similar to: Point estimate from loess contour plot

I'm reading Ripley-Venables "Modern Applied Statistics with S - Fourth edition" , at the same time trying the examples proposed in the book using R 1.8.1 under linux. Now I'm trying the following code from the book (example code of spatial statistics at page 76) with R : | data(topo) library("spatial") topo.loess<-loess(z ~ x * y, topo, | degree= 2,span=0.25) |

If a run a LOESS model and then produce a smoothed surface: Is there any way to determine the coordinates of the local maxima on the surface? [[alternative HTML version deleted]]

R- Users: Can someone indicate what I am during wrong? This is a script essentially from Venerable & Ripley's text on interpolating a surface with loess function but I can not get it to run. Thanks. John Lewis Professor McGill University Montreal library(MASS) library(modreg) data(topo) par(mfcol=c(2,2), pty="s") topo.loess <- loess(z ~ x * y, topo, degree=2,

Hi Folks, I'm probably overlooking some small point, but can't see it. Trying the "Local trend surfaces" example from p.437 of MASS (3rd edn) by V&R, all goes well until the line contour(topo.mar$x, topo.mar$y, topo.lo$fit, levels=seq(700,1000,25), xlab="fi t", ylab="") which produces the response: Error in contour.default(topo.mar$x,

Apologies for the mass mailing today! I am attempting to produce a contour plot for phsical data on a map matrix. I have a small number of data points which each has an (x,y) co-ordinate together with a corresponding value which I would like to cvreate a contour plot for. I have tried the following code: contour(data$x,data$y,data$value) but am told: Error in contour.default(data$x, data$y,

Hi, For some reason I have been unable to use the predict function when I desire the standard error to be calculated too. For example, when I try the following: l<- loess(d~x+y, span=span, se=TRUE) p<- predict(l, se=TRUE) I get the following error message: Error in vector("double", length) : vector size cannot be NA In addition: Warning message: In N * M1 : NAs produced by

I am trying to create a topographic map of an island - the filled.contour function works fine except i am experiencing difficulty trying to represent the sea properly. Basically I want the default colour blue for any instance where z=0, if I simply use the default topo.color I get shades of blue for z values up to 220 metres. Is there a way in which I can specify terrain.color for all values of

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

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

For some reason the following code is not plotting as I want it to. I want to plot a "loess" line plotted over a scatter plot. I get a jumble, with lines connecting all the points. I had a similar problem with "lowess". I solved that by dropping "NA" rows from the data columns. Please help. library(stats) attach(gini_pci_wdi_narm) plot(ny_gnp_pcap_pp_kd, si_pov_gini)

Dear R-community, maybe someone can help me with this: I've been using the loess() smoother for quite a while now, and for the matter of documentation I'd like to resolve the acronym LOESS. Unfortunately there's no explanation in the help file, and I didn't get anything convincing from google either. I know that the predecessor LOWESS stands for "Locally Weighted

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 >

I have a question about running an optimization function on an existing LOESS function defined in R. I have a very large dataset (1 million observations) and have run a LOESS regression. Now, I want to run a Newton-Raphson optimization to determine the point at which the slope change is the greatest. I am relatively new to R and have tried several permutations of the maxNR and nlm functions with

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

Hello Masters, I run the loess() function to obtain local weighted regressions, given lowess() can't handle NAs, but I don't improve significantly my situation......, actually loess() performance leave me much puzzled.... I attach my easy experiment below #------SCRIPT---------------------------------------------- #I explore the functionalities of lowess() & loess() #because I have

Gurus, I used predict(loess(Y~X)); where Y and X are of the same length. But there are same NA's in both Y and X. Those NA's are in the same locations in Y and X. The following is the error messageI got: Error in "[<-"(*tmp*, , i, value = predict(loess(Y~X))) : number of items to replace is not a multiple of replacement length If I replaced the NA's with a

Dear all, To draw a lowess line on a plot was a piece of cake; to draw a loess line, however, seems not that easy. Is the loess plotting implemented at all in relation to the loess function, or do I have to look in add-on packages? Thanks, Marcin

Hello, I'm currently using a tool that provides a Loess fitting, but I obtained results that are slightly different from those provided by R implementation of the Loess. That's why I would like to know if you could give me a source (bibliography or web) that explains in a clear way each step of the algorithm, with the possible options to choose, etc.. in order for me to understand those

Hi all, I'm fitting a line to my dataset. Later I want to predict missing values that exceed the [min,max] interval of my empirical data, therefore I choose surface="direct" for extrapolation. l1<-loess(y1~x1,span=0.1,data.frame(x=x1,y=y1),control=loess.control(surface="direct")) In my application it is highly important that the fitted line intercepts at the point of

Not sure what I'm doing wrong. Can't seem to get loess values. It looks like loess is returning the same values as the input. j <-loess(x1$total~as.numeric(index(x1) plot(x1$total,type='l', ylab='M coms/y global',xlab='') lines(loess(total~as.numeric(index(x1)),x1)) The plot statement works fine No errors with the "lines" statement But I don't