similar to: CDF plot

Hello all, I'm looking for the R-equivalent of the S-option "Connect type: half horiz first". Link: http://miner.stern.nyu.edu/Splus/help/guihelp/__hhelp/connect_type.htm I'm plotting with type="s" or type="S"; this is giving me a stairstep starting, or ending with the value on the x-axis (as documented). But, I want the x-value in the middle of the step,

Is there a function in R for calculating empirical cumulative distribution functions, i.e. the inverse of the quantile function? Perhaps in some library? I''d hate to have to re-invent the wheel. David Edwards, Biostatistics, Novo Nordisk A/S, Bagsværd, Denmark. DEd@novonordisk.com <mailto:DEd@novonordisk.com> Tlf: +45 44 42 62 35. Fax: +45 44 42 14 80 [[alternate HTML version

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in

Good Afternoon, I'm trying to create a cdf plot, with the following code. It works well, but I have little doubt, if you can help solve. When I create the plot, like the graph line would still not appear with point #cdf x<-table(Dataset$Apcode) View(s) hist(s) *plot(ecdf(x))* x<-1 37607 2 26625 3 5856 4 25992 5 30585 6 16064 7 9850 .. ... .. 186 52 -- View this message in

Hi all, is there any package in R capable of smooth approximation of CDF basing on given sample? (Thus, I am not speaking about ecdf) In particular, I expect very much that the approximation should subject to the property: f(x0)<=f(x1) for x0<x1, where x0 and x1 belong to range of the sample given. Polynomial approximation could be OK for me as well. P.S.

Dear All, I am considering to buy a workstation. For the CPUs, I wonder whether anybody have the experience in choosing one for the R. Intel Xeon W3540 2.93 8MB/1066 QC CPU is much cheaper as compared with the Intel Xeon E5540 2.53 8MB/1066 QC CPU. However, its Hz 2.93 is bigger than 2.53. I wonder which one would run R quicker. Thank you. Huang [[alternative HTML version deleted]]

hi y'all, I am wondering if there is any special command, function, package, etc to help me doing a cumulative distribution function, with y-scale - probability scale. I tried the help in R and i got the following answers: cumsum(base) Cumulative Sums, Products, and Extremes ecdf(stepfun) Empirical Cumulative Distribution Function cpgram(ts) Plot

Dear R developer, I am not quite sure, if I should post my concern as a wish to r-bugs at r-project.org. Thus, as recommended, I first send an email to you. My request is the following: I would appreciate, if it was possible to obtain a plot of a 'stepfun' with a strict interpretation of xlim. What I mean: sf <- stepfun(1:4, 1:5) plot(sf, xlim=c(0,10)) does not bound the function to

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

Hi everyone, I have what may appear to be a newbie question, but I have looked everywhere I can think to look and I cannot find an answer. On page 35 of "An Introduction to R" the following command appears: plot(ecdf(eruptions), do.points=FALSE, verticals=TRUE). What is the do.points argument? I know what it does (suppresses printing of the points) but where can I find help on it?

Dear members, I would like a help for extracting the values from a step function (stepfun). >From help(stepfun) we have the following example: Y0<-c(1.,2.,4.,3.) y0<-c(1.,2.,3.,4.) sfun<-stepfun(1:3,y0,f=0) plot(sfun) Now, suppose instead I was given the object (*sfun*, say) from which I wanted to extract the values generated by the function *stepfun*. More precisely, I want to

Hi, I was trying to draw an empirical distribution function with uniform confidence bands. So I tried to find a way to calculate values of the Kolmogorov-Smirnov Distribution but failed. I guess it must be hidden somewhere (since the ks-test is implemented), but I was unable to find it. Is there any way to do this? Thanks Leif Boysen

Hi, are there any function to plot the empirical probability distribution function? I just don't want to reinvent the wheel... Best wishes, Ott -- Ott Toomet PhD Student Dept. of Economics ?rhus University Building 322 Universitetsparken 8000 ?rhus C Denmark otoomet (a) econ au dk ph: (+45) 89 42 20 27 ------------------------------------------- (o_ (*_ (O_

dear all. Would someone be kind and willing to explain the code below for a person who has never used R? ( that is if one has enough time and inclination) It implements gillepsie's stochastic algorithm for Lotka Volterra model. What would help me tremendously is to see the breakdown of the line by line code into plain english. thanks for any insights or other comments. sean

I have istalled R with the rpm package available for RedHat 9.0. My machine is a pentium4. This is my .Rprofile file ( placed in my home directory ): w <- function ( x, y ) write.table ( t ( x ), file = y, quote = F, col.names = F, row.names = F, sep = ',' ) q <- function () q ( save = 'no' ) library( stepfun ) library ( MASS ) The MASS library is not loaded when R is

i've noticed a strange problem when plotting a stepfun. according to the documentation, the xlim parameter should bound the range of the function being plotted, and is returned as the extreme two values (i.e. first and last) in the vector t from the plot.stepfun call. instead, it plots beyond the desired range (although the limits are preserved for the viewing space). to reproduce: foo

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

I want to use a function as an argument to ingtegrate it twice. See the following (senseless) example of a double integration: test<-function(sf,lo,up,rest) { innerFkn<-function(sf,lo) { inte=integrate(f=sf,lower=lo,upper=4) return( inte$value ) } integral=integrate(f=innerFkn,lower=1,upper=2,sf=sf,lo=lo,up=up) return( integral$vlaue+rest ) }

> Almost any method of fitting a density estimate would work on > integrating (numerically) the result. it is a nice idea concerning the monotony property, which will be obtained automatically, but I am going to use results of approximation analytically > In particular, look at package polspline, where > p(old)logspline does the integration for you. thank you, I am going to

Greetings, I have two questions that I could not answer from the documentation. A - ecdf and confidence intervals : Is there a (simple) way to generate confidence intervals (95%) for a ecdf? B - cross-validation of rpart trees : a colleague is using S to generate decision tree and mentioned to me the use of cross-validation. Is this function enabled in R ? if so, how should one proceed to