similar to: PWM Help

How to adapt this piece of code but for: - gamma distribution - 3 parameter log normal More specifically, where can I find the specification of the parameter (lmom) for pelgam() and pelln3()? Lmom package info just gives: pelgam(lmom), lelln3(lmom), where lmom is a numeric vector containing the L-moments of the distribution or of a data sample. # Draw an L-moment ratio diagram and add a line for

Can anyone show me how to add a log Pearson type III plot using the evdistq() command to an extreme value plot using the lmom package? Attached sample code below... Thanks in advance, Dave library(lmom) # annual maximum daily streamflows Mackenzie River mackenzieRiver = c(26600, 30300, 34000, 32000, 29200, 28300, 28600, 26400, 28300, 28800, 29000, 22100, 32900, 31800, 21600, 32100, 27000,

Dear R helpers,   I have a data of 2102 observations (consisting of 0's also), to which I am trying to fit Normal distribution using "lmom" pacakage. If I use Excel, its easy to estimate the parameters of Normal distribution as simple mean and standard devaition. The results I get if I use teh excel are as   Parameters of Normal distribution :-   Mean = 22986.44 and standard

Dera R Helpers, I am re-posting my query. Please guide me. Maithili --- On Fri, 3/13/09, Maithili Shiva <maithili_shiva at yahoo.com> wrote: I am trying to fit the Gumbel distribution to a data. I am using lmom package. I am getting problem in Cumulative Distribution Function of Gumbel distribution as I am getting it as a series of 0's and 1's thereby affecting the P P

Hello, I am using the lmomco package (lmom.ub and pargev) to compute the GEV parameters (location, scale, and shape), which are used to estimate return values. I was wondering how/if I can calculate upper and lower confidence (CI_u, CI_l) intervals for each return frequency using the GEV parameters to fill-in the table below? Xi (location) = 35.396 Alpha (scale) = 1.726 Kappa (shape) =

Hey, I have a set of income data which I'd like to fit to a gamma distribution. How can I estimate the two parameters of the gamma distribution for a vector, e.g. c(2039L, 2088L, 5966L, 2353L, 1966L, 2312L, 3305L, 2013L, 3376L, 3363L, 3567L, 4798L, 2032L, 1699L, 3001L, 2329L, 3944L, 2568L, 1699L, 4545L) I sense this will be a very easy one-liner, but my searching didn't come up with

Dear ALL I have a list of data below 0.80010 0.72299 0.69893 0.99597 0.89200 0.69312 0.73613 1.13559 0.85009 0.85804 0.73324 1.04826 0.84002 1.76330 0.71980 0.89416 0.89450 0.98670 0.83571 0.73833 0.66549 0.93641 0.80418 0.95285 0.76876 0.82588 1.09394 1.00195 1.14976 0.80008 1.11947 1.09484 0.81494 0.68696 0.82364 0.84390 0.71402 0.80293 1.02873 all of them are ninty. Nowaday, i try to find a

Dear Sir in the following example,is the vector lmom a l-moment ratios vector? What is meant by size = northCascades[,1]? And what are the values in c(0.0104,0.0399,0.0405)? Please help me I am unable to understand these from help manual. Best Regards AMINA data(northCascades) lmom <- c(1, 0.1103, 0.0279, 0.1366) kappaParam <- kappalmom(lmom) heterogeneity(500, 19, size =

Hi everyone, In the package POT, there is a function that computes the L-moments of a given sample (samlmu). However, to compute those L-moments, one needs to obtain the total number of combinations between two numbers, which, by the way, requires the use of a factorial. See, for example, Hosking (1990 , p. 113). How does the function "samlmu" in the package POT avoids overflows? I

Respected Sir I have to select a probability distribution using L-moment Ratio Diagram. I am not understanding how to plot sample TAU3 and TUA4 on L-moment Ratio Diagram. Please Guide me Best Regards -- AMINA SHAHZADI Department of Statistics GC University Lahore, Pakistan. Email: amnakhan493@gmail.com amna_989@hotmail.com amna_989@yahoo.com [[alternative HTML version deleted]]

Hi, I want to estimate parameters of weibull distribution. For this, I am using fitdistr() function in MASS package.But when I give fitdistr(c,"weibull") I get a Error as follows:- Error in optim(x = c(4L, 41L, 20L, 6L, 12L, 6L, 7L, 13L, 2L, 8L, 22L, : non-finite value supplied by optim Any help or suggestions are most welcomed -- View this message in context:

Hi List! I?m using regsimq() from the ?lmomRFA?-package to calculate error bounds for diverse distributions. For example: regsimq(gumfit$qfunc, nrec = lmom.data$n, f = lcdfgum, boundprob = c(0.025, 0.975)) Several times I got this error massage: Fehler in quantile.default(ou, probs = boundprob, type = 6) : missing values and NaN's not allowed if 'na.rm' is FALSE So my question

Dear R helpers, How do you estimate the (Location, Scale, Shape) parameters of Generalized Extreme Value distribution using R? I have tried VGAM but just not able to write the R script. Please advise. With regards Maithili

Hi, I have been having difficulties in finding packages/ codes that simplify plotting of a GEV fitted to dataset (by L-moments) that would print out graph comprising dataset versus gumbel reduced variate n return period at the same. Anyone can help me on this? Thanks. [[alternative HTML version deleted]]

I have a one-variable data set in R. The plot of histogram of my numerical variable suggests an inverse gaussian distribution. How can I obtain best estimation for the two parameters of inverse gaussian based on my data? Thanks. -- View this message in context: http://n4.nabble.com/estimate-inverse-gaussian-in-R-tp949692p949692.html Sent from the R help mailing list archive at Nabble.com.

Hi All, I am performing GEV analysis on temperature/precipitation data using L moments: dim(data) [1] 145 192 156 Lmoments <- apply(data, 1:2, function(x) samlmu(x,nmom=4,sort.data=TRUE)) params_GEV <- apply(Lmoments,2:3,pelgev) location <- params_GEV[1,,] (xi) scale <- params_GEV[2,,] (alpha) shape <- params_GEV[3,,] (k) I want to use the ks.boot function but I am

Dear all, I'd like tweaking the ... arguments that one user can pass in my function for fitting a model. More precisely, my objective function is (really) problematic to optimize using the "optim" function. Consequently, I'd like to add in the "control" argument of the latter function a "ndeps = rep(something, #par)" and/or "parscale =

There's a funny inconsistency in how t.test handles paired=T or paired=F. If x and y parameters are lists, paired=F works, but paired=T doesn't. > lg=read.csv("my.csv") > a = subset(lg, condition=="a")["score"] > b = subset(lg, condition=="b")["score"] > t.test(a,b) > t.test(a,b, paired=TRUE) Error in `[.data.frame`(y, yok)

Hi R-users, I'm trying to repeat the same procedure to 1000 data set. I know this is very easy, but I got stuck finding the right and fastest way in running it. IID50=Riidf[1:50,1:1000] #where IID50 is a dataframe consist of 1000 time series(as column) and 50 time scales (row). #what I tried to do: estIID50=rep(NA,1000) for (i in 1:1000) estIID50[i]=pargev(lmom.ub(IID50[1:50,i])) #warning

Hi, I am using parnor function of lmomco package. I believe it provides mean and std. dev for the set of data. But the std. dev provided does not match with the actual std. dev of the data which is 247.9193 (using sd function). Am I missing something here? > lmr <- lmom.ub(c(123,34,4,654,37,78)) > parnor(lmr) $type [1] "nor" $para [1] 155.0000 210.2130 >