similar to: gradient

Dear R users When I use OPTIM with BFGS, I've got a significant result without an error message. However, when I use OPTIMX with BFGS( or spg), I've got the following an error message. ---------------------------------------------------------------------------------------------------- > optimx(par=theta0, fn=obj.fy, gr=gr.fy, method="BFGS", >

Does anybody know of a function that implements the derivative (gradient) of the multivariate normal density with respect to the *parameters*? It?s easy enough to implement myself, but I?d like to avoid reinventing the wheel (with some bugs) if possible. Here?s a simple example of the result I?d like, using numerical differentiation: library(mvtnorm) library(numDeriv) f=function(pars, xx, yy)

Quick question: Which function do you use to calculate partial derivatives from a model equation? I've looked at deriv(), but think it gives derivatives, not partial derivatives. Of course my equation isn't this simple, but as an example, I'm looking for something that let's you control whether it's a partial or not, such as: somefunction(y~a+bx, with respect to x,

Hello everyone! I am having some trouble supplying the gradient function to nlm in R for windows version 2.2.1. What follows are the R-code I use: fredcs39<-function(a1,b1,b2,x){return(a1+exp(b1+b2*x))} loglikcs39<-function(theta,len){ value<-sum(mcs39[1:len]*fredcs39(theta[1],theta[2],theta[3],c(8:(7+len))) - pcs39[1:len] * log(fredcs39(theta[1],theta[2],theta[3],c(8:(7+len)))))

Hi, I would like to apply the L-BFGS optimization algorithm to compute the MLE of a multilevel multinomial Logistic Regression. The likelihood formula for this model has as one of the summands the formula for computing the likelihood of an ordinary (single-level) multinomial logit regression. So I would basically need the R implementation for this formula. The L-BFGS algorithm also requires

Hi , I am trying to get some estimator based on lognormal distribution when we have left,interval, and right censored data. Since, there is now avalible pakage in R can help me in this, I had to write my own code using Newton Raphson method which requires first and second derivative of log likelihood but my problem after runing the code is the estimators were too high. with this email ,I provide

Hi All, I would like to compute the simple finite-difference approximation to the gradient of a scalar function of a large number of variables (on the order of 1000). Although a one-time computation using the following function grad() is fast and simple enough, the overhead for repeated evaluation of gradient in iterative schemes is quite significant. I was wondering whether there are

Hello, I'm trying to fminimize the following problem: You have a data frame with 2 columns. data.input= data.frame(state1 = (1:500), state2 = (201:700) ) with data that partially overlap in terms of values. I want to minimize the assessment error of each state by using this function: err.th.scalar <- function(threshold, data){ state1 <- data$state1 state2 <- data$state2

Dear Rhelpers I have the problem with initial values, could you please tell me how to solve it? Thank you June > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2))) Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start, : NA in the initial gradient > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2),method="BFGS")) Error in optim(start, func, gr =

I'm trying to obtain numerical derivative of a probability computed with mvtnorm with respect to its parameters using grad() and jacobian() from NumDeriv. To simplify the matter, here is an example: PP1 <- function(p){ thetac <- p thetae <- 0.323340333 thetab <- -0.280970036 thetao <- 0.770768082 ssigma <- diag(4) ssigma[1,2] <- 0.229502120

R - Does anyone know of a cubic gradient descent package? I found grad.desc() but that only allows for a 2d function. I have 3 free parameters and thus am looking for a 3d function. Thank you, -- Edward H. Patzelt Research Assistant – TRiCAM Lab University of Minnesota – Psychology/Psychiatry VA Medical Center S355 Elliot Hall: 612-626-0072 www.psych.umn.edu/research/tricam [[alternative

Hi, I am trying to convert some Matlab code into R for running some experiments and I was wondering if there is some function in R which does the work of the gradient function in Matlab calculating the "gradient" of 1-, 2- and 3-d images. I only need the 3-d calculations for running these experiments. Many thanks and best wishes, Ranjan

To whom it may concern, I am a student at Macaleste College, and next semester Macalester is going to offer a course for CellBio that is mainly statistically based. For the most part the students will be using R for analysis. The problem is there will be some simple differential equations for the students to solve. The committee that in charge of the classes corriculam would like only to

I am attempting to extract the derivative/ gradient from this expression df1p <- deriv(f1, "P") > df1p expression({ .value <- s - c - a * P .grad <- array(0, c(length(.value), 1L), list(NULL, c("P"))) .grad[, "P"] <- -a attr(.value, "gradient") <- .grad .value }) So in this case I want to extract the "-a".

Hi all, I have some wind data (U and V components) and I would like to compute Vorticity and Divergence of these fields. Is there any R function that can easily do that? Thanks in advance for any help Igor Oliveira CSAG, Dept. Environmental & Geographical Science, University of Cape Town, Private Bag X3, Rondebosch 7701. Tel.: +27 (0)21 650 5774 South Africa Fax: +27 (0)21

The deriv() function takes an 'expression' as its first argument). I was wondering if the this function can take an array or a vector of expressions as its first argument. Aside, I saw how to give a vector argument to the second argument. like to have something like: deriv(c(~x^2+y^3, ~x^5+y^6), c("x","y")) the documentation for this function talks about being able to

Full_Name: Joerg Polzehl Version: 2.3.1 OS: x86_64, linux-gnu Submission from: (NULL) (62.141.176.22) I observed an incorrect behavior of function deriv when evaluating arguments of dnorm deriv(~dnorm(z,0,s),"z") expression({ .value <- dnorm(z, 0, s) .grad <- array(0, c(length(.value), 1), list(NULL, c("z"))) .grad[, "z"] <- -(z * dnorm(z))

Hello R community, I have been using R's inbuilt maximum likelihood functions, for the different methods (NR, BFGS, etc). I have figured out how to use all of them except the maxBHHH function. This one is different from the others as it requires an observation level gradient. I am using the following syntax: maxBHHH(logLik,grad=nuGradient,finalHessian="BHHH",start=prm,iterlim=2)

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

Hi I am trying to recover the hessian of a problem optimised with box-constraints. The problem is that in some cases, my estimates are very close to the boundary, which will make optim(..., hessian=TRUE) or optimHessian() fail, as they do not follow the box-constraints, and hence estimate the function in the unfeasible parameter space. As a simple example (my problem is more complex though,