similar to: How does locpoly (KernSmooth package) estimate densities?

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 =

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: ---------------------------------- >

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

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 )))

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

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

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

1. Please do make use of the subject line. 2. Please (re-)read the description of the `range.x' argument in ?locpoly: it's suppose to be a vector of min and max x values. 3. I hope you realize how grossly inefficient this computation is... Andy > From: klea lambrou > > > hello R-users.could you please help me on this one?i have > 2 vectors x1 > and y1 and

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]]

I just upgraded to R 2.7.2 (from 2.7.1) this morning (Ubuntu amd64 platform). Shortly afterwards, I ran into a problem loading the odesolve library, it could not find libRblas.so. I was able to fix this by rebuilding odesolve. Now, KernSmooth gives me the same problem... R> library(KernSmooth) Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared library

Hi all, I'm not sure who to ask this, so I'm posting this here. I just ran: require(KernSmooth) And got (I bolded the text): Loading required package: KernSmooth KernSmooth 2.23 loaded *Copyright M. P. Wand 1997-2009* Warning message: package 'KernSmooth' was built under R version 2.12.1 What does that mean? Thanks, Tal ----------------Contact

Any advice when to use denstity() and when the KernSmooth package bkde() to smooth a histogram? No specific problem to use either one, but I'm curious why there are two so similar implementations. Thanks! mario -- Ing. Mario Valle Data Analysis and Visualization Group | http://www.cscs.ch/~mvalle Swiss National Supercomputing Centre (CSCS) | Tel: +41 (91) 610.82.60 v.

Hi, I have a vector or random variables and I'm estimating the density using "bkde" function in the KernSmooth package. The out put contains two vectors (x and y), and the R documentation calls y as the density estimates, but my y-values are not exact density etstimates (since these are numbers larger than 1)! what is y here? Is it possible to get the true estimated density at each

According to the CRAN contributed packages page, the license for "KernSmooth" is *not* GPL. It is listed as "License: Unlimited distribution (from Dr Wand)." Are there any restrictions on this of any kind? Is there some written license document similar to the GPL license? The reason I'm asking is that the legal beagles in the company I work for are extremely paranoid about

Hi, I have two separate questions relating to the KernSmooth package. I am using the dpik function from the KernSmooth package and receive the error Warning message: In kappam * Gcounts : longer object length is not a multiple of shorter object length I saw an earlier post , but the issue was using the bkde fxn and the person appeared to be using too small of a bandwidth.

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 =

Hi Daniel, Thanks for replying; I was hoping to get in touch with you on this issue. I had a look at how SelectionIDAG does it when generating the matcher table, and it does consider the implicit defs as additional output. Here is the match table generated for the pattern: /* 0*/ OPC_CheckOpcode, TARGET_VAL(ISD::SIGN_EXTEND), /* 3*/ OPC_MoveChild0, /* 4*/ OPC_CheckOpcode,

Hi Dominik, Thanks for your reply. In my case, the Defs is the cause of the problem. Or rather, it is part of the problem, because when I remove it from the instruction TableGen gives me a different error message which concerns a part which is deeper into the pattern tree, so at least it is able to proceed beyond that part of the pattern. I have also stepped TableGen inside gdb and