similar to: Symbolic Maximum Likelihood in R

Hi, To my knowledge, Ryacas can do symbolic computation in R, like Mathematica or Maple. I wonder if it (or any other R package) can solve symbolically a system of equations? I have a system of four equations in four variables (non-linear, but not very hard to solve) and wonder if Ryacas or any other package can do it. I do not find the solution in Ryacas. Can it find a symbolic solution

Can someone help me understand the following: > D(expression(dnorm(x, mean)), "mean") [1] 0 > sessionInfo() R version 2.2.1, 2005-12-20, i386-pc-mingw32 attached base packages: [1] "methods" "stats" "graphics" "grDevices" "utils" "datasets" [7] "base" By my computations, this should be

Given X1,...,Xn ~ t_k(mu,sigma) student t distribution with k degrees of freedom, mean mu and standard deviation sigma, I want to obtain the MLEs of the three parameters (mu, sigma and k). When I try traditional optimization techniques I don't find the MLEs. Usually I just get k->infty. Does anybody know of any algorithms/functions in R that can help me obtain the MLEs? I am especially

Hello, I was asked to try the following code on R, gamma.mles function (xx,shape0,rate0) { n<- length(xx) xbar<- mean(xx) logxbar<- mean(log(xx)) theta<-c(shape0,rate0) repeat { theta0<- theta shape<- theta0[1] rate<- theta0[2] S<- n*matrix(c(log(rate)-digamma(shape)+logxbar,shape/rate-xbar),ncol=1) I<- n*matrix(c(trigamma(shape),-1/rate,-1/rate,shape/rate^2),ncol=2)

I'm tinkering around in Ryacas trying to find an easy way to get the first and second partial derivatives of mu and sigma from a normal distribution (actually a bitterly ugly likelihood but this example works for now). I've done all of the work in Mathematica, but I then need to manually write R code whereas I think Ryacas will give me the expression such that I can export for Tex and

This is a very basic question, but I am a bit confused with optim. I want to get the MLEs using optim which could replace the newton-raphson code I have below which also gives the MLEs. The function takes as input a vector x denoting whether a respondent answered an item correctly (x=1) or not (x=0). It also takes as input a vector b_vector, and these are parameters of test items (Rasch estimates

I have a function that is an iterative process for estimating some MLEs. I want to print some progress to screen as the process iterates. I would like to try and line things up nicely in the R window, but am not sure the best way to do this. Below is a toy example. Suppose I want the value of 10 to be just below "iteration" and the value of -1234 to be just below 'Log

Dear all I'm trying to caculate the MLEs for parameters of Inverse Gaussian distribution (in a k-sample problem with common mean) by using EM-Algorithm. I found some package for EM-Algorithm that are useful for missing or incomplete data and are not helpful for solving my problem. (Exactly, the problem is: Let Xij, i=1,..,k , j=1,...,ni, be a random sample from IG(?,?i). So the

This may be a question to R-development but I'm not sure. Symbolic differentiation is implemented in R (maybe not for extremely complex expressions), but it proves that it can be done. I know that in C++ it can be done (symbolic c++), do you think in R it can be programmed just using the R language without resorting to external sources? Does anyone know or can estimate the amount of resources

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

I am trying to use the optim command to get the MLEs for a number of parameters based on some data. Is it possible to write a program that maximizes the likelihood and stores the parameters (many times)? I need to do this a lot of times, each time the data will be updated with more entries (rows). I need to see how the new data is affecting the previous parameter estimates of the model. What is

Hi, this is not an R-help post, but I found this extract below that was written by a leading mathematician back in 1999 when he was talking about statistics and computing. I found it interesting to share and I ask your opinion do you think this still holds today or things have changed? Thanks. ?...we would also like to mention that in our opinion Mathematica provides an excellent and indeed

There is either something wrong with either me or is Yacas/Ryacas doing odd things. See below I ask to simpify an expression and the there's output! If this is a simplification.. well.. Do you think there is something set wrong somewhere. Thanks. > library(Ryacas) Loading required package: XML > a=Sym("a") > N=Sym("N") > b=Sym("b") >

Hi there, I am looking to fit a two-way random effects model to an *unblalanced* layout, y_ijk = mu + a_i + b_j + eps_ijk, i=1,...,R, j=1,...,C, k=1,...,K_ij. I am interested first of all in estimates for the variance components, sigsq_a, sigsq_b and sigsq_error. In the balanced case, there are simple (MM, MLE) estimates for these; In the unbalanced setup,

Dear List: A while back I found a web site called the Rosetta Stone for Computer Algebra Systems showing how to do similar operations in different symbolic programs. This has been very helpful for moving between Mathematica, Yacas and Maxima. I wonder if anyone maintains something akin to this for translating R to SAS, Minitab, Stata etc? I am familiar with the various (useful) reference

Thanks for the response. Yes, I did study the vignette but did not understand it fully. Anyway, I have tried once again now. I am happy to say that I have got what I wanted. library(Ryacas) x <- Sym("x");U <- Sym("U");x0 <- Sym("x0");C <- Sym("C") my_func <- function(x,U,x0,C) { return (U/(1+exp(-(x-x0)/C)))} FirstDeriv <-

Dear Ben, you answerd to Nancy Shackelford about Clarks 2Dt function. Since the thread ended just after your reply, I would like to ask, if you have an idea how to use this function in R I defined it the following way: function(x , p, u) { (p/(pi*u))*(1+(x^2/u))^(p+1) } and would like to fit this one to my obeservational data (count) [,1] [,2] [1,] 15 12 [2,] 45 13 [3,]

Hello, I am brand new in this site as well as in R. Please accept my early apology if it's not the right place to ask such a question. I have the following equation: b=[2(1-a)(1+2a)^0.5]/(1+3a) How can I solve the above equation for 'a' using R? Thanks in advance. KD [[alternative HTML version deleted]]

Hi all, I am trying to implement the following matlab code with Ryacas : syms U x x0 C d1=diff(U/(1+exp(-(x-x0)/C)),x); pretty(d1) d2=diff(U/(1+exp(-(x-x0)/C)),x,2); pretty(d2) solx2 = solve(d2 == 0, x, 'Real', true) pretty(solx2) slope2=subs(d1,solx2) I have tried the following : library(Ryacas) x <- Sym("x");U <- Sym("U");x0 <-

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