similar to: Percentile rank for each element in list

I have a problem where I need to calculate the corresponding cohort percentile ranks for each of several variables. Essentially, what I need is a function that will calculate the distribution-free percentiles from each variable's data vector, returning a corresponding vector of percentiles: e.g.: percentile.my.data<-/function/(my.data) I tried to make ecdf() perform this task but

Hi, can anyone help me to figure out how to compute the percentile of an individual observation with respect to a reference distribution. What I mean is. Let's assume I have a vector consisting of 10 numbers {3,5,8,1,9,5,4,3,5.5,7} and I want figure out what percentile the number 4.9 corresponds to. I failed to find any reference to such a function, although I would assume this must

Dear all, I am familiar with obtaining the value corresponding to a chosen probability via the quantile function. Now I am facing the opposite problem I have a value an want to know it's corresponding percentile in the distribution. So is there a function for this as well? Thank you for your support in advance, Felix

Hello, I am not sure how to do this in R. Any suggestion would be appreciated. I have a vector of values from where I build an empirical CDF. For example: > x <- seq(1,100) > x <- sample(x,1000,replace=T) > quantile(x,probs=seq(0,1,.05)) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 1.00 5.00 10.00 16.00 20.00 25.00 31.00 36.00 41.00

Hi: I have an array of measurements, that I've been doing linear regression model and AI models on. Because there are many errors and the values are "ill-formed" I would like to copy the array, but replace each value with the PERCENTILE of that value, in the original array. i.e. mesments$V1: 9, 77, -1 would become: mesmentsCopy$V1: 50, 100, 0 The actual array has many more

Dear List, I have some discrete data and want to calculate the percentiles and the percentile ranks for each of the unique scores. I can calculate the percentiles with quantile(). I know that "ecdf" can be used to calculate the empirical cumulative distribution. However, I don't know how to exact the cumulative probabilities for each unique element. The requirement is similar

Given a dataset x, the ecdf is ecdf(x). Then I can use ecdf(x)(y) to find the percentile of y. Given the ecdf is there a way to determine what is the value of y that is the boundary of let's say 95 percentile? In other words, is there a function I can call on the ecdf like: fomeFunc( ecdf( x ), 0.95 ) Which will return the highest value of y, for which ecdf( y ) < 0.95? The only solution

Dear List: I am trying to find the area between two ECDFs. I am examining the gap in performance between two groups, males and females on a student achievement test in math, which is a continuous metric. I start by creating a subset of the dataframe male<-subset(datafile, female="Male") female<-subset(datafile, female="Female") I then plot the two CDFs via

I'm familiar with the quantile() command, but what if I have a specific number that I want to know its location in a vector? I know that in known distributions, (for example the normal distribution), there is pnorm and qnorm, but how can I do it with unknown vector? thanks in advance _________________________________________________________________ Walla! Mail - [1]Get

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"

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

Hi, Is there a function that does percentile-percentile plot. I do not mean the qqplot. I need to plot the percentiles rather than points themselves. I am hoping for a plot that tells me that the x percentile of one data set corresponds to the y percentile of the other. for example a point on the plot of (.5, .2) will tell me that the 50th percentile of the first data and the 20th percentile of

Dear R users, Quick question on the use of ecdf for producing a cumulative distribution function. After having produced a cumulative distribution using ecdf, I would like to extract the exact x-value for a given percentile. For example I can ascertain the 1st and 3rd quartile using the summary function: cawa.cdp <- ecdf(cawaocc$LEFF80) plot(cawa.cdp, ylab="Fn(x)")

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.

> On Jun 15, 2017, at 12:37 PM, Andras Farkas via R-help <r-help at r-project.org> wrote: > > 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

Dear list, I am calculating the 95th percentile of a set of values with R and with SPSS In R: > normal200<-rnorm(200,0,1) > qnorm(0.95,mean=mean(normal200),sd=sd(normal200),lower.tail =TRUE) [1] 1.84191 In SPSS, if I use the same 200 values and select Analyze -> Descriptive Statistics -> Frequencies and under "Statistics", I type in '95' under Percentiles,

Dear All, I have to get the value of say 90th percentile of precipitation time series.. The series is of daily precipitation value of 96 years, I have to to get 90the percentile value of daily precipitation each year. If you know the R code or command for this please let me know. I would appreciate your early response. Thanking you, Sincerely, Ajay.

David, thanks for the response. In your response the quantile function (if I see correctly) runs on the columns versus I need to run it on the rows, which is an easy fix, but that is not exactly what I had in mind... essentially we can remove t() from my original code to make "res" look like this: res<-apply(z, 1, quantile, probs=c(0.3)) but after all maybe I did not explain

Hi folks, I'm trying to estimate bias-corrected percentile (BCP) confidence intervals on a vector from a simple for loop used for resampling. I am attempting to follow steps in Manly, B. 1998. Randomization, bootstrap and monte carlo methods in biology. 2nd edition., p. 48. PDF of the approach/steps should be available here: https://wyocoopunit.box.com/s/9vm4vgmbx5h7um809bvg6u7wr392v6i9 If

I would like to give a probability distribution function of a function of (x,y) on the half-plane y>0, and a constant 0<c<1 and I would like to know the c percentile of the marginal distribution of x. I have tried along the lines of the following but I keep getting errors: # SIMPLIFIED PROBLEM # The plan is to solve for the .975 percentile "xc" of the marginal x distribution