similar to: pdf (probability distribution function) and cdf

Dear all, do you know any easy way based on a vector input how to plot easily cdf and pdf. I would like to thank you in advance for your help Regards Alex

Hello all, I would like to plot the emperical CDF, normal CDF and pareto CDF in the same graph and I amusing the following codes. "z" is a vector and I just need the part when z between 1.6 and 3. plot(ecdf(z), do.points=FALSE, verticals=TRUE, xlim=c(1.6,3),ylim=c(1-sum(z>1.6)/length(z), 1)) x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4373,-0.2398), col="red") y

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

Hello,   Could someone help me please and to tell how to get the probability from empirical distribution (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical distribution (which does not correspond to any typical

Hello,   Could someone help me please and to tell how to get the probability from empirical distribution (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical distribution (which does not correspond to any typical

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.

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")

Dear all, I would like to print an empirical cdf on a vector I have while the plot(ecdf(myVector)) works great when I try to specify the line type to be a line (I get points) with plot(ecdf(myVector),type="lines") Error in plot.default(NA, NA, type = "n", xlim = xlim, ylim = ylim, xlab = xlab,  :   formal argument "type" matched by multiple actual arguments I get

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))

Hi all, I'm looking for an analytic method to obtain the PDF & CDF of the sampling distribution of a given correlation (rho) at a given sample size (N). I've attached code describing a monte carlo method of achieving this, and while it is relatively fast, an analytic solution would obviously be optimal. get.cors <- function(i, x, y, N){ end=i*N

Good day everyone, I want to assess the error when fitting a Gram-Charlier CDF to some data 'ws', that is, I want to calculate: Err = |ecdf(ws) - GCh_ser(ws)| The problem is, I cannot get the F(x) values from the ecdf. 'Summary(ecdf())' returns some of the x-axis values, but how do you get the F(x) values? Thank you for any help you can provide. Regards, Augusto

Dear all, I'm trying to estimate the parameters of a lognormal distribution fitted from some data. The tricky thing is that my data represent the time at which I recorded certain events. However, in many cases I don't really know when the event happened. I' only know the time at which I recorded it as already happened. Therefore I want to fit the lognormal from the cumulative

Dear UseRs, maybe is a silly question: how can I get Empirical CDF values from an object created with ecdf()?? Using print I obtain: Empirical CDF Call: ecdf(t) x[1:57] = 4.1, 4.4, 4.5, ..., 491.3, 671.27 Thanks in advance. Regards, Vito Diventare costruttori di soluzioni Became solutions' constructors "The business of the statistician is to catalyze the scientific

Hello, I am not sure how to do this in R. Any suggestion would be appreciated. I have a vector of values from where I build an empirical CDF. For example: > x <- seq(1,100) > x <- sample(x,1000,replace=T) > quantile(x,probs=seq(0,1,.05)) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 1.00 5.00 10.00 16.00 20.00 25.00 31.00 36.00 41.00

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

Dear R Users, I am trying to suppress the information printed by the ecdf function during an assignment. Various alternatives have failed me so far: > a=summary(ecdf(rnorm(100)))["1st Qu."] Empirical CDF: 100 unique values with summary > invisible(a=summary(ecdf(rnorm(100)))["1st Qu."]) Empirical CDF: 100 unique values with summary > (function()

Dear all, How can I use the histogram density estimate (hist) to find the value of the cdf at a certain point? Thanks Maram [[alternative HTML version deleted]]

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?

Dear List: I am trying to find the area between two ECDFs. I am examining the gap in performance between two groups, males and females on a student achievement test in math, which is a continuous metric. I start by creating a subset of the dataframe male<-subset(datafile, female="Male") female<-subset(datafile, female="Female") I then plot the two CDFs via