search for: lfactorial

...arameters,y,x1,x2) { p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3)] delta1<-parameters[4] alpha2<-parameters[5] mu<-alpha1*((x1)^beta1)*exp(-delta1*(x1^alpha2)) if(y>0 & x1>0 & x2==1, L<-lgamma(y+p)+p*(log(p)-log(mu+p))+y*(log(mu)-log(mu+p))-lfactorial(y)-lgamma(p) ) else if(y>0 & x1>0 & x2==2, L<-lgamma(y+p)+p*(log(p)-log(mu+p))+y*(log(mu)-log(mu+p))-lfactorial(y)-lgamma(p) ) else if(y>0 & x1>0 & x2==3, L<-lgamma(y+p)+p*(log(p)-log(mu+p))+y*(log(mu)-log(mu+p))-lfactorial(y)-lgamma(p) ) else L } Than...

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]...

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)              {                

...low: function (parameters,y1,x11) { p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3] delta1<-parameters[4] lamda1<-parameters[5] mu<-alpha1*((x11)^beta1)*exp(-delta1*(x11^lamda1)) ifelse(y1>0|x11>0, L<-lgamma(y1+p)+p*(log(p)-log(mu+p))+y1*(log(mu)-log(mu+p))-lfactorial(y1)-lgamma(p) ,Inf) L } This is working for me. 2 Way two: I select the data whose x2=1 in the whole range of data. It works but it is not right comparing the value of MLE. the program is: function (parameters,y,x1,x2) { p<-parameters[1] alpha1<-parameters[2] beta1<-paramet...

Version 1.3.1 (2001-08-31) factorial() is not in R. It is in S-Plus with the definition factorial <- function(n) gamma(n + 1) -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not

...e of what the wrong is? Thanks! Xin My function is: function (parameters,y,x1,x3) { alpha<-parameters[1:10]; beta<-parameters[11]; g<-parameters[12]; theta<-parameters[13]; j=x3 p=alpha[j]*(x1^beta)*exp(-g*x1) ifelse(x1>0, L<-lgamma(y+p)+p*log(theta)+y*(log(1-theta))-lfactorial(y)-lgamma(p) ,Inf) L } [[alternative HTML version deleted]]

Dear colleagues. I wonder if anybody knows about a procedure in R to calculate the Brillouin Diversity index. I searched the net but did not find anything about it. Thanks a lot for any help Best, Philipp *************************************************** Prof. Dr. Philipp Fischer Head of AWI Center for Scientific Diving & Dept. In situ Ecology Section Shelf Sea Systems

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:

...is: function (parameters, y1,x11) { p<-parameters[1] alpha1<-parameters[1] beta1<-parameters[2] delta1<-parameters[3] mu<-alpha1+beta1*(x11)+delta1*(x11^2) ifelse(y1>=0|x11>=0, L<- lgamma(y1+p)+p*(log(p)-log(mu+p))+y1*(log(mu)-log(mu+p)) -lfactorial(y1)-lgamma(p) ,Inf) L } I got outputs even it sounds a problem for delata. Actually, there is a mistake made for the parameters (p & alpha1) set up. If I correct them as: p<-parameters[1] alpha1<-parameters[2] beta1<-parameters[3] delta1<-parameters[4] Then err...

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 have some big combinations like: 4444444444444444444444444444 choose 784645433 Can R compute these? Is there any package that does stirlings approximation in R? -- Thanks, Jim. [[alternative HTML version deleted]]

Hello, I am struggling for some time now to estimate AR(1) process for commodity price time series. I did it in STATA but cannot get a result in R. The equation I want to estimate is: p(t)=a+b*p(t-1)+error Using STATA I get 0.92 for a, and 0.73 for b. Code that I use in R is: p<-matrix(data$p) # price at time t lp<-cbind(1,data$lp) # price at time t-1

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

Dear Lister, I am facing a strange problem fitting a GLM of the negative binomial family. Actually, I tried to estimate theta (the scale parameter) through glm.nb from MASS and could get convergence only relaxing the convergence tolerance to 1e-3. With warning messages: glm1<-glm.nb(nbcas~.,data=zonesdb4,control=glm.control(epsilon = 1e-3)) There were 25 warnings (use warnings() to see

...d by the predict() method for loess to return a suitable array. o factanal(), loess() and princomp() now explicitly check for numerical inputs; they might have silently coded factor variables in formulae. o New functions factorial(x) defined as gamma(x+1) and for S-PLUS compatibility, lfactorial(x) defined as lgamma(x+1). o findInterval(x, v) now allows +/-Inf values, and NAs in x. o formula.default() now looks for a "terms" component before a 'formula' argument in the saved call: the component will have `.' expanded and probably will have the original env...

...d by the predict() method for loess to return a suitable array. o factanal(), loess() and princomp() now explicitly check for numerical inputs; they might have silently coded factor variables in formulae. o New functions factorial(x) defined as gamma(x+1) and for S-PLUS compatibility, lfactorial(x) defined as lgamma(x+1). o findInterval(x, v) now allows +/-Inf values, and NAs in x. o formula.default() now looks for a "terms" component before a 'formula' argument in the saved call: the component will have `.' expanded and probably will have the original env...