similar to: For and while in looping

Hi R-users,   I have this code here: library(numDeriv)   fprime <- function(z) { alp  <- 2.0165;   rho  <- 0.868;   # simplified expressions   a      <- alp-0.5   c1     <- sqrt(pi)/(gamma(alp)*(1-rho)^alp)   c2     <- sqrt(rho)/(1-rho)   t1     <- exp(-z/(1-rho))   t2     <- (z/(2*c2))^a   bes1   <- besselI(z*c2,a)   t1bes1 <- t1*bes1   c1*t1bes1*t2 }   ## Newton

Hi r-users,   I would like to solve for z values using newton iteration method.  I 'm not sure which part of the code is wrong since I'm not very good at programming but would like to learn.  There seem to be some output but what I expected is a vector of z values.  Thank you so much for any help given.   newton.inputsingle <- function(pars,n) {  runi    <- runif(974, min=0, max=1)

Hi r-users,   I hope somebody can help me with this code. I would like to solve for z values using newton iteration method.  I 'm not sure which part of the code is wrong since I'm not very good at programming but would like to learn.  There seem to be some output but what I expected is a vector of z values.  Thank you so much for any help given.   newton.inputsingle <-

Hi R-users,   I have this code below and I understand the error message but do not know how to correct it.  My question is how do I get rid of “with absolute error < 7.5e-06” attach to value of cdf so that I can carry out the calculation.   integrand <- function(z) { alp  <- 2.0165   rho  <- 0.868   # simplified expressions   a      <- alp-0.5   c1     <-

Hi r-users,   I hope somebody can help me to understand the error message.  Here is my code; ## Newton iteration newton_gam <- function(z) { n   <- length(z)   r   <- runif(n)   tol <- 1E-6   cdf <- vector(length=n, mode="numeric")   fprime <- vector(length=n, mode="numeric")   f   <- vector(length=n, mode="numeric")     for (i in 1:1000)   {

Hi, I have a density that I need to get MLEs from, which includes definite integrals both in the denominator and in the numerator of the density function. It looks like the outcome depends on the initial values given. My program is shown below: library(circular) ######################################## 4 parameters ######################################## z<-rvonmises(100,0,1)

Hi, R guys: I'm using L-BFGS-B method of optim for minimization problem. My function called besselI function which need non-negative parameter and the besselI will overflow if the parameter is too large. So I set the constraint box which is reasonable for my problem. But the point outside the box was test, and I got error. My program and the error follows. This program depends on CircStats

#bug 1: besselI() for nu<0 and expon.scaled=TRUE #tested with R-devel (2007-06-17 r41981) x <- 2.3 nu <- -0.4 print(paste(besselI(x, nu, TRUE), "=", exp(-x)*besselI(x, nu, FALSE))) #fix: #$ diff bessel_i_old.c bessel_i_new.c #57c57 #< bessel_k(x, -alpha, expo) * ((ize == 1)? 2. : 2.*exp(-x))/M_PI #--- #> bessel_k(x, -alpha, expo) * ((ize == 1)? 2. :

Hi R-users,   I wrote a code to evaluate double sum as follows:   ff2 <- function(bb,eta,z,k) { r <- length(z) for (i in 1:r) { sm1 <- sum((z[i]*bb/2)*(psigamma((0:k)+eta+1,deriv=0)/(factorial(0:k)*gamma((0:k)+eta+1))))  sm2 <- sum((besselI(z[i]*bb,eta)*log(z[i]*bb/2) - sm1)/besselI(z[i]*bb,eta))  sm2 } ff2(bb,eta,z,10)     but it gave me the following message:   >

Hello, I am trying to compute MLE for non-Gaussian AR(1). The error term follows a difference poisson distribution. This distribution has one parameter (vector[2]). So in total I want to estimate two parameters: the AR(1) paramter (vector[1]) and the distribution parameter. My function is the negative loglikelihood derived from a mixing operator. f=function(vector)

Hi, I have written out the log-likelihood function to fit some data I have (called ONES20) to the non-central chi-squared distribution. >library(stats4) >ll<-function(lambda,k){x<-ONES20; 25573*0.5*lambda-25573*log(2)-sum(-x/2)-log((x/lambda)^(0.25*k-0.5))-log(besselI(sqrt(lambda*x),0.5*k-1,expon.scaled=FALSE))} > est<-mle(minuslog=ll,start=list(lambda=0.05,k=0.006))

Hello, I am trying to compute MLE for non-Gaussian AR(1). The error term follows a difference poisson distribution. This distribution has one parameter (vector[2]). So in total I want to estimate two parameters: the AR(1) paramter (vector[1]) and the distribution parameter. My function is the negative loglikelihood derived from a mixing operator. f=function(vector)

Hello Everybody in order to get some needed results out of my function i need to get my besselI function evaluated at some values which normally gave Inf or 0 (expon.scaled NAN) back. So I would like to increase the range in R from approxamittly 1e+320 to aabout 1e+500 or something like that. Is there any possibility or pacckage to do this easily? Thank You Sebastian Kaiser Institut for Statistics

Dear R users, I try to compute this summation, http://www.nabble.com/file/p25054272/dd.jpg where f(y|x) = Negative Binomial(y, mu=exp(x' beta), size=1/alp) http://www.nabble.com/file/p25054272/aa.jpg http://www.nabble.com/file/p25054272/cc.jpg In fact, I tried to use "do.call" function to compute each u(y,x) before the summation, but I got an error, "Error in X[i, ]

The besselK() function knows about overflows/underflows internally; there is a constant xmax_BESS_K in src/nmath/bessel.h (and referred to only in bessel_k.c), equal to 705.342, which is checked if expon.scaled is FALSE. (The equivalent number for bessel_i.c is 709, defined as exparg_BESS in bessel.h.) However, besselK(x) silently returns +Inf if x>705.342. This behavior is reasonable for

Dear R-xperts I have just written a little hypergeometric function, included below [the hypergeometric function crops up when solving a common type of ODE]. It works fine on single values of the primary argument z, but vectorizing it is getting confusing. The best I have come up with so far just tests for z being longer than 1 and if so, uses sapply() recursively. This is fine, except that it

Debian tries to build its packages on a variety of platforms. The arm platform compiled 0.90.1 (the last Debian release before the Debian package required an Atlas library, something we no longer require) failed in 'make check'. The log snippet follows; I traced this to the example(Bessel) code. > matplot(nu, t(outer(xx,nu, besselI)), type = 'l', ylim = c(-50,200), +

I post this message on R-help mailing list but someone emailed me that I can get helped if I post this one on R-devel mailing list. I have a small handheld pc having ARM process as a CPU. I installed debian and installed R (R-1.7.0 base and core, help html, latex-help) using apt-get command. Everything worked great except for drawing even simple graphs x <- 1:10 plot(x) I got error

I have a small handheld pc having ARM process as a CPU. I installed debian and installed R using apt-get command. Everything worked great except for drawing even simple graphs x <- 1:10 plot(x) I got error messages 1: Nonfinite axis limits [GScale(nan,nan,1, .); log=0] 2: relative range of values = 9.0072e+15 * EPS, is small (axis 1). 3: Nonfinite axis limits

Hi, I've been trying to use optim to minimise least squares for a function, and then get a guess at the error using the hessian matrix (calculated from numDeriv::hessian, which I read in some other r-help post was meant to be more accurate than the hessian given in optim). To get the standard error estimates, I'm calculating sqrt(diag(solve(x))), hope that's correct. I've found