similar to: quantile() type 1 for some ordered factors in R-devel

Hi Kurt, Thank you for fixing quantile(). However, do you think c.factor() can potentially break more functions? For example, with this new change, classification from the partykit package using predict() comes back NA because of this: https://github.com/cran/partykit/blob/597245ef3dfc98411ce919b74c68ba565f077c47/R/party.R#L500 I understand that most of the fixes will probably be simple with

I am calling quantiles as follows. I don't understand why sometimes the columns (data values) above 95% are returned as "NULL"!! When I drop the percentile down to 92%, I see colums appearing. Why would any quantile be empty? I see sometimes that 95% percentile is being chosen as "1" for my data between 0 and 1, where obviously there's no column value equal to 1. But

Is it a bug that quantile() can return a lower sample quantile for a higher probability? > ##### quantile returns decreasing results with increasing probs (data at the end of the message) > quantile(x2, (0:5)/5) 0% 20% 40% 60% 80% -0.0014141174 -0.0009041968 -0.0009041968 -0.0007315023 -0.0005746115 100% 0.2905596324 >

I do: x<-rnorm(1000) quantile(x,c(.025,.975)) 2% 98% -1.844753 1.931762 Since I want to find a 95% confidence interval, I take the .025 and .975 quantiles. HOWEVER R says I have the 2% (not 2.5%) and 98% (not 97.5%) points. Is it just rounding the printed 2% and 98%, or is it REALLY finding .02 and .98 points instead of .025 and .975? Thanks for any help. Bill Simpson

I do: x<-rnorm(1000) quantile(x,c(.025,.975)) 2% 98% -1.844753 1.931762 Since I want to find a 95% confidence interval, I take the .025 and .975 quantiles. HOWEVER R says I have the 2% (not 2.5%) and 98% (not 97.5%) points. Is it just rounding the printed 2% and 98%, or is it REALLY finding .02 and .98 points instead of .025 and .975? Thanks for any help. Bill Simpson

Greetings, I am having a problem with DescTools::Quantile (a function computing quantiles from weighted samples): # these sum to one probWeights = c( 0.0043, 0.0062, 0.0087, 0.0119, 0.0157, 0.0204, 0.0257, 0.0315, 0.0378, 0.0441, 0.0501, 0.0556, 0.06, 0.0632, 0.0648, 0.0648, 0.0632, 0.06, 0.0556, 0.0501, 0.0441, 0.0378, 0.0315, 0.0257, 0.0204, 0.0157, 0.0119, 0.0087,

Dear all, from the help page of quantile: "x ??? numeric vectors whose sample quantiles are wanted. Missing values are ignored." from the help page of IQR: "x ??? a numeric vector." as a matter of facts it seems that both quantile() and IQR() do not check for the presence of a numeric input. See the following: set.seed(11) x <- rbinom(n=11,size=2,prob=.5) x <-

I'm trying to work out the simplest way to remove the upper and lower quantiles, in this case upper and lower 25% from an array. I can do it in two steps but when I try it in one, it fails. Is there something simple missing from my syntax or are there other simple elegant way to accomplish this? Thanks J > i <-1:20 > i2 <- i[i<quantile(i,.75)] > i3 <-

Guys: 1).When I using the 'quantile' function, I get really confused. Here is what I met: > x<-zoo(rnorm(500,0,1)) > quantile(x,0.8) 400 1.060258 > c=rnorm(500,0,1) > quantile(c,0.8) 80% 0.9986075 why do the results display different? Is that because of the different type of the class? 2).And I want to use the 'rollapply' function to compute a

Dear R-users, I don't know if there is a problem in wtd.quantile (from library "Hmisc"): -------------------------------- x <- c(1,2,3,4,5) w <- c(0.5,0.4,0.3,0.2,0.1) wtd.quantile(x,weights=w) ------------------------------- The output is: 0% 25% 50% 75% 100% 3.00 3.25 3.50 3.75 4.00 The version of R I am using is: 2.1.0 Best,Jing

All - I have an example data frame x l.c.1 43.38812035 085 47.55710661 085 47.55710661 085 51.99211429 085 51.99211429 095 54.78449958 095 54.78449958 095 56.70201864 095 56.70201864 105 59.66361903 105 61.69573564 105 61.69573564 105 63.77469479 115 64.83191994 115 64.83191994 115 66.98222118 115 66.98222118 125 66.98222118 125 66.98222118 125 66.98222118 125 and I'd like to get the 3rd

The attached patch adds some sanity checks to the "type" argument of quantile(). Output from the following commands show the change of behavior with the current patch: vec <- 1:10 quantile(vec, type = c(1, 2)) quantile(vec, type = 10) quantile(vec, type = "aaa") quantile(vec, type = NA_real_) quantile(vec, type = 4.3) quantile(vec, type = -1) Current behavior

Hi, A simple quantile question: I need to calculate the 95% and 5% quantiles (aka percentiles) for the following data: 67.12 64.51 62.06 55.45 51.41 43.78 10.74 10.14 if I use the formula: 95% quantile point= 95 (8+1)/100, I get the 8.55th point as the 95% quantile. Which does not make too much sense as I have only 8 data points. The other option is to use (95*8)/100 = 7.6th data point (which can

Dear All, we have: t<-seq(0,24,1) a<-10*exp(-0.05*t) b<-10*exp(-0.07*t) c<-10*exp(-0.1*t) d<-10*exp(-0.03*t) z<-data.frame(a,b,c,d) res<-t(apply(z, 1, quantile, probs=c(0.3))) my goal is to do a 'reverse" of the function here that produces "res" on a data frame, ie: to get the answer 0.3 back for the percentile location when I have

Simple Question I have 100x100 matrix and I want to calculte each row's 30,50% quantile ex) a=matrix(rnorm(10000),100,100) quantile(a[1,],c(0.3,0.5)) quantile(a[2,],c(0.3,0.5)) . . . . I want get results at once. so I try quantile(a[1:100,],c(0.3,0.5)) but I can get what I exactly want. How can I calculte that? -- View this message in context:

Hi all, I am an R newbie trying to do some calculations I do in Mathematica in R on a GNU/Linux system. The main thing I am interested in doing is taking a 0.999 quantile on a data set in a file who's data is normally distributed, say foo.csv. e.g in Mathematica if I have something like this : a=Import["foo.csv"] b=Transpose[a][[1]] Quantile[b,0.999] In R I can load all the data

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

Hi all, I am running into a snag using quantile function in stats. Basically, I don't understand why the loop below throws the error that it does. test.data <- rnorm(1000, 0, 1) for (i in seq(0.00001, 0.001, 0.00001)){ test <- quantile(test.data, probs=seq(0,1,i)); print(i); } It runs fine from 1e-05 to 0.00024, but then throws the error Error in quantile.default(test.data,

To calculate Percentile for a set of observations Excel has percentile() function. R function quantile() does the same thing. Is there any significant difference btw percentile and quantile? Regrads, -- View this message in context: http://www.nabble.com/Diff-btw-percentile-and-quantile-tp22328375p22328375.html Sent from the R help mailing list archive at Nabble.com.

Is the following behaviour of the quantile function what one would expect? > a <- 1:100 > quantile(a,.6) 60% 60.4 Philippe -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the