similar to: Maximum likelihood for censored geometric distribution

Hi, I have tried to use uniroot to solve a value (value a in my function) that gives f=0, and I repeat this process for 10000 times(stimulations). However error occures from the 4625th stimulation - Error in uniroot(f, c(0, 2), maxiter = 1000, tol = 0.001) : f() values at end points not of opposite sign I have also tried interval of (lower=min(U), upper=max(U)) and it won't work as well.

?Dear All I have some data expressed in geometric means and 95% confidence intervals. Can I code them in metafor as: rma(m1i=geometric mean 1, m2i=geometric mean 2, sd1i=geometric mean 1 CI /3.92, sd2i=geometric mean 2 CI/3.92.......etc, measure="MD") All of the studies use geometric means. Thanks! Edward ---------------------------- [[alternative HTML version deleted]]

Sorry to prolong a thread on something that is clearly off topic, but when Michael Meyer wrote >by using the geometric mean all asymptotic results no longer apply. that is flat our wrong. It's true that the geometric mean converges to something different that E[X], but it does indeed have an asymptotic distribution and one that makes sense in some contexts. There is no reason that

---------- Forwarded message --------- From: Sahil Sharma <sahilsharmahimalaya at gmail.com> Date: Tue, Oct 17, 2023 at 12:10?PM Subject: r-stats: Geometric Distribution To: <do-use-Contact-address at r-project.org> Hey I want to raise one issue in *r-stats **geometric distribution * function. I have found the dgeom(x,p) which denotes probability density function of geometric

Hi, I have a bunch of data which is assumed to be instances of a geometric random variable with outliers. How can I do a robust estimation of the parameter p so that the effect of outliers is minimized? As a part of the estimation process, I also need to know which are the outliers in the data. I found glmrob which does robust estimation of Poisson and binomial random variables but not geometric

Dear useRs, I'd like to announce a new package called geozoo, short for geometric zoo. It's a compilation of functions to produce high-dimensional geometric objects, including hypercubes and hyperspheres, Boy's surface, the hyper torus and a selection of polytopes. For a complete list, as well as images and movies, visit

Dear list, I have got an array with observational values t and I would like to fit a geometric distribution to it. As I understand the geometric distribution, there is only one parameter, the probability p. I estimated it by 1/mean(t). Now I plotted the estimated density function by plot(ecdf(t),do.points=FALSE,col.h="blue"); and I would like to add the geometric distribution. This

better posted on r-sig-ecology? -- or maybe even stack exchange? Cheers, Bert On Mon, Jan 22, 2024 at 7:45?AM Rich Shepard <rshepard at appl-ecosys.com> wrote: > A statistical question, not specific to R. > > I'm asking for a pointer for a source of definitive descriptions of what > types of data are best summarized by the arithmetic, geometric, and > harmonic >

>>>>> Rich Shepard >>>>> on Mon, 22 Jan 2024 07:45:31 -0800 (PST) writes: > A statistical question, not specific to R. I'm asking for > a pointer for a source of definitive descriptions of what > types of data are best summarized by the arithmetic, > geometric, and harmonic means. In spite of off-topic: I think it is a good

By the Strong Law of Large Numbers applied to log(X) the geometric mean of X_1,...,X_n > 0 and IID like X converges toexp(E[log(X)]] which, by Jensen's inequality, is always? <= E[X] and is strictly less than E[X] except in trivial extreme cases. In short: by using the geometric mean all asymptotic results no longer apply. Michael Meyer [[alternative HTML version deleted]]

I want to make a function for geometric seqeunce since testing=function(x){i=1;ans=1;while(true){ans=ans+(1/x)^i ; i=i+1} ;return(ans)} doesn't work... the program is freeze... from my research, i know i should use iterators. I read iterators.pdf at http://cran.r-project.org/web/packages/iterators/iterators.pdf and didnt find it helps solving my problem at all... Is there any sources I

Dear Rich, It depends how the data is generated. Although I am not an expert in ecology, I can explain it based on a biomedical example. Certain variables are generated geometrically (exponentially), e.g. MIC or Titer. MIC = Minimum Inhibitory Concentration for bacterial resistance Titer = dilution which still has an effect, e.g. serially diluting blood samples; Obviously, diluting the

A statistical question, not specific to R. I'm asking for a pointer for a source of definitive descriptions of what types of data are best summarized by the arithmetic, geometric, and harmonic means. As an aquatic ecologist I see regulators apply the geometric mean to geochemical concentrations rather than using the arithmetic mean. I want to know whether the geometric mean of a set of

Dear all, I''m working with the geometric distribution for the time being, and I''m confused. This may have more to do with statistics than R itself, but since I''m getting results from R I find counterintuitive (well, yeah, my statistical intuition has not been properly sharpened), I feel like asking. The point first: If I do > rgeom(1,prob=1) I get: [1] NaN Warning

On Mon, 22 Jan 2024, Rich Shepard wrote: > As an aquatic ecologist I see regulators apply the geometric mean to > geochemical concentrations rather than using the arithmetic mean. I want to > know whether the geometric mean of a set of chemical concentrations (e.g., > in mg/L) is an appropriate representation of the expected value. If not, I > want to explain this to non-technical

I'm trying to estimate the parameters for GARCH(1,1) process. Here's my code: loglikelihood <-function(theta) { h=((r[1]-theta[1])^2) p=0 for (t in 2:length(r)) { h=c(h,theta[2]+theta[3]*((r[t-1]-theta[1])^2)+theta[4]*h[t-1]) p=c(p,dnorm(r[t],theta[1],sqrt(h[t]),log=TRUE)) } -sum(p) } Then I use nlminb to minimize the function loglikelihood: nlminb(

Hi, I have been using maxLik to do some MLE of Geometric Brownian Motion Process and everything has been going fine, but know I have tried to do it with jumps. I have create a vector of jumps and then added this into my log-likelihood equation, know I am getting a message: NA in the initial gradient My codes is hear # n<-length(combinedlr) j<-c(1,2,3,4,5,6,7,8,9,10)

Folks: I know this may be overreaching, but are we missing what's important? WHY do the zeros occur? Are they values less then a known or unknown LOD? -- and/or is there positive mass on zero? In either case, using logs to calculate a geometric mean may not make sense. Paraphrasing Greg Snow, what is the scientific question? What is the model? Cheers, Bert On Mon, Jan 17, 2011 at 9:13 AM,

Is there a function or an easier way to computer geometric means of each rows in a nxn matrix and spit out in an 1xn matrix ? -- Edward Chen [[alternative HTML version deleted]]

Hi All, I need to calculate the median for even number of data points.However instead of calculating the arithmetic mean of the two middle values,I need to calculate their geometric mean. Though I can code this in R, possibly in a few lines, but wondering if there is already some built in function. Can somebody give a hint? Thanks in advance [[alternative HTML version deleted]]