search for: samlmu

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 was tryin...

...ows Mackenzie River mackenzieRiver = c(26600, 30300, 34000, 32000, 29200, 28300, 28600, 26400, 28300, 28800, 29000, 22100, 32900, 31800, 21600, 32100, 27000, 24800, 28000, 35000, 32000, 25000, 15800, 28800, 29900, 28000, 25600, 19700, 25700, 29500, 26800, 30000, 29500) # estimate moments moments = samlmu(mackenzieRiver, sort.data = TRUE) log.moments <- samlmu( log(mackenzieRiver), sort.data = TRUE ) # estimate parameters parGEV <- pelgev(moments) # GEV parPE3 <- pelpe3(moments) # Pearson parLPE3 <- pelpe3(log.moments) # log Pearson # plot result evplot(mackenzieRiver, rp.axis = TR...

I saw a Help e-mail related to MLE. Does R have a probability weighted method (PWM) estimator function? I can't seem to find anything on PWM, unless my eyes are playing trick on me. [[alternative HTML version deleted]]

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

...,0,177000,0,12490,500,0,1750,0,2100,600,0,13139.58,115777,1400,0,5800,4000,0,1500,1608,11199,14050,42295.49,10900,0,76688,204400,8947.2,4900,4850,3300,0,2000,0,3000,1650,5400,4354,0,0,10070,22457.95,0,100,0,0,4000,1100,1500,0,3992.83,19100,0,4626.65,500,17332)    lmom                          <- samlmu(amounts); lmom    parameters_of_Normal  <- pelnor(lmom);   parameters_of_Normal       Please guide as o why there is so much of a difference in standard deviation i.e. .     See the Web&#39;s breaking stories, chosen by people like you. Check out Yahoo! Buzz. http://in.buzz.yahoo.co...

...rary(lmom) x <- c(986.78,1067.76,1046.47,1034.71,1004.53,1007.89,964.94,1060.24,1188.07,1085.63,988.33,972.71,1177.71,972.48,1203.20,1047.27,1062.95,1113.65,995.97,1093.98) #Estimating the parameters for GUMBEL distribution N <- length(x) lmom <- samlmu(x); lmom parameters_of_GUMBEL <- pelgum(lmom); parameters_of_GUMBEL # Parameters are xi = 1019.4003 alpha = 59.5327 # _____________________ P - P Plot ________________________ e <- c(1:N) f <- c((e-.5)/N) Fx <- cdfgum(x, para = pa...

...library(lmom) x <- c(986.78,1067.76,1046.47,1034.71,1004.53,1007.89,964.94, 1060.24, 1188.07, 1085.63,988.33,972.71, 1177.71,972.48,1203.20, 1047.27,1062.95, 1113.65, 995.97, 1093.98) #Estimating the parameters for GUMBEL distribution N <- length(x) lmom <- samlmu(x); lmom parameters_of_GUMBEL <- pelgum(lmom); parameters_of_GUMBEL # Parameters are xi = 1019.4003 alpha = 59.5327 # _____________________ P - P Plot ________________________ e <- c(1:N) f <- c((e-.5)/N) Fx <- cdfgum(x, para = parameters_of_GUM...

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 unsure of how to implement it using the pgev distribution. For example:...

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