similar to: LOESS function Newton optimization

Hi R-users, Recently, I use maxNR function to find maximizer. I have error appears as follows Error in maxNRCompute(fn = fn, grad = grad, hess = hess, start = start, : NA in the initial gradient My code is mu=2 s=1 n=300 library(maxLik) set.seed(1004) x<-rcauchy(n,mu,s) loglik<-function(mu) { log(prod(dcauchy(x,mu,s))) } maxNR(loglik,start=median(x))$estimate Does anyone know how

Greetings, I use maxNR function under maxLik package to find the REML estimates of the parameters of variance components in heteroskedastic linear regression models. I assume that in the model there is additive/multiplicative/mixed heteroskedasticity and I need estimate the respective parameters of additive/multiplicative/mixed variance components. For my research purposes I make a

Hello everyone... We were trying to implement the Newton-Raphson method in R, and estimate the parameters a and b, of a function, F, however we can't seem to implement this the right way. Hope you can show me the right way to do this. I think what we want R to do is to read the data from the website and then peform maxNR on the function, F. Btw the version of R being used is "RGui for

Dear All, Hello! I have some questoins in R programming as follows: Question 1- How to take the integral of this function with respect to y, such that x would appear in the output after taking integral. f(x,y)=(0.1766*exp(-exp(y+lnx))*-exp(y+lnx))/(1-exp(-exp(y+lnx))) y in (-6.907,-1.246) It is doable in maple but not in R. At least I could not find the way. p.s: result from maple is:

Dear R Users: The functions of our "micEcon" package [1,2] can be subdivided into three categories: - microeconomic demand and firm models - sample selection models (mainly selection()) - routines for (likelihood) maximisation (e.g. maxLik(), maxNR(), maxBHHH()) (mainly used for ML estimation of sample selection models) Although sample selection models are often used in

Hello, I?m with a problem in using nonlinear quantile regression, the function nlrq. I want to do a quantile regression o nonlinear function in the form a*log(x)-b, the coefficients ?a? and ?b? is my objective. I try to use the command: funx <- function(x,a,b){ res <- a*log(x)-b res } Dat.nlrq <- nlrq(y ~ funx(x, a, b), data=Dat, tau=0.25, trace=TRUE) But a can?t solve de problem,

Hi R-users, Does R has a topic on newton's method? Thank you for the info.

I'm updating the loess routines to allow for, among other things, arbitrary local polynomial degree and number of predictors. For now, I've given the updated package its own namespace. The trouble is, panel.loess still calls the original code in package:stats instead of the new loess package, regardless of whether package:loess or package:lattice comes first in the search list. If I

by() fails for 1-column matrices and dataframes: X <- data.frame(a=1:10) g <- gl(2,5) by(X, g, colMeans) Suggested fix: --- by-old.R 2007-12-10 15:26:22.501086600 +0100 +++ by.R 2007-12-10 15:25:58.390477200 +0100 @@ -26,7 +26,7 @@ IND[[1]] <- INDICES names(IND) <- deparse(substitute(INDICES))[1] } else IND <- INDICES - FUNx <-

Please do as we ask (repeatedly) and study the help page before posting. 'family' is a separate argument, not part of loess.control, as the help page correctly documents. If you use cars.lo2 <- loess(dist ~ speed, cars, family = "symmetric", control = loess.control(surface = "direct", iterations = 20)) cars.lo2$pars$iterations it prints *20*, as it is

Dear Anupam Tyagi, You didn't include your data, so it's not possible to see exactly what happened, but I think that you misunderstand the object that loess() returns. It returns a "loess" object with several components, including the original data in x and y. So if pass the object to lines(), you'll simply connect the points, and if x isn't sorted, the points

Thanks, John. However, loess.smooth() is producing a very different curve compared to the one that results from applying predict() on a loess(). I am guessing they are using different defaults. Correct? On Thu, 23 Mar 2023 at 20:20, John Fox <jfox at mcmaster.ca> wrote: > Dear Anupam Tyagi, > > You didn't include your data, so it's not possible to see exactly what >

Dear R community, I am trying to understand how the predict function, specifically, the predict.loess function works. I understand that the loess function calculates regression parameters at each data point in 'data'. lo <- loess ( y~x, data) p <- predict (lo, newdata) I understand that the predict function predicts values for 'newdata' according to the loess regression

Hello, I was hoping that someone familiar with the implementation details of the loess algorithm might be able to help me resolve some difficulties I am having. I am attempting to reproduce some of the functionality of the loess() function in C++. My primary motivation is that I would like to understand the algorithm in detail. So far I have managed to create a working port in C++ for the

Hi, I have 2 questions regarding using loess function in stats package. 1. I have about 1 million points. I did loess with alpha=0.5 and degree=1 and 2 regressors. It has run for more than 5 hrs on 64bit 16CPU and 64GB server (with no other process running). I am wondering if this is usual? And if there is anyway to make it run faster? 2. I noticed a difference in RAM consumption when specifying

I am trying to (semi) calculate the confidence intervals for a loess smoother (function: loess()), but have been thus far unsuccessful. The CI for the loess predicted values, yhat, are apparently yhat +- t*s * sqrt(w^2), where s is the residual sum of squares and w is the weight function Correct me of I'm wrong, but R uses the tricubic function (1-abs(z)^3)^3, where z = (x-xi)/h, where h

Hi, I wonder why my attempt to extend an existing loess fit to a new data set is producing error. I was trying the following: dat = read.csv(choose.files()) x = dat[,2]; y = dat[,1] x.sort = sort(x) y.loess = loess(y~x, span=0.75) # For testing the above fit with a new dataset: test = read.csv(choose.files()) # test data new_x = test [,1]; new_y = test[,2] new_x.sort = sort(new_x) predicted

>From what I understand, loess in R uses the standard tri-cube function. SAS/INSIGHT offers loess with Gaussian weights. Is there a function in R that does the same? Also, can anyone offer any references comparing properties between tri-cube and Gaussian weights in LOESS? Thanks. - Andr? -- View this message in context:

Hi everyone. I'm trying to do a loess with missing value on independant variable. doc = c(2.27904, 2.59536, 7.44696, NA, 6.24264, 4.58400, 5.79192, 5.39502, 7.41216, 4.09440, 4.22868, 4.24620, 5.43804, 1.95528); distance = c(26.5,56.5, 90.3, 123.0, 147.5, 176.0, 215.7, 229.3, 252.0, 325.3, 362.0, 419.3, 454.6, 470.0); myloess = loess(doc ~ distance, na.action = na.omit); plot(distance,

Hi, I am trying to create a loess smooth from hydrologic data. My goal is to create a smooth line that describes discharge at a certain point in time. I have done this using the 'lowess' function and had no problem, but I'm having some difficulty with loess. I am inputting the date ('date') and discharge ('q') values using the 'scan' function, then inputting