similar to: Suggestion for quantile.default()

On Fri, May 31, 2019 at 01:28:55AM -0400, Scott Kostyshak wrote: > 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")

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

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

I am working on a two stage threshold quantile regression model in R, and my aim is to estimate the threshold of the reduced-form equation (call it rhohat), and the threshold of the structural equation (call it qhat), in two stages. On the first stage, i estimate rhohat by quantile regression and obtain the fitted values. I use these fitted values to estimate qhat on the second stage. The code is

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

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,

The patch below fixes the NChisquare documentation problem that I've been mentioning for some time now. NOTE: There is one DEQN where the LaTeX part contains real LaTeX code, because I did not see how to get a sum sign (and a roman math font) otherwise. Seems to work, though ... MARTIN? -k ********************************************************************** ***

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 >

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 <-

Dear R-Users and Developpers, Currently R does not include functions to compute the skewness and kurtosis. I programmed it myself in the following way, but probably *real* programmers/statisticians can do that better: mykurtosis <- function(x) { m4 <- mean((x-mean(x))^4) kurt <- m4/(sd(x)^4)-3 kurt } myskewness <- function(x) { m3 <- mean((x-mean(x))^3) skew <-

Hi, I'm working on a data set that contains a couple of factors and a number of dependent variables. From all of these dependent variables I would like to calculate mean, standard deviation and quantiles. With the function FUN I get all the means and stdev that I want but quantiles are only calculated for the first of the dependent variables (column 8 in the summarize command). What do I

Hello all, Thanks in advance for you attention. I would like to generate a third value that represents the quantile value of a variable in a data frame. # generating data x <- as.matrix(seq(1:30)) y <- as.matrix(rnorm(30, 20, 7)) tmp1 <- cbind(x,y) dat <- as.data.frame(tmp1) colnames(dat) <- c("id", "score") dat # finding percentiles of "score"

Peter, thanks, very nice, this will work for me... could you also help with setting up the code to run the on liner "approx(sort(x), seq(0,1,,length(x)), q)$y" on the rows of a data frame using my example above? So if I cbind z and res,? df<-cbind(z,res) the "x" in your one liner would be the first 4 column values of each row and "q" is the last (5fth) column

It would depend on which one of the 9 quantile definitions you are using. The discontinuous ones aren't invertible, and the continuous ones won't be either, if there are ties in the data. This said, it should just be a matter of setting up the inverse of a piecewise linear function. To set ideas, try x <- rnorm(5) curve(quantile(x,p), xname="p") The breakpoints for the

It looks like a homework assignment. It also looks like you didn't read the documentation carefully enough. The 'len.out' argument in seq is solely for specifying the length of a sequence. The 'quantile' function omputes the empirical quantile of raw data in the vector 'x' at cumulative probabilit(y)(ies) given in the weights' argument, with interpolation I'm

For some reason, the help file on quantile() says "Missing values are ignored" in the description of the x argument. Yet this is only true if na.rm=TRUE. I suggest the help file is amended to remove the words "Missing values are ignored". Rob _____________________________ Rob J Hyndman Professor of Statistics, Monash University Editor-in-Chief, International Journal of

Hi r-users, I have a matrix B and a list of 3x3 matrices (mylist). I want to calculate the quantiles in the list using each of the value of B as probabilities. The codes I wrote are: B <- matrix (runif(12, 0, 1), 3, 4) mylist <- lapply(mylist, function(x) {matrix (rnorm(9), 3, 3)}) for (i in 1:length(B)) { quant <- lapply (mylist, quantile, probs=B[i]) } But quant

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,

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