search for: quantiles

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

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")

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 &lt...

...-------------------------------------------------------- Saji Ren from Shanghai China GoldenHeart Investment Group ------------------------------------------------------------------ -- View this message in context: http://n4.nabble.com/Confusion-in-quantile-and-getting-rolling-estimation-of-sample-quantiles-tp1015893p1015893.html Sent from the R help mailing list archive at Nabble.com.

...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 argument that allows one to choose between the various sample quantile methods. type=7 gives identical results to the current R function quantile.default(). In our revised function, type=8 is the default following the r...

Hi, so I don't speak computer and I have no idea what this code is telling the program to do, but I apparently need to be able to find and isolate influencial observations. Problem, I have no idea what the error means and where it may be from in the code. error I get is below the code { ## OLS results NameC<- lm(gpanew~female+female:lastinit+agenew+canadian+mom_ed+yearstudy) ## default:

...864 105 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 quantile by l.c.1 so I use tapply(x, l.c.1, quantile) and my output includes all quantiles (i.e., 0, 25%, 50%, 75%, 100%) but I'm only interested in the 75% quantile. Is there an additional statement or function I can use to get just the quantile that I want? Thanks for your help - SR Steven H. Ranney

...<- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) ) So I got the mean, median std dev, quantiles etc. IS there any way I can add the mode to the mixt. Thanks ahead for any suggestions. -- View this message in context: http://r.789695.n4.nabble.com/Can-package-plyr-also-calculate-the-mode-tp4663235.html Sent from the R help mailing list archive at Nabble.com.

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

Hi, In R-devel (2020-05-17 r78478) quantile() type 1 seems to behave a little bit strange for some ordered factors: quantile(factor(1:3, ordered = TRUE), 0.5, type = 1) returns ?2? as expected. But quantile(factor(2:4, ordered = TRUE), 0.5, type = 1) returns ?4? and quantile(factor(3:5, ordered = TRUE), 0.5, type = 1) returns ?NA?. Furthermore, the function returns ?NA? for calls like

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, 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 points of the...

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.

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 b...

...ted in if there is an accepted (and/or R-implemented) method to apply weighting to the data so as to weigh recent data more heavily. I wrote the following function, but it seems quite inefficient, and not really very flexible in its applications - so if anyone has any suggestions on how to look at quantiles/percentiles within R while also using a weighting schema, I would be very interested. Note - this function supposes the data in X is time-sequenced, with the most recent (and thus heaviest weighted) data at the end of the vector WtPercentile <- function(X=rnorm(100), pctile=seq(.1,1,.1)) { X...

bradleyd wrote > Excuse the request from an R novice! I have a data frame (DATA) that has > two numeric columns (YEAR and DAY) and 4000 rows. For each YEAR I need to > determine the 10% and 90% quantiles of DAY. I'm sure this is easy enough, > but I am a new to this. > >> quantile(DATA$DAY,c(0.1,0.9)) > 10% 90% > 12 29 > > But this is for the entire 4000 rows, when I need it to be for each YEAR. > Is there no way to use a "by" argument in the quantil...

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 = 7.6...

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 have to do differently in order to get all the quantiles that I want? Thanks, Kim sgldm2 <- read.t...

Hi All, I want to create a categorical variable, cat.pfoa, in the file of pfas.pheno (a data frame) based on log2pfoa values. I can do it using the following code. pfas.pheno <-within(pfas.pheno, {cat.pfoa<-NA cat.pfoa[pfas.pheno$log2pfoa <=quantile(pfas.pheno$log2pfoa,0.25, na.rm =T)]<-0 cat.pfoa[pfas.pheno$log2pfoa >=quantile(pfas.pheno$log2pfoa,0.75, na.rm =T)]<-2

...42135.6 18458.85 Numeric,5 CAPITAL FEDERAL 536152.3 26888.67 Numeric,5 CATAMARCA 11409.81 5415.242 Numeric,5 .... Cuando en realidad en lugar de [,3] debería decirme los valores correspondientes a los quantiles que le solicité, ¿verdad? Por defecto, with(ProduccionAC, tapply(Costo, Provincia, quantile, probs = c(0.25, 0.75, 0.85, 0.90, 0.95))) es un "array". Si lo fuerzo como as.data.frame, resulta algo extraño: with(ProduccionAC, tapply(ProduccionAC$Costo, ProduccionAC$Provincia, BUE...