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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

Hi I'm trying to maximize a joint likelihood of 2 likelihoods (Likelihood 1 and Likelihood 2) in mle2, where the parameters I estimate in Likelihood 2 go into the likelihood 1. In Likelihood 1 I estimate the vector logN with length 37, and for the Likelihood 2 I measure a vector s of length 8. The values of s in Lieklihood 2 are used in the Likelihood 1. I have 2 questions: ##1 I manage

Hi all, I would like to plot part of the emperical CDF. Suppose the variable is x, I just need the part when x>1,therefore, I am using the following codes. tail <- x>1 plot(ecdf(x[tail]), do.points=FALSE, verticals=TRUE) The "x" value starts from 1, but the yaxs still begins from 0, not the corresponding value when "x" is 1. How can I make it match? Could anyone

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:

Hi all. Belove is the example from the cluster-help page wtih the output. I simply cannot figure out how to relate the estimate and robust Std. Err to the p-value. I am aware this a marginal model applying the sandwich estimator using (here I guess) an emperical (unstructered/exchangeable?) ICC. Shouldent it be, at least to some extend, comparable to the robust z-test, for rx :

Hey Kevin, Am I correct in assuming that if I want a particular widget to respond to an event which doesn''t take an ID as an argument (like evt_size or evt_left_down), that I have to inherit a new widget and define the event handler within the inherited class? Here''s a little contrived code example to illustrate what I mean: class MyCtrl < Wx::TextCtrl def

Dear all, I am trying to get the emperical bayes estimates together with their standard errors out of lme4. Up to now I have used MLwiN to get these estimates. I have fitted the following - very simple - model, just to find out how this works. test<-lmer(y~(1|subject),data,REML=F) ranef(test,postVar=T) str(ranef(test,postVar=T) If I use the formulation of the emperical bayes estimates and

Hi, I am new to R, and I suspect I am missing something simple. I have a data set that performance data that correlates request rate to response times http://pastebin.com/Xhg0RaUp There is some jitter in the data, but mostly it looks like a hockey puck curve. It does not get converted into a straight line when I tried log conversions, so it does not seem to be a

Hey guys, It''s been awhile, but I''ve put up a new tutorial page on Wx::Frame. It''s the first half of the tutorial on Frame, the rest will come a little later. Let me know what you guys think. Any comments or criticisms are welcome. Let me know what was good and what could have been better, so that future tutorials will be able to incorporate those improvements.

Dear R-users, I have two sets of code that appear to me to be equivalent, shown below, and yet I get the error message "Error in dim(robj) <- c(dX, dY) : dim<- : dims [product 4] do not match the length of object [1]" after executing the assignment to logdens2. Both functions post.a1 and post.a2 return the same values when run alone and not embedded in the function outer. I

About a year ago (ouch!), Robert Carlin submitted a ported version of the wxPython demo/sample. I hoped to include it in wxruby2, but it relies on SplitterWindow, which isn''t in wxruby2 yet. I tried to implement SplitterWindow myself, but got such strange results that I''m thinking I did something wrong. Even though the generated C++ code specified a variable number of

Hello, I want to estimate the parameters of a binomial distributed rv using MLE. Other distributions will follow. The equation system to solve is not very complex, but I've never done such work in R and don't have any idea how to start... The system is: (1) n*P = X (2) [sum {from j=0 to J-1} Y{j} /(n-j)] = -n * ln (1-X / n) where * only X is given (empirical mean)

Hi Folks, The recent correspondence about "strange fisher.test result", and especially Peter Dalgaard's reply on Tue 03 April 2007 (which I want to investigate further) led me to take a close look at the code for binom.test(). I now have a query! The code for the two-sided case computes the p-value as follows: if (p == 0) (x == 0) else if (p == 1) (x == n)

Hi Guy's I was wondering if someone could point me in the right direction. dbinom(10,1,0.25) I am using dbinom(10,1,0.25) to calculate the probabilty of 10 judges choosing a certain brand x times. I was wondering how I would go about simulating 1000 trials of each x value ? regards Brendan -- View this message in context:

Full_Name: Chanseok Park Version: R 2.2.1 OS: RedHat EL4 Submission from: (NULL) (130.127.112.89) pbinom(any negative value, size, prob) should be zero. But I got the following results. I mean, if a negative value is close to zero, then pbinom() calculate pbinom(0, size, prob). dbinom() also behaves similarly. > pbinom( -2.220446e-22, 3,.1) [1] 0.729 > pbinom( -2.220446e-8, 3,.1)

Hi all Is there any reason why the parameter i in a "for" loop ignores a value of zero? For example sim=c() p=.2 for(i in 0:5) {sim[i]=dbinom(i,5,p) } sim [1] 0.40960 0.20480 0.05120 0.00640 0.00032 In this example the quantile i= 0 was ignored since dbinom(0,5,p) [1] 0.32768 The same behaviour occurs if I use a while loop to perform the same calculation: sim=c() p=.2 i=0

I'm trying to fathom how to answer two example problems (3.3.2 & 3.3.3) in: Krishnamoorthy. 2006. "handbook of statistical distributions with applications" The first requires calculating single trial probability of success for a binomial distribution when we know: trial size=20, successes k=4, P(x<=k)=0.7 Appreciably all the binomial functions are requiring "prob",

Thanks to those who responded to my inquiry about generalized linear mixed models on R and S-plus. Before I summarize the software, I note that there are several ways of doing statistical inference for generalized linear mixed models: (1)Standard maximum likelihood estimation, computationally intensive due to intractable likelihood function (2) Penalized quasi likelihood or similar

Hi Folks, There has been past correspondence regarding Catherine Loader's Bell Labs (oops, Lucent) paper "Fast and Accurate Computation of Binomial Probabilities" which gives the algorithm on which R's dbinom() is based. The original URL given in the R documentation "?dbinom" is: http://cm.bell-labs.com/cm/ms/departments/sia/catherine/dbinom but this link is

In principle a good idea, but I'm not sure the whereabouts of Catherine Loader are known at this point. Last peeps from her on the net seem to be about a decade old. .pd > On 18 May 2020, at 10:31 , Abby Spurdle <spurdle.a at gmail.com> wrote: > > This has come up before. > > Here's the last time: > https://stat.ethz.ch/pipermail/r-devel/2019-March/077478.html