search for: steepest

I am trying to code a steepest ascent algorithm to optimize parameters used in a survivor function type problem. My unknown parameters (alpha, Beta0, and Beta1) for which I have been able to optimize using Newton's method. I keep getting an error because my alpha becomes negative and I can't calculate the likelihood. He...

..., x3, bs="cr")) ,with the smoothing parameter estimation method "magic"(default). Regarding this, I have two questions : Question 1 : In some cases the value of "mgcv.conv$fully.converged" becomes "FALSE", which tells me that the method stopped with a "steepest descent step failure". So I'd like to modify the arguments of magic() to make it easier to converge. But It doesn't seem like that I can do it by modifying the gam defaults through gam.control(). Is there any means to set magic() arguments from outside ? Question 2 : Sometimes the sm...

...with SSfpl. I fit the model using: model<-nls(temp~SSfpl(time,a,b,c,d)) summary(model) I am interested in the values of the lower and upper asymptotes (parameters a and b), but also in the gradient of the line at the inflection point (c) which I assume tells me my rate of increase when it is steepest (?). However, I cannot work out how to derive a slope estimate. I'm guessing it has something to do with the scaling parameter d but having searched the internet for hours I have not made any progress, and it is probably quite simple. Any help would be hugely appreciated! All the best Sam...

Hi All, Is the Levenberg-Marquardt algorithm available in R. This method combines the steepest descent algorithm and Newton's method. Thanks in Advance, Dermot MacSweeney. ************************************************************** Dermot MacSweeney NMRC, Email: dsweeney at nmrc.ucc.ie Lee Maltings, Tel: +353 21 904178 Prospect Row, Fax: +353 21 270271 Cork, WWW: http:/...

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

Hi I am looking to perform a discrete mean-variance optimisation, specifically to maximise the ratio of portfolio mean over portfolio standard deviation for a portfolio of several hundred stocks through discrete position size holdings in each stock, where all position sizes must be elements of a small finite set of integer amounts which include zero. I don't think any of the standard R

...d on the three vertexes. The problem is: Sometimes the triangle indices are not given back correctly, they just default to 1 leading to wrong results. The following lines can be used to visualize it using the rgl library. If the error occurs (may be architecture depending) the interpolation "steepest" part of the interp() surface will not be hit by the interpp() interpolation points. library(akima) library(rgl) data(akima) # data rgl.spheres(akima$x,akima$z , akima$y,0.5,color="red") rgl.bbox() # interp: akima.li <- interp(akima$x, akima$y, akima$z, xo=seq...

Dear all, I would like to use the Levenberg-Marquardt algorithm for non-linear least-squares regression using function nls.lm. Can anybody help me to find a a way to compute confidence intervals on the fitted parameters as it is possible for nls (using confint.nls, which does not work for nls.lm)? Thank you for your help Michael

...I am aware that medium-experienced R user might teach some "wrong" stuff, but our main idea behind this mailing list are real basic questions like understanding and getting used to the command line (read in your data etc.) because we made the experience that these few steps are the steepest and might stop somebody from further using R. I would like to know if there are some people on this list, who call themselves beginners or have students who are about to make their first steps in R and would like to use this list as well. best regards Martin https://lists.uni-wuerzburg.d...

...all sorts of fields! It is an exceedingly fine piece of software - fast, reliable and easy to set up. On some early problems that I looked at, it was twice as fast as the equivalent NAG routine. To quote from the manual, "A hybrid method is used combining features from the Newton -Raphson, Steepest descent and Marquardt methods and calculating and maintaining an approximation to the first derivative matrix using the ideas of Broyden." Now that I have converted to R, I will miss my trusted friend. I have started using nls() but have not accumulated enough experience to compare the two....

Hi R-help! I'm trying to understand how R's nlm function updates its estimate of the Hessian matrix. The Dennis/Schnabel book cited in the references presents a number of different ways to do this, and seems to conclude that the positive-definite secant method (BFGS) works best in practice (p201). However, when I run my code through the optim function with the method as "BFGS",

GENERAL REFERENCE ON NONLINEAR OPTIMIZATION? What are your favorite references on nonlinear optimization? I like Bates and Watts (1988) Nonlinear Regression Analysis and Its Applications (Wiley), especially for its key insights regarding parameter effects vs. intrinsic curvature. Before I spent time and money on several of the refences cited on the help pages for "optim",

Rob J Hyndman gives great explanation here (https://robjhyndman.com/hyndsight/estimation/) for reasons why results from R's arima may differ from other softwares. @iacobus, to cite one, 'Major discrepancies between R and Stata for ARIMA' (https://stackoverflow.com/questions/22443395/major-discrepancies-between-r-and-stata-for-arima), assign the, sometimes, big diferences from R