search for: distn

...sition in an array so the output is the power of each combination of test, distribution, sample size and mu1, however my code is not working and i keep getting an error message returned. My code is: proj1.sim<-function(){ tests<-c("t","Wilcoxon","bootstrap") distns<-c("Normal","Uniform","Beta") sample.sizes<-c("5","10","25","50","100") mu1s<-c("0","0.5","1","1.5","2") res<-array(0,c(length(tests),length(distns),l...

Hi, the functions in distn.R were converted from .Internal to .External ([1], in 2012), and to .Call ([2], in 2014). They are still listed as .Internal in names.c, although they are not used in that way. Shouldn?t they be removed? There?s quite some simplification to be had, e.g., do_math3 could go away and do_math2 would b...

...Does the Windows box have the same version of R on it? > The only thing I can think of is that there are quite a lot of cases > where a function is generated on the fly as in: > > eval(parse(t=paste("dprob <- > function(x,l,s){",dist.functions[2,][dist.functions[1,]==distn],"(x,l,s)}",sep=""))) This isn't very idiomatic. If you need dprob to call the function named in dist.functions[2,][dist.functions[1,]==distn], wouldn't it be easier for R to assign that function straight to dprob? dprob <- get(dist.functions[2,][dist.functions[1,]=...

Dear all, I am trying to simulate from truncated Pareto distribution. I know there is a package called PtProcess for Pareto distribution...but it is not for truncated one. Can anyone please help me with this? Thanks in advance. Cassie [[alternative HTML version deleted]]

Hi, I'm trying to use ad.test or ad2.test to test whether a given data set is exponential. I see that one of the function inputs is "distn", but I try "exponential" and 5 other variants, but I still get the error message: ad.test(test2, distn="exponential", fit=list(estimate = 0.167685), H=NA, sim=100) Error in ad.test(test2, distn = "exponential", fit = list(estimate = 0.167685), : 'distn...

...tried it on my lowly windows box that is years old and got it down to 88.07%. The only thing I can think of is that there are quite a lot of cases where a function is generated on the fly as in: eval(parse(t=paste("dprob <- function(x,l,s){",dist.functions[2,][dist.functions[1,]==distn],"(x,l,s)}",sep=""))) I haven't added the options to any of these. The highest time used by any of my functions is 0.05% - the rest is dominated by gc(). There may not be much point in parallising the code until I can reduce the garbage collection. I am not short of me...

...05 RAM: < 1MB > system.time( pwilcox( 21000, 211, 211) ) user system elapsed 0.020 0.000 0.025 > system.time( a <- qwilcox( 0.43, 200, 400) ) user system elapsed 0.040 0.000 0.07 RAM: < 1MB There is no more need for wilcox_free at [dpq]wilcox in src/library/stats/distn.R (every other call after the first one with the same m,n will just read the results from the array so it will be really fast) and for #define WILCOX_MAX 50 in src/nmath/nmath.h p.s. modified files are in the attachment have fun, -- Ivo Ugrina << http://web.math.hr/~iugrina >> Teachi...

...rn leads to the function dnbeta() already available in the stats package for some time. You may find this code in http://www.uni-bayreuth.de/departments/math/org/mathe7/R-devel/dnf.tar.gz This archive includes the C-File dnf.c as well as some adapted versions Fdist_new.Rd and distn_new.R; in the latter two files I indicated all modifications w.r.t. Fdist.Rd and distn.R by a corresponding %%-tag. You may modify these files as you like in order to conform your design principles --- and hopefully we will soon have df() for ncp, too... Best, Peter

...red time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is correlated with T. In other words, I would like to generate U from the conditional distribution [U | T=t]. One approach might be to assume that the joint distn [T, U] is bivariate lognormal. So, the marginals [T] and [U], as well as the conditional [U | T] are also lognormal. I can estimate the marginal [T] using the right-censored data Y (assuming independent censoring). For example, I might use survival::survreg to do this. Then, I assume that U is...

Hi all, I would like to estimate power and necessary sample size for a GLM with a response variable that has a poisson distribution. Do you have any suggestions for how I can do this in R? Thank you for your help. Sincerely, Craig -- Craig A. Faulhaber Department of Forest, Range, and Wildlife Sciences Utah State University 5230 Old Main Hill Logan, UT 84322 (435)797-3892

for learning purposes and also to help someone, i used roger peng's document to get the mle's of the gamma where the gamma is defined as f(y_i) = (1/gammafunction(shape)) * (scale^shape) * (y_i^(shape-1)) * exp(-scale*y_i) ( i'm defining the scale as lambda rather than 1/lambda. various books define it differently ). i found the likelihood to be n*shape*log(scale) +

Hi, I've written some C code for density evaluation of the non-central t distribution. It works with the R-1.7.1 source code if placed in the src/nmath directory and after appropriate changes are made to Makefiles, to the dt function in src/library/base/R/distn.R etc. I haven't read a lot of R source code so it may need some R-ification, but I've tried to use the dt.c file as a standard. Use and abuse. Claus ===> File dnt.c <=== /* * AUTHOR * Claus Ekstr?m, ekstrom@dina.kvl.dk * July 15, 2003. * * This program is free sof...

...ata into matrix ## parameter setting N=nrow(X) # # of servers T=ncol(X) # Time m=sum(X)/(N*T) # mean of the training set M=matrix(m,nrow=N,ncol=T) s=sum((X-M)^2)/(N*T) # std of the training set K=3 # # of clusters alpha=0.5 # parameter for Dirichlet distn sigmae=0.5 # var of cluster mean mu q1=rep(1/K,K) # prior for Z(n,1) ## MCMC sampling data=list("X","m","s","N","T","K","alpha","sigmae","q1") inits=function(){list(a0=rbeta(1,1,1), qx=matr...

...ed.o d blcen.o distance.o hclust-utils.o nls.o rWishart.o HoltWinters.o PPsum.o arima.o burg.o filter.o mAR.o pacf.o starma.o port.o fa mily.o sbart.o approx.o loglin.o lowess.o massdist.o splines.o lm.o complete_cases.o cov.o deriv.o fft.o fourier.o model.o optim .o optimize.o integrate.o random.o distn.o zeroin.o rcont.o influence.o bsplvd.o bvalue.o bvalus.o loessf.o ppr.o qsbart.o sgram .o sinerp.o sslvrg.o stxwx.o hclust.o kmns.o eureka.o stl.o portsrc.o lminfl.o -fopenmp -L../../../../lib -lRlapack -lgfortran - lm /opt/lib/gcc/powerpc-ibm-aix5.3.0.0/4.7.4/libgcc.a -lg -lm /opt/lib/libintl.a /...

On 2015-10-15 15:02, Prof Brian Ripley wrote: > On 15/10/2015 13:32, Michael Felt wrote: >> Hi. >> >> Just wanted to let you know I am getting close to packaging R for AIX in rephrase - would like to be active in keeping R binaries current for AIX. My interest in not in R per se (rather a colleague who has a project that uses R, so I hope to assist him, and others like him).

.... wrote: > Craig, I found the package asypow difficult to use and it did not > yield results in the ballpark of other approaches. (could not figure > out the "constraints" vector). > > I wrote some simple functions, one asy.pwr uses the non-central > chi-square distn. > > asy.pwr<-function(counts=c(7,1),py=c(1081,3180)) > { # a two group Poisson regression power computation # py is > person-years or person-time however measured > group<-gl(2,1) > ncp<-summary(glm(counts~group+offset(log(py)),family=poisson))$null.de > viance >...

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

Hello all, I need to imitate the 'q' function (qnorm, qweibull, etc) for Clark's 2Dt distribution model. I'm not skilled enough in R to code it myself, so I thought I could find the base code for one of the existing 'q' functions and just modify it. However, I'm having trouble navigating through the R Source Code on the CRAN website. I've searched some forum and

Dear experts, Helps are badly needed. I'm trying to generate a panel data with error term from N(0,1) and alpha from U(0,20).Explanatory variables are from multivariate std normal distn. Problem arised when I tried to contaminate the data in Y by adding additional term from N(50,1). I ask the computer to choose 5 random data from Y by using the command runif(5,1,50) since we have 50 data altogether. i worry the computer will choose the same data twice. will that happen? i attach...