search for: ksmooth

Dear R Users, Before running ksmooth( ), a suitable bandwidth selection is needed. I use some functions for this task and receive these results for my data: width.SJ(y,nb=100,method="ste") : 40.25 bcv(y,nb=100) : 40.53 ucv(y) : 41.26 bandwidth.nrd(y) : 45.43 After implementing the function ksmooth(x,y, ban...

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 compatibil...

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 that should be equivalent (i.e. the local polynomial estimators KernSmooth::locpoly and locpol::locpol with degree 0). However, when optimizing the bandwidth of ksmooth separately using the same loss function,...

...thods' Robinson (1988) Speckman (1988) where I estimate a nonparametric function for each of the parametric variables in terms of the nonparametric one i.e. X[,i]=g(Z)+ u this is done because I need the $E( X[,i]\vert Z)$ for each position j in the vectors. the problem is that when I use the ksmooth() function in R it estimates the function at different points and not those that consist of the Z vector. the ksmooth() function in Splus on the other hand evaluates the points at the corresponding Z vector. both codes are given below d<-ksmooth(lprice,XX[,i],kernel="box") unique(lp...

I'd like to superimpose a ksmooth() onto a barplot(). My data is: > d 2009-06-20 2009-06-21 2009-06-22 2009-06-23 2009-06-24 2009-06-25 2009-06-26 2009-06-27 2009-06-28 2009-06-29 2009-06-30 2009-07-01 2009-07-02 Same Breed (B) 12.64 21.08 13.52 12.51 13.71 9.91 14.24...

Hi Every Body, I don't know why some regression functions have no related prediction function. For example lowess, loess, lokerns, lpridge, lpepa, and ksmooth. What could help? Is there any global or wrapper function so that can help? Regards, Amir Safari --------------------------------- [[alternative HTML version deleted]]

Dear R user, My input data, positive and negative, is complete without missing data. After running ksmooth( ) , I receive for $y , many NAs. What could be the reasons and how can I receive complete output? So many thanks for any help. Amir Safari --------------------------------- [[alternative HTML version deleted]]

Hi, Consider the Nottingham temperature and the Sunspot data: >data(nottem) >data(sunspot) Assume that we may model each data set by a nonparametic autoregressice model of the form Yt = m(Yt-1) +et, What are the value of x and y for using function ksmooth(x,y,"normal",bandwidth=0.01)? Thanks a lot! Owen

All: I am trying to use gam in a scatterplot smoothing problem. The data being smoothed have greater 1000 observation and have multiple "humps". I can smooth the data fine using a function something like: out <- ksmooth(x,y,"normal",bandwidth=0.25) plot(x,out$y,type="l") The problem is when I try to fit the same data using gam out <- predict.gam(gam(y~s(x)),se=TRUE) plot(x,out$fit,type="l") I only seem to get fits that would correspond to "big" bandwidths using ksmoot...

Hello, I want to do regression or missing value imputation by knn. I searched r-help mailing list. This question was asked in 2005. ksmooth and loess were recommended. But my case is different. I have many predictors (p>20) and I really want try knn with a given k. ksmooth and loess use band width to define neighborhood size. This contrasts to knn's variable band width via fixing a k. Are there any such functions I can use i...

Hi Does R have a function or has someone written a function to draw a 3d surface from a scatter plot of values using either ksmooth or locpoly. OR a transform function a that merges x relation z and y relation z to (x,y) relation z? I tried out scatterplot3d but it seems it would take a bit of work to get scatterplot3d to draw a curved surface. Lawrence

...locally before replacement. I also tried >anova (m1, Terms = 1-2 = 0) and I get: Invalid assginment: No object name : 1-2 = 0 What am I doing wrong? Thanks for your help!! >From: Jean Eid <jeaneid at chass.utoronto.ca> >To: r-help at stat.math.ethz.ch >Subject: Re: [R] ksmooth in SPLUS vs R Date: Mon, 22 Sep 2003 20:23:32 >-0400 > >Please do forget my questions as they are really trivial and I do not kno >what I was thinking of. > >Thank you thomas for clarifying my cloudy head today. > >jean, > > >On Mon, 22 Sep 2003, Jean Eid wrote: &...

...r/users/susrod/stats/R-0.63.1/src/library/modreg/src' g77 -O2 -fpic -c bsplvd.f -o bsplvd.o g77 -O2 -fpic -c bvalue.f -o bvalue.o g77 -O2 -fpic -c bvalus.f -o bvalus.o g77 -O2 -fpic -c dpbfa.f -o dpbfa.o g77 -O2 -fpic -c dpbsl.f -o dpbsl.o gcc -ieee_with_inexact -g -O2 -I../../../../include -c ksmooth.c -o ksmooth.o gcc -ieee_with_inexact -g -O2 -I../../../../include -c loessc.c -o loessc.o g77 -O2 -fpic -c loessf.f -o loessf.o g77 -O2 -fpic -c ppr.f -o ppr.o g77 -O2 -fpic -c qsbart.f -o qsbart.o g77 -O2 -fpic -c sbart.f -o sbart.o g77 -O2 -fpic -c sgram.f -o sgram.o g77 -O2 -fpic -c sinerp.f -...

Hello, I am trying to get fitted/estimated values using kernel regression and a triangular kernel. I have found packages that easily fit values from a kernel regression (e.g. ksmooth) but do not have a triangular distribution option, and density estimators that have triangular distribution options that I can't seem to use to produce estimated values (e.g. density). Any help is appreciated. Bryan [[alternative HTML version deleted]]

...are shown as below: ... make[4]: Entering directory `/home/ming/R-0.63.3/src/library/modreg/src' f77 -c bsplvd.f -o bsplvd.o f77 -c bvalue.f -o bvalue.o f77 -c bvalus.f -o bvalus.o f77 -c dpbfa.f -o dpbfa.o f77 -c dpbsl.f -o dpbsl.o cc -ieee_with_inexact -g -I../../../../include -c ksmooth.c -o ksmooth.o cc -ieee_with_inexact -g -I../../../../include -c loessc.c -o loessc.o f77 -c loessf.f -o loessf.o f77 -c ppr.f -o ppr.o f77 -c qsbart.f -o qsbart.o f77 -c sbart.f -o sbart.o fort: Info: sbart.f, line 164: No path to this statement 23012 continue ^ f77 -c sgram.f -o sgram....

...skal.test function (x, ...) UseMethod("kruskal.test") <environment: namespace:stats> > ls(3) [1] "acf" "acf2AR" "add.scope" .............. [181] "kruskal.test" "ks.test" "ksmooth" ....................... [475] "window<-" "write.ftable" "xtabs" > class(kruskal.test) [1] "function" > getS3method("kruskal.test","function") Error in getS3method("kruskal.test", "functio...

...of "Politecnico di Milano". I'm studying Local Regression Techniques such as loess, smoothing splines and kernel smoothers. Choosing "symmetric" for the argument "family" in loess function it is possible to produce a robust estimate , in function smooth.spline and ksmooth I didn't find this possibility. Well, is there a way to produce a robust estimate using smoothing splines or kernel smoothers? And if the answer is no, why? I'm asking these questions because I need to know loess' advantages and disadvantages compared to other techniques. Thank you very...

Hi all, I am looking for a command for doing 2D filtering (rectangular or Gaussian) in R... I have looked at ksmooth, filter and convolve but they seem to be 1D... Any thoughts? Thanks a lot! [[alternative HTML version deleted]]

In The American Statistician vol 42 (1988) pages 257 - 280, Yoav Benjamini investigates some variations on the box plot, including vaseplots, which maek the width of each box vary proportionally to he estimated density at a particular point. Has anyone implemented these in R ? Thanks as always Peter

I'm using nonparametric regression (packeges ksmooth and ks). My question: is there any way to compute confidence bands (or variability bands) with R. Confidence bands for functions are intervals [CLO(x);CUP(x)] such that with probability 1-alpha the true curve is covered by the band [CLO(x);CUP(x)]. Thanks very much for any help you can offer. Mi...