similar to: xlim in lattice

There seems to be 2 functions call ecdf... http://lib.stat.cmu.edu/S/Harrell/help/Hmisc/html/ecdf.html http://127.0.0.1:11885/library/stats/html/ecdf.html How do I get the one ecdf {Hmisc} to run instead of the ecdf {stats} A pointer in the right direction would be greatly appreciated. Tried to instal Hmisc but got this message, so I assume I have it > utils:::menuInstallPkgs() Warning:

Is there an easy way to "thin" a lattice plot? I often create plots from large data sets, and use the "pdf" command to save them to a file, but the resulting files can be huge, because every point in the underlying dataset is rendered in the plot, even though it isn't possible to see that much detail. For example: require(Hmisc) x <- rnorm(1e6)

I have been using the ECDF function in the Hmisc package to produce cumulative distribution function plots. The problem is that for small datasets the steps "look bad" (not my characterization but from the client). Is there a way to get the same information but smoothed? I have tried the densityplot (lattice), which gives a smoothed line, but this does not give the cumulative density.

Hi, Newbie here. I read the R for Beginners but i still don't get this. I have the following data (this is just an example) in a CSV file: courseid numstudents 101 209 141 13 246 140 263 8 321 10 361 10 364 28 365 25 366 23 367

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

Hi, i want to plot empirical cumulative density functions for two variables in one plot. For better visualizing the differences in the two cumulative curves I'd like to log-scale the axis. So far I found 3 possible functions to plot ecdf: 1) ecdf() from the package 'stats'. I don't know how to successfully set the log.scales? Combining two plots is not a problem: plot(ecdf(x1))

Hi All, I have codes as follows to get the ecdf plots: > day.hos2<-c(6,4,6,6,4,6,5,4,7,5,6,6,8,6,17,9,8,4,6,3,5,8,7,12,5,10,6,4,6 ,13,7,6,6,25,4,9,96,6,6,6,6,9,4,5,5,4,10,5,7,6) > day.hos3<-c(5,6,7,6,4,5,6,6,6,6,19,7,5,9,8,8,7,5,6,20,40,5,8,7,7,5,6,13, 11,9,4,6,9,16,6,7,6) > f<-ecdf(day.hos2) > plot(f,col.p='red',col.h='red') >

Hello, I'd like to plot an empirical cumulative distribution function, except instead of the fraction of values < x, I'd like the fraction of values > x. I think this can be done using the ecdf function in {Hmisc}. I installed the package and loaded it. However, when following the example given in the documentation, I get an error: x <- rnorm(100) ecdf(x,what='1-F')

Hi all, given this example #start a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) length(a) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) length(b) out<-ks.test(log10(a+1),log10(b+1)) # max distance D

Hi, I am using ecdf-function and want to use the ecdf()-data-points for nls() as data-parameter. nls() expects 'list' or 'environment' as a data-type, knots(ecdf(mydata)) gives me 'numeric'. What should I do now? Thanks in advance - Jochen Here is the code: ################################################# # --- Fit --- # Gumbel-Dist-Function, cumulative,

Hi r-users, I would like to overlap 2 ecdf plots.  I tried this below and it gives me two plots of ecdf but just both just in black. par(mar=c(4,4,2,1.2),oma=c(0,0,0,0),xaxs="i", yaxs="i") plot(ecdf(datobs)) lines(ecdf(gam_sum_gen)) Then I try to add colors etc and also the legend but fail. par(mar=c(4,4,2,1.2),oma=c(0,0,0,0),xaxs="i", yaxs="i")

I am trying to return the p value for a stat from the ECDF. That is the index of the first occurrence, on an ordered vector, of a value either greater than or equal to a given value. Ideally I would not have to order the vector beforehand. Currently I use: PValue<-function(stat, ECDF){ ###Get the length of the ECFD L<-length(ECDF) ###Loop through the ECDF until the p value is

Dear all, I would like to calculate the complementary cumulative distribution function. As it is known, the ccdf is the 1-ecdf(X)==1-F(x). (From ?ecdf help is shown that ecdf returns a function which is the F(x) I would like to use that function inside my function and after call curve (which accepts function as input). My code looks like that onVector<-seq(1:100)

Hi. I have the following two ecdf plots in one graph: plot( ecdf(....), do.points=FALSE, verticals=TRUE, main=paste("Ecdf of distances ",DIM,sep=""), col="red" ); lines( ecdf(....), do.points=FALSE, verticals=TRUE ); How do I change the color of the resulting graph? Adding col="red" to either plot or lines results in an error

Hello: [Kindly Cc when replying] The question in a nutshell is this: Is there a more robust alternative to Ecdf ()? The details: I've used Ecdf () _a lot_ over the past few years and I have learned to live with its warnings. But I am running short on time and patience now [*] Here is a reproducible example: > library (Hmisc) > x <- read.csv ( file =

Hello, My specific system is > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 5.1 year 2002 month 06 day 17 language R Each of the hmisc and stepfun packages has a function

> 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 just created a utility function that replicates what I have done in the past with Excel or OO.org by putting a formula of the form =sum($A1:A$1) in an upper-corner of a section and then doing a "fill" procedure by dragging the lower-rt corner down and to the right. When divided by the grand sum of the entries this function then calculates a 2D-discrete-ECDF. I

Hi, I have two variables ranging both from 0 to 1 (n=500 each). Now I am interested in plotting them both in one plot (using ggplot2). So far I used ecdf() (from an example I found with google) to get values for the cumulatice distribution function which gives a relative curve. I also want to do the same plot but using absolute cumulative values instead of relative. Can that be done with ecdf or

I have a csv file that has approximately 50k rows. In the first value of each row, a file name is listed, and there are 162 different file names. At the end of each row, there is a number value. What I would like to be able to do is for the 162 different files (or we could call them categories), is compute the ecdf for the values within that category. Then plot the ecdf for each file on the