similar to: gradient

> On Apr 5, 2018, at 2:00 PM, g l <gnulinux at gmx.com> wrote: > >> Sent: Thursday, April 05, 2018 at 4:40 PM >> From: "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us> >> >> the coef function. >> > > For the benefit of other novices, used the following command to read the documentation: > > ?coef > > Then tried and

> Sent: Thursday, April 05, 2018 at 4:40 PM > From: "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us> > > the coef function. > For the benefit of other novices, used the following command to read the documentation: ?coef Then tried and obtained: > cvalue100<-coef(graphmodelp~100) > cvalue100 NULL Then looked at the model values which of course correspond

I have never found the R symbolic differentiation helpful because my functions are typically quite complicated, but was prompted by Steve Ellison's suggestion to try it out in this case: ################# reprex (see reprex package) graphdta <- read.csv( text = "t,c 0,100 40,78 80,59 120,38 160,25 200,21 240,16 280,12 320,10 360,9 400,7 ", header = TRUE ) nd <- c( 100, 250,

> Sent: Friday, April 06, 2018 at 5:55 AM > From: "David Winsemius" <dwinsemius at comcast.net> > > > Not correct. You already have `predict`. It is capale of using the `newdata` values to do interpolation with the values of the coefficients in the model. See: > > ?predict > The ? details did not mention interpolation explicity; thanks. > The

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

> On Apr 6, 2018, at 3:43 AM, g l <gnulinux at gmx.com> wrote: > >> Sent: Friday, April 06, 2018 at 5:55 AM >> From: "David Winsemius" <dwinsemius at comcast.net> >> >> >> Not correct. You already have `predict`. It is capale of using the `newdata` values to do interpolation with the values of the coefficients in the model. See:

Readers, Data set: t,c 0,100 40,78 80,59 120,38 160,25 200,21 240,16 280,12 320,10 360,9 400,7 graphdata<-read.csv('~/tmp/data.csv') graphmodeld<-lm(log(graphdata[,2])~graphdata[,1]) graphmodelp<-exp(predict(graphmodeld)) plot(graphdata[,2]~graphdata[,1]) lines(graphdata[,1],graphmodelp) Please what is the function and syntax to obtain gradient values for the model curve at

> On Apr 6, 2018, at 8:03 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > >> On Apr 6, 2018, at 3:43 AM, g l <gnulinux at gmx.com> wrote: >> >>> Sent: Friday, April 06, 2018 at 5:55 AM >>> From: "David Winsemius" <dwinsemius at comcast.net> >>> >>> >>> Not correct. You already have

Dear R-help subscribers; I have a question regarding making gradients in R. I've searched on the web, but was only able to find functions that make a gradient between color X and Y, which is not what I want. I want to 'pick out' individual, smaller pieces of a gradient based on a range of numbers. Say that I have a range of numbers, leading from 0 to 1. Then I want 0 to refer to

Hi all, I would like to use optim() to estimate the equation by the log-likelihood function and gradient function which I had written. I try to use OPG(Out Product of Gradient) to calculate the Hessian matrix since sometime Hessian matrix is difficult to calculate. Thus I want to pick the Gradient matrix from the gradient function. Moreover, could R show the process of calculation on gradient

//Referring to the response posted many years ago, copied below, what is the specific criterium used for singularity of the gradient matrix? Is a Singular Value Decomposition used to determine the singular values? Is it the gradient matrix condition number or some other criterion for determining singularity? // //Glenn // / / /> What does the error 'singular gradient' mean

Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: 7bit X-Virus-Scanned: by amavisd-new X-Spam-Checker-Version: SpamAssassin 2.63 (2004-01-11) on hypatia.math.ethz.ch X-Spam-Level: **** X-Spam-Status: No, hits=4.2 required=5.0 tests=MSGID_FROM_MTA_HEADER,RCVD_IN_BL_SPAMCOP_NET autolearn=no version=2.63 Dear r-help list, my question is about ordination technics:

Hi, I'm very interested in using the constrOptim() function currently in the R-devel sources. In particular, I'm trying to fit point process conditional intensity models via maximum likelihood. However, I noticed that the gradient of the objective function must be supplied for all but the Nelder-Mead method. I was wondering why this was because optim() itself does not require a gradient

Dear all, I am using the functions 'optim' and 'nlminb'. For both, you can provide a function which computes the gradient of the objective function (to enhance speed and precision). In my case, both the objective function and the gradient take time to be computed and share many common computations (similar matrix, products, etc...). Therefore, I have to compute these

I use optim to compute maximum likelihood estimations without giving an analytical gradient to optim. However, I would like to get an output of the final numerical gradient vector and the final matrix of contributions to the gradient. But I did not find any mention of this kind of output in help pages. Does anyone know how to do that ? Stephane Luchini GREQAM Marseille, France

Hello, I hope this doesn't turn into a statistics question but here I go. I am using the nls function with a Gaussian distribution, see coding below. When I run the nls I get an error back saying that I have a linear gradient. I then, of course am unable to do anything else. The data that I am using are intensity values from some mass spectrometry data. Is there something I can

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", >

Hi, I wonder whether there is a way to generate a polygon (a triangle in my case) with color gradient using grid.polygon() in package grid? I tried something like library(grid) grid.polygon(x=c(0, 0.5, 1), y=c(0.5, 1, 0.5), gp=gpar(col=NA, fill=colorRampPalette(c("green", "lightgray"), space="Lab")(200))) But am only

Hi all, Is there a way to fill a rectangle or polygon with a color and/or transparency gradient? This would be extremely useful for me in terms of adding some additional information to some plots I'm making, especially if I could define the gradient on my own by putting functions into rgb something like rgb( r=f(x,y), g=f(x,y), b=f(x,y), alpha=f(x,y) ). Not so important whether the

> Date: Wed, 20 Mar 2002 14:31:03 +0100 (CET) > From: =?iso-8859-1?Q?G=F6ran_Brostr=F6m?= <gb at stat.umu.se> > Subject: [R] optim with gradient > > I want to maximise a function using 'optim' with a method that requires > the gradient, so I supply two functions, 'fun' for the function value > and 'd.fun' for its gradient. My question is: Since