similar to: CDF calculation from kernel density estimates for a 324X 15 matrix

I'm trying to register with net2phone. I've already changed chan_sip.c, User-Agent: string to say "User-Agent: Cisco ATA 186 v2.16 ata18x (030401a)". But still I'm getting the error msg. Here is the debug msg: IP Address is xxx.xxx.xxx.xxx 11 headers, 0 lines Reliably Transmitting: REGISTER sip:66.33.146.12 SIP/2.0 Via: SIP/2.0/UDP

note: whole function is below- I am sure I am doing something silly. when I use it like USGS(input="precipitation") it is choking on the precip.1 <- subset(DF, precipitation!="NA") b <- ddply(precip.1$precipitation, .(precip.1$gauge_name), cumsum) DF.precip <- precip.1 DF.precip$precipitation <- b$.data part, but runs fine outside of the function: days=7

Hi everybody, I tried to analyze a custom Affymetrix 3'-biased Array. So I wanted to make a cdf package. (My CDF file size is 1.12Go). I tried several methods but the same error occured Method 1 > #Set the working directory > setwd("D:/Analyse R/Cel files") > #library to create cdf env > library("makecdfenv") >#Create cdf environment >pkgpath

Hi all, I'm working with data to which I'm fitting three-parameter weibull distributions (shape, scale & shift). The data are of low sample sizes (between 10 and 80 observations), so I'm reluctant to check my fits using chi-square (also, I'd like to avoid bin choice issues). I'd use the Kolmogorov-Smirnov test, but of course this is invalid when the distribution

Hi all, I have gone through some basics of R in eclipse environment. What I need to work is more than basic level so I could not find out the solution by myself. could anyone help me ? My case, I have climate data (precipitation) in nc format with single layer for each time interval in a day. That means for January 1, 2013, I have 8 separate files each containing 3 hrs cumulative precipitation

> 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

Dear all, I have define a discrete distribution P(y_i=x_i)=p_i, which I want to plot a CDF plot. However, I can not find a function in R to draw it for me after searching R and R-archive. I only find the one for the sample CDF instead my theoretical one. I find stepfun can do it for me, however, I want to plot some different CDF with same support x in one plot. I can not manage how to do it with

Hello Recently, I've been dealing with a seemingly simple problem. I have a table with two columns filled with values from a test I made (say, amount of precipitation and Months measured). Now, instead of plotting a typical linear graphic with precipitation on the y-axis and the months in the x-axys, I would like to "convert" the values of precipitation in a color gradient,

This question might seem silly, because I felt that it MUST be in the mailing list archives or help files somewhere, but I simply couldn't find it. I want to make some simple CDF (cumulative distribution function) plots to check whether distributions are Gaussian / normal. But in order to check how "normal" the distribution is, I really need the y-axis to be Gaussian as well

Hi, I would like to overlap the cdf curve for observed and generated data  Here is my code: plot(cdf,main ="CDF of the sum for winter season-Hume",cex.axis=1.2,xlab="Rainfall (mm)", xaxs="i",yaxs="i",col=c("black","red"), lty=c(1,1),ylab="Cumulative probability", xlim=c(0,800),lwd=1) lines(ecdf(datobs))

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

I am performing a precipitation analysis. data is in the form of daily precipitation amounts, e.g. x<- c(4,5,3,0,0,0,2,4,6,4,0,0,0,2,2,0,3,4,1,0,...) I would like to find the length of the "storm", length of storm would be defined as the number of days with continues precipitation. in this case the returned vector would be: (3,4,2,3,...) I would also like the amount of

Dear all, I am calculating the bivariate skew normal cdf in "sn" package using "pmsn" function. Although it is quite convenient ( thanks to prof. Azzalini) but it seems to be slow. For example, it takes about 1 minute in calculation of 100k of such cdf values. I am thinking to write a c++ code for this although not very familiar with it. Any other idea?    Thanks in advance,

Hi, I have a data set which is assumed to follow weibull distr'. How can I find of cdf for this data. For example, for normal data I used (package - lmomco) >cdfnor(15,parnor(lmom.ub(c(df$V1)))) Also, lmomco package does not have functions for finding cdf for some of the distributions like lognormal. Is there any other package, which can handle these distributions?

Hi all, Just a random shot in the dark. Does anyone have/know of a function for the CDF of a generalized error dist? -- Elliot Williams (ewilliams at ucsd.edu) Economics Department, UC San Diego -------------- next part -------------- An embedded message was scrubbed... From: Elliot Williams <ewilliams at ucsd.edu> Subject: [R] Generalized Error Distribution (Exponential Power) CDF?

Hi, I would like to use the following codes to plot the CDF for pareto distribution. Before doing this, I have plot the emperical one. x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4477557,), col="red") Could anyone give me some advice whether the above codes are correct? Many thanks. -- View this message in context:

All, Thanks for all of your help & advice! I created a plot starting with qqnorm, if I remember right, but I had to add a LOT of extras. I was surprised that it doesn't include the probability (e.g. 50% at mean) and instead provides the standard deviations (e.g., 0 at mean) on the axis. Plus, there is no line through Q1 & Q3 to help give a sense of the normality of the graph.

Hello friends of R, My name is Toni, i'm 25 and I'm working on the Meteorological Investigation team from Balearic Islands. I had contact to you because I have a problem: I done a file for every day since 1912 about precipitation. That file has the following structure: > str(Ast) Loading required package: sp Formal class 'SpatialPixelsDataFrame' [package "sp"]

Dear all, I have some additionale question concerning the spgrass6 package. * When you set a region in GRASS, does the readGRASS6 function in R only load data contained in the zoomed region or the whole map ? * When you have a MASK map in grass, does the readGRASS6 function in R only load data contained inside the MASK area ? Could this be the problem ? Thanks, Jessica

Hello, I wanted to know if there is a simple way of getting the inverse cdf for a KDE estimate of a density (using the ks or KernSmooth packages) in R ? The method I'm using now is to perform a numerical integration of the pdf to get the cdf and then doing a search for the desired probablity value, which is highly inefficient and very slow. Thanks, -fj [[alternative HTML version deleted]]