similar to: geometric mean of probability density functions

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

Dear All, Is it possible to simulate an AR(1) process via a distribution? I have simulated an AR(1) process the usual way (that is, using a model specification and using the random deviates in the error), and used the generated time series to estimate 3- and 4-parameter distributions (for instance, GLD). However, the random deviates generated from these distributions do not follow the

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

?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]]

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

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

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

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 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)

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

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

Hi I have some density estimates obtained from density(). I would like to calculate the sum of squares of these. As the x values of the estimates are not the same, and I would prefer not to restrict the estiomate to a certain range of x values, how can I do the calculation? Lets say: d1 <- density(Data1) d2 <- density(Data2) If the x values would be the same, I would: ssq <- sum(

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]]

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

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,

---------- 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 need to compute the geometric mean of a population. How i can do it?? thanks. Jorge M. A. Magalhae -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request

Hi, With R version 2.10.0 (2009-10-26) on Windows, I computed the p=1.e-20 quantile of the geometric distribution with parameter prob=0.1. > qgeom(1.e-20,0.1) [1] -1 But this is not possible, since X=0,1,2,... I guess that this might be a bug in the quantile function, which should use the log1p function, instead of the naive formula. Am I correct ? Best regards, Micha?l