similar to: Questions on implementing logistic regression

A web search on "gradient descent R" also brought up a bunch of stuff. Is any of this what you want? Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon, Feb 5, 2018 at 10:23 AM, Bert Gunter <bgunter.4567 at

Good morning, Is there a package that replaces the sgd package to explore the Gradient Descent (SGD) t echnique ? Best regards, *~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~ Tony *~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~* [[alternative HTML version deleted]]

Hi. guys, I am trying to write my own Stochastic Gradient Ascent for logistic regression in R. But it seems that I am having convergence problem. Am I doing anything wrong, or just the data is off? Here is my code in R - lbw <- read.table("http://www.biostat.jhsph.edu/~ririzarr/Teaching/754/lbw.dat" , header=TRUE) attach(lbw) lbw[1:2,] low age lwt race smoke ptl ht ui ftv

1. It might help if you could state more specifically what you want to do. 2. Maybe check here if you haven't already done so: https://cran.r-project.org/web/views/ Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon,

Hello R Help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and

Hello I will be applying MDS (actually Isomap) to make a psychological "concept map" of the similarities between N concepts. I would like to scale to a large number of concepts, however, the resulting N*(N-1) pairwise similarities is prohibitive for a user survey. I'm thinking of giving people random subsets of the pairwise similarities. Does anyone have recommendations for this

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

I am trying to model a dependent variable as a threshold function of my independent variable. What I mean is that I want to fit different intercepts to y following 2 breakpoints, but with fixed slopes. I am trying to do this with using ifelse statements in the nls function. Perhaps, this is not an appropriate approach. I have created a very simple example to illustrate what I am trying to do.

Yes, you're right. In fact, it's just an adaptation of a matlab command and the author advises using N^4 replications that's why it's the default in the function. The bistochastic matrix is not my subject of interest, but I need it to perform some random tranformation of a vector of incomes. Florent Bresson ----- Message d'origine ---- De : Richard M. Heiberger <rmh at

Thanks, I tried someting like this, but computation takes times for large matrices btransf <- function(y,X=length(y)^4) { N <- length(y) bm <- matrix(rep(1/N,N^2),N,N) for(j in 1:X){ coord <- sample(1:N,4,replace=T) d <- runif(1,0,min(bm[coord[1],coord[2]],bm[coord[3],coord[4]]))

Hi, I have a dataset and I want to fit a function to it. The function is variogram model (http://en.wikipedia.org/wiki/Variogram) The variogram model is defined by three parameters and I want them to be automatically optimized for real time data. I tried to use gafit {gafit} for this, but there are some data configuration, where optimal results given by gafit() are negative, which is not correct

Folks: I thought John Nash's comment below was profound and a possible Fortunes candidate: (Aside: I believe it applies to a great deal of what is discussed on this list, not just stochastic optimization.) Cheers, Bert ... (in the context of stochastic optimization) >... As with many tools in this domain, for effective use they > require more knowledge than many of their users

Hello, I am Mohiuddin Abdul Qader, final year student from dept of CSE in Bangladesh University of Engineering & Technology(BUET). My major was artificial intelligence & i finished my course on Machine Learning and Pattern Recognition this year. I am very keen to contribute in open source community. I have just completed my thesis on 'Location Based Structured Web Search'. For the

Dear useRs, I have just discovered that the R optim function does not return the number of iterations. I still wonder why line 632-634 of optim C, the iter variable is not returned (for the BFGS method for example) ? Is there any trick to compute the iteration number with function call number? Kind regards Christophe -- Christophe Dutang Ph.D. student at ISFA, Lyon, France website:

Hi, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is

While working on 'random walk' applications, I got interested in optimizing noisy objective functions. As an (artificial) example, the following is the Rosenbrock function, where Gaussian noise of standard deviation `sd = 0.01` is added to the function value. fn <- function(x) (1+rnorm(1, sd=0.01)) * adagio::fnRosenbrock(x) To smooth out the noise, define another

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)

> Only if the recursion is very deep. In practice, a recursive descent > parser isn't going to run out of stack space, nor will a quicksort or > binary-tree walker, The recursive-descent parser case has happened in practice: http://my.opera.com/hallvors/blog/2012/07/17/twitter-crashes-itself-with-commas?1 Also, I've seen some recursion-related PR's in Clang, although I think

Hello, i have a little suggestion: something like 'wine --no-x' It should disable any X output I need something like this, because i want to run Descent 3 dedicated server, however it creates status window so i can't run it in ssh :( Thanks