similar to: bug in apply with median

Seriously, if a method gives a wrong result, it's wrong. line() does NOT implement the algorithm of Tukey, even not after the patch. We're not discussing Excel here, are we? The method of Tukey is rather clear, and it is NOT using the default quantile definition from the quantile function. Actually, it doesn't even use quantiles to define the groups. It just says that the groups

I found a strange inconsistency: If m is a matrix and d is a data frame then - mean(m), median(m), max(m) and max(d) all return a single value but - mean(d) returns the column means - median(d) fails - both var(m) and var(d) return the variance covariance matrix You pretty much have to experiment to figure this out since much of this behavior is not readily obvious from the help files.

Here are some data frames: df3.2 <- data.frame(1:3, 7:9) df4.2 <- data.frame(1:4, 7:10) df3.3 <- data.frame(1:3, 7:9, 10:12) df4.3 <- data.frame(1:4, 7:10, 10:13) df3.4 <- data.frame(1:3, 7:9, 10:12, 15:17) df4.4 <- data.frame(1:4, 7:10, 10:13, 15:18) Now here are some commands and their answers: > median(df3.2) [1] 2 8 > median(df4.2) [1] 2.5 8.5 > median(df3.3)

Hi! Suppose I have a dataset as follows pd = c(10,7,10,11,7,11,7,6,8,3,12,7,7,10,10) I wish to calculate the mean, standard deviation, median, skewness and kurtosis i.e. regular standard statistical measures. average = mean(pd) stdev    = sd(pd) median = median(pd) skew    = skewness(pd) kurt     =  kurtosis(pd) Q. No (1) How do I get these at a stretch using some R package? I came across

This is my reproducible example tv.ms<-structure(list(inq = structure(4:17, .Label = c("D4", "D5", "D6a", "D6b", "D6c", "D7", "D8", "F4", "F5a", "F5b", "F6a", "F6b", "F6c", "F6d", "F7a", "F7b", "F8"), class =

Here is an attached patch. Best, Serguei. Le 29/05/2017 ? 12:21, Serguei Sokol a ?crit : > The problem or actual R implementation relies on an assumption > that median(x[i] | x[i] <= quantile(x, 1/3)) == quantile(x, 1/6) > which reveals not to be true despite very trustful appearance. > > If we continue with the example of x=y=1:9 > then quantile(x, 1/6)=2.5 (here quantile()

<<insert bug report here>> > apply(array(1:20,c(2,2,5)),2:3,function(x) x) Error: length of dimnames must match that of dims > Changing: dimnames = if (is.null(dn.ans)) list(ans.names, NULL) else c(list(ans.names), dn.ans) To: dimnames = if (length(dn)==0) NULL else if (is.null(dn.ans)) list(ans.names, NULL) else c(list(ans.names), dn.ans) seems to fix this. Chuck

Dear Developers, I've been struggling through writing R extension in C. I've been using an external pointer to store my data (please see sample below). I encountered a very weird erroneous behaviour: when I tried to use my external pointer to a structure holding several types of data, including SEXPs, I discovered that SEXPs change their types between returning from initialization

In do_matrix in src/array.c there is a type switch containing : case LGLSXP : for (i = 0; i < nr; i++) for (j = 0; j < nc; j++) LOGICAL(ans)[i + j * NR] = NA_LOGICAL; That seems page inefficient, iiuc. Think it should be : case LGLSXP : for (j = 0; j < nc; j++) for (i = 0; i < nr; i++) LOGICAL(ans)[i + j * NR] = NA_LOGICAL; or more simply : case

Dear useRs, Given this code I end up with a list of class "by": a <- sample(1:5,200,replace=TRUE) b <- sample(c("v1","v2","v3"),200,replace=TRUE) c <- sample(c(11,22,33),200,replace=TRUE) data <- runif(200) grouped <- by(data,list(a,b,c),function(x) {c(min=min(x),max=max(x), median=round(median(x),digits=2),mean=round(mean(x),digits=2))})

OTOH, > sapply(1:9, function(i){ + sum(dfr$time <= quantile(dfr$time, 1./3., type = i)) + }) [1] 8 8 6 6 6 6 8 6 6 Only the default (type = 7) and the first two types give the result lines() gives now. I think there is plenty of reasons to give why any of the other 6 types might be better suited in Tukey's method. So to my mind, chaning the definition of line() to give sensible

Hi, I've got two suggestions how to speed up median() about 50%. For all iterative methods calling median() in the loops this has a major impact. The second suggestion will apply to other methods too. This is what the functions look like today: > median function (x, na.rm = FALSE) { if (is.factor(x) || mode(x) != "numeric") stop("need numeric data")

Hi, I noticed that by() returns an object of class 'by', regardless of what its argument 'simplify' is. ?by says that it always returns a list if simplify=FALSE, yet by.data.frame shows: ---<--------------------cut here---------------start------------------->--- function (data, INDICES, FUN, ..., simplify = TRUE) { if (!is.list(INDICES)) { IND <-

Hi, I need to extract the second largest element from each row of a matrix. Below is my solution, but I think there should be a more efficient way to accomplish the same, or not? set.seed(1) a <- matrix(rnorm(9), 3 ,3) sec.large <- as.vector(apply(a, 1, order, decreasing=T)[2,]) ans <- sapply(1:length(sec.large), function(i) a[i, sec.large[i]]) ans Thanks in advance for your

Perhaps I missed something obvious, but when I applied patch 2 and then recompiled R-0.49, I found that the objects such as glm.fit that were supposed to be updated still had their original definitions. After some hair-pulling, I remembered that patch saves a backup copy of the original files it changes. Because of the way the makefile for the system database is structured, both the new files and

for months, we keep fighting issues with smbd (now at released 2.2.6), config is with spoolss, domain logon, and the client in question is an NT4 machine. server is a i386/linux 2.2.19 For a very long time, we thought it was related to oplocks, but now after having them disabled, it still is there. Level 3 Log is as follows: [2002/10/22 17:08:18, 3] smbd/ipc.c:reply_trans(480) trans

hi, Just a very simple question: is there an R equivalent to the matlab command find(X) which returns the indices of vector X that store non-zero elements? e.g. > find( [1 0 0 1 0]) ans = 1 4 so, in R, how do I do: ans <- rfind( c(1,0,0,1,0)) so that ans is the vector c(1,4) thanks, stephen -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help

I think that the "dimname names" of tables and arrays could make aperm() and apply() (and probably some other functions) easier to use. (dimname names are, for example, created by table() ) The use would be something like: -- x <-table( from=sample(3,100,rep=T), to=sample(5,100,rep=T)) trans <- x / apply(x,"from",sum) y <- aperm( trans,

Full_Name: Roger Bivand Version: 0.99.0 OS: RH Linux 6.1 Submission from: (NULL) (158.37.60.152) I am working on an interface between R and the GRASS geographical information system, written in R, with no dynamically loaded code. I have written full examples, and tested then under R 0.90.1, both by entering example() for each function and R CMD check, both of which worked without problem. Under

Hi, I wonder whether the mechanism of parsing parameters to functions has changed between 0.63.1 and 0.63.3? The following code yeils different results in R 0.63.1 (Version 0.63.1 (Dec 5, 1998)) and R 0.63.3. cave<-function(x,a,b) { return(c(mean(x[a],na.rm=T),mean(x[b],na.rm=T))) } datx <- data.frame(rbind(c(1,2,3,4),c(4,5,6,7)))