similar to: Negative Quartile

I have data that looks like lake,loglength,logweight 1,2.369215857,1.929418926 1,2.426511261,2.230448921 1,2.434568904,2.298853076 1,2.437750563,2.298853076 1,2.442479769,2.230448921 1,2.445604203,2.356025857 ... 102,2.722633923,3.310268367 102,2.781755375,3.502153893 102,2.836324116,3.683407299 102,2.802773725,3.583312152 102,2.790285164,3.546419267 102,2.806179974,3.599118565

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

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, 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

Hello, I am having some issues with the order write.table. The fact is that I need to use "," as the decimal character and not "." as default, and when I use: write.table(Sales,file="Sales.xls",quote=FALSE, sep = "\t", dec = ",", row.names = FALSE, col.names = TRUE) It does it perfectly, but then, in a different part of the code:

I was hoping to calculate the percentile for each date. So group all of one date together, calculating the value of the 2.5 and 97.5 percentile. -- View this message in context: http://r.789695.n4.nabble.com/Calculating-percentiles-for-multiple-dates-tp4638183p4638378.html Sent from the R help mailing list archive at Nabble.com.

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,

Hi All: I am working with a dataset on Arsenic toxicity, and I am trying to calculate the 20th, 40th, 60th, 80th, and highest percentiles for a variable, dietary Moisture (variable name dMoist). The inbuilt function quantile(dMoist) would print 0, 25th, 50th, 75th, and 100th percentile. Does there exist a function that can calculate xth percentile (where x = 10th, 20th, ... etc) values?

Hi, I am using the package "demography" from Rob Hyndman for the Lee-Carter-Model. It is an amazing powerful tool but I am struggling with one issue: I want to compute different percentiles of the survival probability distribution derived from the Lee-Carter-Forecast (e.g. the 50%tile, 60%tile, 75%tile and 99%tile) for each of the next 10 years. Is there any possibility to retrieve

I have a 20x3 matrix as follows: > m <- replicate(3, matrix(rnorm(20),20,1)) I need to compute, say, 95th and 99th percentiles of each column such that the resulting matrix becomes 2x3 with each row representing the respective percentile. My "best effort" is to compute one column at a time as follows: > quantile(m[,1], c(0.95, 0.99)) To do the same for columns 2 and 3, I

I am looking to create what I would call a "simple variation" on the boxplot. What I would like to do is to be able to plot the upper and lower confidence limits as the "box" and the 10th and 90th percentile as the whiskers. What I have done is write the code to create a dataframe, the columns of which are the mean, sd, 10th percentile, 90th percentile, lower confidence limit

Hi all, I have a problem on how R calculates Percentiles : Suppose I have following data set: > data1 [1] -16648185 -14463457 -14449400 -13905309 -13443436 -13234755 -12956282 -11660896 [9] -10061040 -9805005 -9789583 -9754642 -9562164 -9391709 -9212182 -9151073 [17] -9092732 -9068214 -8978151 -8943912 -8761890 -8632106 -8541580 -8501249 [25] -8234466 -8219015

Hi there, How accurate is the aproximation R makes to the Chi-Square distribution? For example, if I run: > qchisq(1/1000000,6) [1] 0.03650857 how accurate is 0.0365 compared to the theoretical percentile? What kind of approximations have been made in the software's algorithm? It woudl be useful to know since I am working with tiny percentiles such as one one-millionth and one

The standard R boxplot appears to use quartiles to determine the height of the rectangles and a range parameter - RNG - (default=1.5 I think) that determines the length of the whiskers as <= RNG x Interquartile Range. Is it possible to instead define the range as extending to the 95th percentile? If so, how would this be done? nb, I'm plotting multiple boxplots on a single chart so a

Greetings Everybody: I generated a 1.2MB dta file based on the general social survey with Stata8 for linux. The file can be re-opened with Stata, but when I bring it into R, it says all the values are missing for most of the variables. This dataset is called "morgen.dta" and I dropped a copy online in case you are interested http://www.ku.edu/~pauljohn/R/morgen.dta looks like this

Hi Has someone got a package/script at hand to do a historical simulation to calculate the Value at Risk? If your not sure what Historical Simulation is: In simple terms, Historical Simulation (HS) is just taking sample percentiles over a moving sample. Suppose we want to use HS to predict a portfolio's Value-at-Risk at a confidence level of 99 percent and the window size is chosen to be 250

2017-10-10 8:25 GMT+02:00 Karan Sandha <ksandha at redhat.com>: > Hi Gandalf, > > We have multiple tuning to do for small-files which decrease the time for > negative lookups , meta-data caching, parallel readdir. Bumping the server > and client event threads will help you out in increasing the small file > performance. > > gluster v set <vol-name> group

Dear R users, I am having trouble devising an efficient way to run a loess() function on all columns of a data.frame (with the x factor remaining the same for all columns) and I was hoping that someone here could help me fix my code so that I won't have to resort to using a for loop. (You'll find the upcoming lines of code in a single block near the end of the message.) Here's a

Hi All, I am looking at applications of percentiles to time sequenced data. I had just been using the quantile function to get percentiles over various periods, but am more interested 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

I just tried setting: performance.parallel-readdir on features.cache-invalidation on features.cache-invalidation-timeout 600 performance.stat-prefetch performance.cache-invalidation performance.md-cache-timeout 600 network.inode-lru-limit 50000 performance.cache-invalidation on and clients could not see their files with ls when accessing via a fuse mount. The files and directories were there,