similar to: rgamma with rate as vector

Hi all; I recently have been used 'maxLik' function for maximizing G2StNV178 function with gradient function gradlik; for receiving this goal, I write the following program; but I have been seen an error  in calling gradient  function; The maxLik function can't enter gradlik function (definition of gradient function); I guess my mistake is in line ******** ,that the vector  ‘h’ is

Dear R users, i would like to transform the following function from R-code to C-code and call it from R in order to speed up the computation because in my other functions this function is called many times. `dgcpois` <- function(z, lambda1, lambda2) { `f1` <- function(alpha, lambda1, lambda2) return(exp(log(lambda1) * (alpha - 1) - lambda2 * lgamma(alpha))) `f2` <-

How to convert the following ms() in Splus to Optim in R? The "Calc" function is also attached. ms(~ Calc(a.init, B, v, off, d, P.a, lambda.a, P.y, lambda.y, 10^(-8), FALSE, 20, TRUE)$Bic, start = list(lambda.a = 0.5, lambda.y = 240), control = list(maxiter = 10, tol = 0.1)) Calc <- function(A.INIT., X., V., OFF., D., P1., LAMBDA1., P2., LAMBDA2., TOL., MONITOR.,

I use "while" loop but it produces an errro. I have no idea about this. Error in "[<-"(`*tmp*`, i, value = numeric(0)) : nothing to replace with The problem description is The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0

I have an error for a simple optimization problem. Is there anyone knowing about this error? lambda1=-9 lambda2=-6 L<-function(a){ s2i2f<-(exp(-lambda1*(250^a)-lambda2*(275^a-250^a)) -exp(-lambda1*(250^a)-lambda2*(300^a-250^a))) logl<-log(s2i2f) return(-logl)} optim(1,L) Error in optim(1, L) : function cannot be evaluated at initial parameters Thank you in advance -- View this

Hello, The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updataed alpha, replicate 1) and 2) until alpha is converged to a value I coded 1) and 2) (it works), but faced some

Dear R and S users, I've been working on fitting finite mixture of negative exponential distributions using maximum likelihood based on the example given in MASS. So far I had much success in fitting two components. The problem started when I tried to extend the procedure to fit three components. More specifically, likelihood = sum( ln(c1*exp(-x/lambda1)/lambda1 + c2*exp(-x/lambda2)/lambda2

Dear All The problem is about regularization methods in multiple regression when the independent variables are collinear. A modified regularization method with two tuning parameters l1 and l2 and their product l1*l2 (Lambda 1 and Lambda 2) such that l1 takes care of ridge property and l2 takes care of LASSO property is proposed The proposed method is given

Hi All, I am using the package 'penalized' to perform a multiple regression on a dataset of 33 samples and 9 explanatory variables. The analysis appears to have performed as outlined and I have ended up with 4 explanatory variables and their respective regression coefficients. What I am struggling to understand is where do I get the variance explained information from and how do I

Dear R people, This is a possibly silly question, but the rgamma function takes the shape and scale arguments and simulates gamma rvs corresponding to those values, right? But the shape and scale parameters have to be positive, right? However, rgamma quite happily returns to me values for negative values of shape and scale, and in some cases returns negative values eg. > rgamma(1, 1, -1) [1]

Estimado Jorge Perdóneme que lo moleste de nuevo, hay otra condición además de que sum(y)=1 y es que y[1] tiene que dar 0 en el ejemplo y<- c (0.0000000000, 0.6321985783, 0.2325728597, 0.0855587737, 0.0314753138, 0.0115791209, 0.0042597205, 0.0015670636, 0.0005764905, 0.0002120790) y[1]=0 sum(y)=1 esto se utiliza en el paquete EpiEstim donde se

Hi, Has anyone encountered the problem of rgamma function in C? The following simplified program always dies for me, and I wonder if anyone can tell me the reason. #include <Rmath.h> #include <time.h> #include <Rinternals.h> SEXP generateGamma () { srand(time(NULL)); return (rgamma(5000,1)); } Has anyone encountered a similar problem before? Is there another way

I was trying to generate random numbers with a gamma distribution. In R the function is: rgamma(n, shape, rate = 1, scale = 1/rate). My question is that if X~gamma(alpha, beta) and I want to generate one random number where do I plug alpha and beta in rgamma? and, what is the meaning and use of rate? Thanks for your attention, Jorge [[alternative HTML version deleted]]

dgamma and friends in S are documented as dgamma(x, shape, rate=1) pgamma(q, shape, rate=1) qgamma(p, shape, rate=1) rgamma(n, shape, rate=1) whereas R has dgamma(x, shape, scale=1, log = FALSE) pgamma(q, shape, scale=1, lower.tail = TRUE, log.p = FALSE) qgamma(p, shape, scale=1, lower.tail = TRUE, log.p = FALSE) rgamma(n, shape, scale=1) Note the use of rate vs scale. Indeed, as both S and

Hello fellow R users, I wanted to view the density on the standard deviation scale of a gamma(0.001, 0.001) prior for the precision. I did this as seen in the code below and found that for some reason rgamma is giving many values equal to zero, which is strange since a gamma distribution is continuous. What is going on here? Thanks for any help in advance. Greg > x1 <- rgamma(10000,

Dear R users,? ? ? ? ? ? ? ? ? ? ? ? ? I am using "Glmnet" package in R for applying "elastic net" method. In elastic net, two penalities are applied one is lambda1 for?LASSO and lambda2 for ridge ( zou, 2005) penalty.?How can I? write the code to? pre-chose the? lambda1 for?LASSO and lambda2 for ridge without using cross-validation Thanks in advance? Tayo? [[alternative

Many thanks to those took time replied to my question. They were very helpful and I solved my problem by reparameterization. With the help of optim() the fitting procedure is very robust and insensitive to initial starting value. Once again, many thanks. Kevin -------- Original post --------- >Dear R and S users, > >I've been working on fitting finite mixture of negative

Dear all, I have two functions (f1, f2) and 4 unknown parameters (p1, p2, p3, p4). Both f1 and f2 are functions of p1, p2, and p3, denoted by f1(p1, p2, p3) and f2(p1,p2,p3) respectively. The goal is to maximize f1(p1, p2, p3) subject to two constraints: (1) c = k1*p4/(k1*p4+(1-k1)*f1(p1,p2,p3)), where c and k1 are some known constants (2) p4 = f2(p1, p2, p3) In addition, each parameter

Dear R users, I am using "enet" package in R for applying "elastic net" method. In elastic net, two penalities are applied one is lambda1 for LASSO and lambda2 for ridge ( zou, 2005) penalty. But while running the analysis, I realised tht, I optimised only one lambda. ( even when I looked at the example in R, they used only one penality) So, I am

Hi, I'm seeing something that may be a bug in R's standalone math library, which is packaged by Debian as r-mathlib. I reported it to the Debian BTS as http://bugs.debian.org/657573 I'm using Debian squeeze, and the code was tested with r-mathlib 2.11.1-6 (default on stable) and 2.14.1-1 (from testing/unstable). I summarize this report below. The following code with the R math