search for: geometrically

?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]]

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

Hello, I'm writng this message to see if anyone knows how to analyze a data with geometrically distributed outcome. My situation now is : I have a outcome which is geometrically distributed. And I've also self-derived the link function for geometric distribution. I want to use generalized linear models to analyze it, but there're only models for binary data, poisson distributed data...

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

>>>>> 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

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 samples will generate the following concentrations: 1, 1/2, 1.4, 1/8, 1/16, ... (or the rec...

---------- 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

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

Does anyone know how to approach R code for the MLE of a geom. distribution? thanks! [[alternative HTML version deleted]]

Hi all, I need to check for a difference between treatment groups in the parameter of the geometric distribution, but with a cut-off (i.e. right censored). In my experiment I stimulated animals to see whether I got a response, and stopped stimulating if the animal responded OR if I had stimulated 10 times. Since the response could only be to a stimulation, the distribution of response times

...em which has given me no end of grief recently, and am posting here in the hope that somebody can give me a straight answer. I'm a IT postgrad, not a statistician, so people may have to speak really slowly and clearly for me to get it :) I am collecting simulation data, and the results are geometrically distributed (or approximately so). From what I can gather from my stats books, provided the sample size is large enough (>30 or so) I can use t and z-tests to compare means under different experimental conditions as the CLT says that the sample means will be approximately normally distribu...

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)

Hi all, I am trying to convert geometric means in a matrix to cover classes. My values are as such: perc<-c(0,0.025136418, 0.316227766, 1.414213562,3.16227766, 7.071067812, 15.8113883, 35.35533906, 61.23724357, 84.40971508, 97.46794345) cover<-c(0,1,2,3,4,5,6,7,8,9,10) This is what I am trying to accomplish veg_mean[veg_mean>0 && veg_mean < .1] <- 1

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

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, 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

Hello, Can anyone think of a non-iterative way to generate a decreasing geometric sequence in R? For example, for a hypothetical function dg, I would like: > dg(20) [1] 20 10 5 2 1 where I am using integer division by 2 to get each subsequent value in the sequence. There is of course: dg <- function(x) { res <- integer() while(x >= 1) { res <- c(res, x) x

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

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 >

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]]