similar to: Frequency Distribution

CRAN et al., I would like to add an extented introduction or other arbitrary sections to my package lmomco. I have been shipping inst/doc/Introduction.Rd. I would like to have this content inserted to the front of the PDF build for the CRAN. The R-exts.pdf seems to be a little silent on this subject? For my purposes, I have been doing this R CMD Rd2dvi --pdf

Dear all, I wanna to do multiple comparison test for proportions (multiple chi squre ?), could someone tell me how in R, thank you!

I am porting some FORTRAN to R in which an Inf triggers an if(). The trigger is infinite on exp(lgamma(OVER)). What is the canonical R style of determining OVER when exp(OVER)== Inf? The code structure that I am porting is best left intact--so I need to query R somehow to the value of OVER that causes exp(lgamma(OVER)) to equal Inf. On my system, exp(lgamma(171)) is about first to equal Inf.

I upgraded to R-2.2.1 on two PPC G5 computers today. Further I want to work with the tseries package for the first time. As root with R CMD INSTALL tseries_0.10-0.tar.gz I get the following gcc-3.3 -bundle -flat_namespace -undefined suppress -L/usr/local/lib - o tseries.so arma.o bdstest.o boot.o dsumsl.o garch.o ppsum.o tsutils.o -framework vecLib -L/usr/local/lib/gcc/powerpc-apple-

I have question (curiosity) regarding returned values of R's qcauchy () function, for nonexceedance probability (F). It seems the ideal returned range of cauchy distribution should be [-Inf,Inf]. For F=0 > qcauchy(0) [1] -Inf but for F=1 > qcauchy(1) [1] 8.16562e+15 It seems to me that the proper return value should be Inf??? For default (location=0,scale=1) quantile function of

I have a function XOLD() from a nearly verbatim port of legacy FORTRAN in a package. I have remplemented this function as XNEW() using much cleaner native R and built-in functions of R. I have switched the package to the XNEW(), but for historical reasons would like to retain the XOLD() somewhere in the package directory structure. An assertion through a README or other will point to

Dear R forum >From the given data, I have estimated the parameters of Wakeby distribution using lmomco package as library(lmomco) (amounts <- read.csv("input_S.csv")$amount) # ___________________________________________________________ # Wakeby distribution - Parameter estimation N                      = length(amounts) lmr                    = lmom.ub(amounts)

Hi >i have this data > X [1] 5.79 1579.52 2323.70 68.85 426.07 110.29 108.29 1067.60 17.05 22.66 [11] 21.02 175.88 139.07 144.12 20.46 43.40 194.90 47.30 7.74 0.40 [21] 82.85 9.88 89.29 215.10 1.75 0.79 15.93 3.91 0.27 0.69 [31] 100.58 27.80 13.95 53.24 0.96 4.15 0.19 0.78 8.01 31.75 [41] 7.35 6.50

Hi guys, I'm trying to use lmomco package. first I did the manual calculation on what is the estimates scale and location parameter given L-CV=0.2, L1=1000 L-moments and k (shape parameter) =- 0.1. so what i get is: location: 821.0445 scale: 260.7590 shape: -0.1000 #I assign this as GEV vectors using vec2par GEVpara2<-vec2par(c( 821.0445 , 260.7590 ,-0.1),'gev') #then I

Hi, I'm working on producing a simple cumulative frequency distribution. Thanks to the help of the good people on this list I now have four vectors that I'd like to join/relate into a table. e.g. v1 <- myHistogram$breaks # classes v2 <- myHistogram$counts # freqs v3 <- cumsum(v2) # cumulative freq v4 <- ((v3 / length(myData)) *

Dear R helpers, I am not a statistician and right now struggling with Richards curve. Wikipedia says (http://en.wikipedia.org/wiki/Generalised_logistic_function) The "generalized logistic curve or function", also known as Richard's curve is a widely-used and flexible sigmoid function for growth modelling, extending the well-known logistic curve. Now I am confused and will like to

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) =

Hi, I have a data set which is assumed to follow weibull distr'. How can I find of cdf for this data. For example, for normal data I used (package - lmomco) >cdfnor(15,parnor(lmom.ub(c(df$V1)))) Also, lmomco package does not have functions for finding cdf for some of the distributions like lognormal. Is there any other package, which can handle these distributions?

Hi all, Has anyone used the qua.regressCOP2 function from the copBasic package??? The default copula function used in this function is plackett copula and I wanted to use archimedean copula. Attached below is my code: mycop<-frankCopula V=seq(0.001,0.99,by=0.000217) R<-qua.regressCOP2(0.25,V,cop=mycop,para=c(3.504)) And this is the error I get: Warning messages: 1: In

I seem to remember this was discussed a year or two ago, but I can't find it in the archives. platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 1 minor 7.0 year 2003 month 04 day 16 language R

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 >

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

I have been lurking in this list a while and searching in the archives to find out how one learns to write fast R code. One solution seems to be to write part of the code not in R but in C. However after finding a benchmark article (http://www.sciviews.org/other/benchmark.htm) I have been more interested in making the R code itself more efficient. I would like to find more info about this. I have

Hi *, is there a way to obtain some kind of p-value for a model fitted with RMA using the lmodel2 package? I know that p-values are discussed and criticized a lot and as you can image from my question I'm not very much of a statistican (only writing my bachelor thesis). As fare as I understood the confidence interval statistic correctly, a coefficient is regarded as statistically significant

Hi! Unfortunately the version loaded on the office server is 2.6.0 (for some undisclosed so called policy decision by my adamant IT dept. who are not willing to upgrade), I need to use YieldCurve package compatible with this version. On my standalone machine I have R 2.9 loaded and hence I have no problem in using the YieldCurve package. (a) Kindly guide how do I download the version of