similar to: quantile type 1 perhaps?

Hi everyone, I'm stuck on my dissertation which is due next week. I'm a business major student and my project is a comparative research on corporate social responsibility (CSR) between Chinese and German firms. According to my literature review, it's quite obvious that German firms have much better CSR initiatives and strategies than the Chinese ones and I wanted to test whether it was

I'm not sure who is responsible for quantile(), but I assume they read this list. Ivan Frohne and I have produced a revision of the quantile.default() function which enables the computation of alternative sample quantile definitions. The code and .Rd file are attached. This enables the user to produce quantiles that are equivalent to those in various statistics package. There is a type

Hi, I'm trying to decide between doing a FA or PCA and would appreciate some pointers. I've got a questionnaire with latent items which the participants answered on a Likert scale, and all I want to do at this point is to explore the data and extract a number of factors/components. Would FA or PCA be most appropriate in this case? Cheers, Hannah -- View this message in context:

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

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

Hi there all, This is my first post to the list and I'll first say a few things: - R is great! - The archives of this list have helped me solve all of my problems/questions so far - I only know enough statistics "to be dangerous" I'm looking for a way to do post-hoc tests for the Friedman test. I have a dataset from a within-subjects design with 5 conditions where some of

I have a dataset that does not include native scores, but only serial quantile rankings for a set of units. Clearly these observations are dependent (in that you can't alter one observation without also altering others). Are there methods for dealing with quantile dependent variables. My atempt to find such methods has not bee successful. Any leads to theory, texts or R code would be most

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

Dear friends , I am trying to perform quantile regression analysis to analyse my work. I could install the R package in windows xp. Now I am struggling for the next work.I have *marks of students at the university examinations*( say response variable Y) and their *entrance examination marks* ( Independent one variable X ).I have entered data in Excel spread sheet as records. I will be thankful

Since R is open source and help may come from varied levels of experience on R-Help, I wonder if it might be helpful to construct a method that can be used to "rate" those who provide help on this list. This is something that is done on other comp lists, like http://www.experts-exchange.com/. I think some of the reasons for this are pretty transparent, but I suppose one reason is that

Hi, I have just found this forum, and it looks like a great place to get some help (I hope) For my dissertation, which is due way too soon, I am doing a survey, comparing attitudes of 2 independent groups, with 5 scale likert questions. Basically I want to show if they have similar or different attitudes. I am testing 4 hypotheses, and have in total about 20 questions. I have to say my

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")

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

Hi, I have a series of id's with multiple visits and questionnaire scores. This is a clinical trial that will be analyzed using the last observation carried forward method. In other words, in order to comply with intent to treat analysis when many subjects withdraw, data points for the last visit must be generated and filled in with the last observation. The ultimate goal is to tabulate the

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

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 Patrick Hausmann <c18g at uni-bremen.de> napsal dne 15.11.2007 18:59:06: > Hello Petr, > > one question solved, the next is standing in front of me... If you > have a minute to look .... great! > > > x > V1 V2 F1 > 1 A 2 0.1552277 > 2 A 3 0.1552277 > 3 A 4 0.1552277 > 4 B 3 0.8447723 > 5 B 2 0.8447723 > 6 C 6 0.2500000

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 >

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

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