similar to: factorial() not here (PR#1194)

Hello all, I was going to solve (n-m)! * (n-k)! = 0.5 *n! * (n-m-k)! for m when values of n and k are provided n1<-c(10,13,18,30,60,100,500)         # values of n kx<-seq(1,7,1);                               # values of k slv2<-lapply(n1,function(n){    slv1<-lapply(kx,function(k){              lhs<-function(m)              {                

### I got bit again by the same bugs I wrote about a year ago. ### The bugs are related to matrices and arrays of lists. ### 1. There is a clear inconsistency in how R handles two ### functionally equivalent statements. ### array() is able to take a list and create a matrix. ### matrix() is unable to create that matrix. > vector("list", 2) [[1]] NULL [[2]] NULL >

I am working on a project that requires me to do very large factorial evaluations. On R the built in factorial function and the one I created both are not able to do factorials over 170. The first gives an error and mine return Inf. Is there a way to have R do these larger calculations (the calculator in accessories can do 10000 factorial and Maple can do even larger) -- View this message in

R : Copyright 2001, The R Development Core Team Version 1.3.1 (2001-08-31) > tmp <- factor(1:3) > get("print.factor")(tmp) [1] 1 2 3 Levels: 1 2 3 > print.a <- function(x,...) { + print("this is a") + NextMethod("print", x, quote = FALSE, right = TRUE, ...) + } > > get("print.a") function(x,...) { print("this is a")

Dear: I try to revise the maximum likelihood function below using something constrains. But it seems something wrong with it. Becasue R would not allow me to edit the function like this. It is very appreciate if you can help. function (parameters,y,x1,x2) { p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3)] delta1<-parameters[4] alpha2<-parameters[5]

Hi all, why factorial(150) shows the error out of range in 'gammafn'? I have to calculate the number of subset formed by 150 samples taking 10 at a time. How is this possible? best [[alternative HTML version deleted]]

lgamma(x) and lfactorial(x) are defined to return ln|Gamma(x)| {= log(abs(gamma(x)))} or ln|Gamma(x+1)| respectively. Unfortunately, we haven't chosen the analogous definition for lchoose(). So, currently > lchoose(1/2, 1:10) [1] -0.6931472 -2.0794415 NaN -3.2425924 NaN -3.8869494 [7] NaN -4.3357508 NaN -4.6805913 Warning message: In

Dear: Continuing the issue of 'ifelse'! I selecting the data whose 'x2'=1 for maximizing likelihood. I used two way to do this but the results are different. 1.Way one I use the data for x2=1 and run the program. It works for me. Tthe program is described as below: function (parameters,y1,x11) { p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3]

Dear R friends, I was wondering if there were factorial and modulus functions out there that I've somehow overlooked? -P. -- Peter L. Hurd, Ph.D. phurd at uts.cc.utexas.edu http://www.zo.utexas.edu/research/phurd fax 512.471-3878 Section of Integrative Biology, University of Texas, Austin TX 78712 USA

> tmp <- data.frame(y=rnorm(12), g=factor(rep(1:2,6))) > bwplot(g ~ y, data=tmp) Error in segments(x1 = structure(c(-1.68373790592731, 0.159038997376781, : Argument "x0" is missing, with no default > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1

Hi Rers, I have a silly question. I don't know how to express the loglikelihood function of 1/(x!) where x=x1,x2,....xn in R. Could anyone give me a hint? Thank you in advance. Chunhao Tu

I repeat my emails of 11/15/01 and 2/26/02, since it looks like this ESS bug is still not fixed in ESS 5.1.23, and I think some resolution is needed. When help() is invoked, ESS makes a copy of .Last.value in the .GlobalEnv, which is *not* where R normally stores it (R stores it in package:base). When this copy becomes stale it leads to wrong answers. The bug is in essd-r.el, lines 63-64:

I'm a total newbie at using R, and so there probably is a better way to do this. However, I couldn't find one, and so maybe this will help someone. I was calculating log-likelihoods using a multinomial model, and found that for large n, prod(n:1) wouldn't work to calculate factorials (e.g., prod(200:1) = Inf). The below function calculates the natural log of a factorial (e.g.

This feels like inconsistent behavior. latticeParseFormula works the way I anticipated for factor, but not for ordered. I want the behavior I see with tmp2, but not with tmp. My next step is to use the right.name to isolate the tmp2[,c("a","b")] columns. tmp <- data.frame(y=(1:12)+.1, a=factor(rep(1:3,4)), b=ordered(rep(1:4,

Dear All: I used optim() to minimise the loglikelihood function for fitting data to negative binomial distribution. But there initial value of log-likelihood and iteration 10 value are reasonable. for example: initial value 1451657.994524 iter 10 value 47297.534905 iter 20 value -623478636.8236478 Then the iter 20 vlaue suddelnly changes to a negative value and in the end the error mesage is

PLEASE HELP I actually want to do the following: a[j] = (1/(j!))*Π (i-1-d), j = 500, Π means product i = 1 to j   Yet, j! will stop at 170 and Π (i-1-d) at 172; so, a[j] will not exceed 170. I would like to have at least 200 a[j].   WHAT SHOULD I DO?   PLEASE SEE MY CODE FOR DETAIL!! #################################################### R CODE:

Dear list, perhpas this question is more suitable for R-dev but since I am not really a developer I post it here first. Apparently the following lines do not create any problem in R: library(combinat) r <- 20; b <- 2; sum( sapply(0:r,function(x) nCm(r,x)^(2*b)) ) > 2^64 while in C I obtain an overflow of data even using unsigned long long and with long double I incurr in precision

Hi there, I have written a function to calculate factorials as follows: fact <- function(x) { recurse <- x > 1 x[!recurse] <- 1 if( any(recurse) ) { y <- x[recurse] x[recurse] <- y * fact( y - 1 ) } x } I want to be able to do the famous birthday problem, which will involve the computation of 365!, however it shall get cancelled

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### 1. as.numeric behaves differently in R than in S and I think this shows a bug in how S3

The R function "optim" fails when a function requires additional arguments, if the option "hessian=T" is specified. I am using R Version 1.0.0 on Windows 98. Here is an example of the Rosenbrock Banana function from the optim help example, with the function and gradient modified to take an additonal argument. Note that the call to optim works fine unless a hessian is