similar to: locpoly (was: no subject)

hello R-users.could you please help me on this one?i have 2 vectors x1 and y1 and both have the same size.i want to use the locpoly function from the kernsmooth package in order to estimate a regression function.at each estimation i want to leave one observation out,and i want my estimation to be at the observation i left out.i tried : for (j in 1:length(x1)) +

Hi all, I have a data frame called "rst", see below: ------------------------------------------------------------------------------------------ # This is a paste able example # In case you don't have "KernSmooth" package installed, please uncomment below line. # install.packages("KernSmooth") library(KernSmooth) rst <- data.frame(hsp = rnorm(23), dal =

Dear list, I've got a question concerning difference between loess and locpoly. I have to use a plug-in method to chose a bandwith so I take locpoly method to fit a curve. My problem is:I know how to get predicted values in loess: m=loess(y~x) y_fitted=predict(m). But how to get the same in locpoly? I computed like this: bw=dpill(x, y, blockmax = 5, divisor = 20,trim = 0.01, proptrun =

Dear R users, I am currently using the locpoly function from the KernSmooth package to estimate densities. However, I have some trouble understanding how this estimation technique is implemented in R. My main concern comes from the fact that this function gives negative estimates when the bandwidth is sufficiently large (mainly in the tails of distributions). I have read some articles on this

is there a way of estimating derivative curves, similar to the ones we get from 'locpoly', from 'loess' estimation. i am interested in estimation of 1st and 2nd derivatives... --------------------------------- [[alternate HTML version deleted]]

Updating my 1.8.0 R installation (>update.packages() ) I obtain the following (SORRY FOR THE LENGTH OF THE LOG BUT IT HELPS!!!): ................ downloaded 135Kb KernSmooth : Version 2.22-11 in /usr/lib/R/library Version 2.22-12 on CRAN Update (y/N)? y mgcv : Version 0.9-3.1 in /usr/lib/R/library Version 0.9-6 on CRAN Update (y/N)? y trying URL

Hello All, Is there a local bandwidth selection routine for local polynomial regression (locpoly) ? Thanks Chinthaka Kuruwita

Hi, I want to fit a kernel density estimator by bkde of library KernSmooth. I need only the density value at the point 0. I do not understand the following behaviour: > q <- rnorm(100) > bkq <- bkde(q, bandwidth=0.11, gridsize=1, range.x=c(0,0)) Error in 0:L : NA/NaN argument > bkq <- bkde(q, bandwidth=0.11, gridsize=1, range.x=c(-1,1)) > bkq $x [1] -1 $y [1] NA > bkq

I would like to get the fortran code for the functions locpol and dpill in the kernsmooth library , the purpose is to incorporate other selection criterion for the bandwidth in which a varying bandwidths are replaced instead of the constant bandwidth. I will send you back the improved version as soon as I get the fortran code for the upper mention functions. ((LOCPOLY and DPILL )))

Hello, I'm using R 1.40 on MacOS X X.1.2 (installed via the fink package manager). To upgrade my installed packages, I tried to use update.packages() today. All went well for most packages, with the exception of KernSmooth and cluster. In both cases, libraries were not found although I think they are present. Here's what happened: ---------------------------------- >

Dear R Users, When using locpoly function, number of output values is smaller than the number of input values. How is it possible to get number of output component $y equal to the number of inputs. Thanks a lot, Amir --------------------------------- [[alternative HTML version deleted]]

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

Thank you for your reply. I know the x in dpik() means the vector. But I don't know how to import into c() with a huge metadata (>1000). Following is my some try, and the h is: [1] 0.001180569, which seems to be feasible.

Hi there and thanks in advance. Nowadays I am working on the plug-in bandwidth selection with R. Firstly, my 1010 data is the return rate from Yahoo Finance. Secondly, my code is following: > r=read.table("/Users/user/Desktop/research/a.txt",sep=",",header=TRUE) > x<-r[8:1010,] > library(KernSmooth) >

Hello, I've been using the KernSmooth package recently and think I have found a problem with it: after loading the library I can issue bkde(c(27,26,27), bandwidth=dpik(c(27,26,27)), range.x=c(4.4, 113.6), gridsize=128, truncate=T) and bkde returns an error. If I change the gridsize to 129 the function works perfectly. I have tried this on my Linux box, and on a nearby Solaris machine, both

Apologies in advance if my comments don't help, in which case, no need to respond, but I noted in ?ksmooth: "bandwidth the bandwidth. The kernels are scaled so that their quartiles (viewed as probability densities) are at ? 0.25*bandwidth." So, could this be a source of the discrepancies you cited? Given that ?ksmooth explicitly says: "Note: This function was implemented for

Hi, I'm relatively new to R and R development, so please forgive me for any obvious errors. What I am trying to do is use the command dpik within the package KernSmooth to estimate bandwidth parameters for GPS telemetry data. I have been able to get this to work on a case by case basis without any problem, but would like to extend this so that I can batch process many different animals for

Dear Sir, Madam, or to whom this may concern, my name is Jan Failenschmid and I am a Ph.D. student at Tilburg University. For my project I have been looking into different types of kernel regression estimators and corresponding R functions. While comparing different functions I noticed that stats::ksmooth returned different estimates for the same bandwidth as other kernel regression estimators

Hi I hope this is going to the right place. I am trying to write a program which uses KernSmooth library to estimate mutual information between two time series at various different lags. At the moment it’s producing negative values, which is supposed to be impossible (something is fishy). I am summing across one row of the matrix to get p(value is in bin x) and summing across the columns to get

Hallo R-users, I would like to know if there is a way to get the effective degrees of freedom in local polinomial regression. At the moment, to carry out the local polinomial regression, I am using the function locpoly() in library KernSmooth (I need to estimate both the regression function and its derivatives, up to the 3rd one). Thanks, Simone -- Simone Vantini MOX - Modeling and