns? df=data.frame(col1=c(1:3,NA,NA,4),col2=c(7:9,NA,NA,NA),col3=c(2:4,NA,NA,4)) Thanks Joseph ____________________________________________________________________________________ Be a better friend, newshound, and [[alternative HTML version deleted]]

"I see: consistency with additive identity. " Ummm, well: > 1+NULL numeric(0) > sum(1,NULL) [1] 1 Of course, there could well be something here I don't get, but that doesn't look very consistent to me. However, as I said privately, so long as the corner case behavior is documented, which it is, I don't care. Cheers, Bert Bert Gunter "The trouble with

Dear list, as a result of a logical operation I want to assign a new variable to a DF with NA-values. z <- data.frame( x = c(5,6,5,NA,7,5,4,NA), y = c(1,2,2,2,2,2,2,2) ) p <- (z$x <= 5) & (z$y == 1) p z[p, "p1"] <-5 z # ok, this works fine z <- z[,-3] p <- (z$x <= 5) & (z$y == 2) p z[p, "p2"] <-5 z # this failed... - how

Hi- General question: I have written a function that performs a permutation test. I use the function "sample" to randomize my data, then I call on another function I wrote to analyze the randomized data. This process repeats "n" times-- 10, 100 or whatever. The function applied to the randomized data creates a matrix and sometimes the matrix contains NaN's. I would

Dear R-users,  I have a problem, I have the following dataframe:   d<-data.frame(  'y1'=c(1,2,1,2,1,NA,NA), 'y2'=c(1,2,1,1,1,2,1), 'y3'=c(1,NA,1,NA,NA,2,1), 'y4'=c(NA,2,NA,1,1,2,NA), 'a'=c(1,1,1,1,1,1,2) ) where the last variable counts the number of missing values in a row. Now, i want to set rows where a>1 to NA and arrive at something like the