search for: epanechnikov

...(Epanech.)--0.00452; Plug-in (normal)--0.0009; Plug-in(Epanech.)--0.002. this is the condition: N=1010 data sample is from normal distribution N(0,0.0077^2). The grid points are taken to be [-0.05,0.05] and increment is 10. Bandwidth is taken the above h value r respectively and the kernel can be Epanechnikov kernel or Gaussian kernel. The following is my code: ######################################################### # Define the Epanechnikov kernel function kernel<-function(x){0.75*(1-x^2)*(abs(x)<=1)} ############################################################### # Define the kernel density...

...tor. bw_cx[i,] is a 1x (2+3) vector representing the bandwidths h for each variable, which were calculated using the npcdensbw function. (no this is not a mistake, I deliberately use the conditional one) For this I use the below function in R. kerz <- npudens(bws=(bw_cx[i,]),cykertype="epanechnikov",cxkertype="epanechnikov",oxkertype="liracine",tdat=tdata,edat=dat) In version 2.15.2, this worked fine, as I was able to retrieve the necessary density estimates with kerz$dens. This version was installed on a very old (read very slow and about to crash) computer. I rec...

...(-1.2,1.2,len=501) matplot(x, cbind(Kcos(x),Kcosine(x)), type='l', lty=1) The big difference : - R's version is smooth (differentiable at the border of support) - Scott's (not really "his", of course!) version is not differentiable but looks much closer to the Epanechnikov kernel and is hence almost as `good' (less than half a percent of MSE loss w.r.t Epanechnikov). Problem: - An average user knowing some statistics literature will most probably assume that a "cosine" kernel means the one in the literature, *NOT* the one we have in R now...

...Cancer) library(kknn) BCancer = na.omit(BreastCancer) d = dim(BCancer)[1] i1 = seq(1, d, 2) i2 = seq(2, d, 2) t1 = BCancer[i1, ] t2 = BCancer[i2, ] y2 = BCancer[i2, 11] x = 10 k = array(1:x, dim = c(x,1)) ker = array(c( "rectangular", "triangular", "epanechnikov", "biweight", "triweight", "cos", "inv", "gaussian"), dim = c(8,1)) f = function(x, ker){ BreastCancer.kknn <- kknn(Class~., train = t1, test = t2, k = x, kernel = ker, distance = 1) fit = fitted(B...

Hi, i would like to do a local linear regression but with a user-defined function f(x,y) as kernel. (not the typical Gaussian or Epanechnikov kernel function but some similar function). More, would it be possible to do a local linear regression by adding weights to the (x,y) data points? Many thanks in advance. -- View this message in context: http://www.nabble.com/User-defined-Kernels-tp15335660p15335660.html Sent from the R help maili...

Hello, my name is Carsten. This ist my first post to R-help mailing list. I estimate densities with the function "density" out of the package "stats". A simplified example: #generation of test data n=10 z = rnorm(n) #density estimation f=density(z,kernel="epanechnikov",n=n) #evaluation print(f$y[5]) Here I can only evaluate the estimation at given points. These points are determined by the parameter n. By default they are equidistant distributed on the interesting interval. But I need to evaluate the estimation (the estimated densitiy function) at manu...

...available in sp. - The function buffer.line() allows to compute buffer around a line - The function distfacmap() converts one factor map into a set of maps giving the distance to the patches belonging to the different levels. - The kernel home-range estimation can now be fitted using an Epanechnikov kernel (though it is still optional), resulting in a quicker estimation. - The former function enfa() has been modified. It now requires a data frame (available units) and a vector (use of each unit). If the data consist of a point pattern and of an object kasc, the function data2enfa is...

...available in sp. - The function buffer.line() allows to compute buffer around a line - The function distfacmap() converts one factor map into a set of maps giving the distance to the patches belonging to the different levels. - The kernel home-range estimation can now be fitted using an Epanechnikov kernel (though it is still optional), resulting in a quicker estimation. - The former function enfa() has been modified. It now requires a data frame (available units) and a vector (use of each unit). If the data consist of a point pattern and of an object kasc, the function data2enfa is...

Epanechnikov kernel works if option canonical=TRUE, however it would be good to know why it does not for for canonical=FALSE (default). Sorry for craetaing maybe useless thread. Best regards, Ondra. [[alternative HTML version deleted]]

Hi, R users   I need to use the function (locpoly) to fit a local poynomial regression model, The defult for kernal function is " normal" , but  I need to use different kernal functions such as :Uniform,Triangular,Epanechnikov,...... Could someone help me define these functions to fit local polynomial regression model?. Email:assaedi76@yahoo.com     Thanks alot [[alternative HTML version deleted]]

...nd Jones' recommendations in Ch. 5 of Event History Modelling. I get the following summary(haz_1) Number of Observations .......... 50 Censored Observations ........... 43 Method used ..................... Local Boundary Correction Type ........ Left and Right Kernel type ..................... Epanechnikov Minimum Time .................... 1 Maximum Time .................... 3 Number of minimization points ... 51 Number of estimation points ..... 101 Pilot Bandwidth ................. 0.25 Smoothing Bandwidth ............. 1.27 Minimum IMSE .................... 6716.9 Can anyone provide any advice?...

...g: library(np) N<-100 X<-matrix(c(rnorm(N,1,1), rnorm(N,0,1)), ncol=2) BETA <-matrix(1,2,1) Z<-X%*%BETA L<-rlogis(N,location=0, scale=1) Y <-as.vector(X%*%BETA+L>=0)*1 KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", ckertype="epanechnikov" ) KSi <- npindex(KS, errors=TRUE) se(KSi) But then I get as a result a vector Nx1, which I do not understand what it is, and if I let errors=FALSE then I get a NA as a result. So, how can I get the standard error of the estimated coefficient? Thank you Dimitris -- View this message...

Dear all, I am using np package in order to estimate a model with Klein and Spady estimator. To estimate the model I use KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2) and to estimate beta hats standard errors I use KSi <- npindex(KS, gradients=T, boot.num=300) vcov(KSi) This is fine so far, but if I want to estimate the bootstrapped standard errors on estimates by se(KSi) then the result is NA and if I include the argument errors=TRUE i...

...0 q<-2 BETA<-matrix(1,3,1) X<-matrix(c(rnorm(N,0,1), rnorm(N,1,1)), ncol=q) X<-cbind(X,1) L<-rlogis(N,location=0, scale=1) Y <-as.vector(X%*%BETA+L>=0)*1 KS <- npindexbw(xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2) Thank you Dimitris -- View this message in context: http://r.789695.n4.nabble.com/error-in-optimization-when-I-include-constant-term-in-Klein-and-Spady-np-package-tp3692728p3692728.html Sent from the R help mailing list archive at Nabble.com.

...to evaluate on data without bandwidths. * npsigtest interface brought in line with other functions. * significance tests can now be performed on npreg outputs. * added a vignette and faq. * summary on npconmode now properly retrieves names from bandwidth objects. * fixed the 6th and 8th order epanechnikov kernels. * fixed some quietness issues. * npplot now returns data upon request for conditional densities. * npreg and npcdens now take the appropriate limits in some pathological cases. We are grateful to John Fox, Achim Zeilies, Roger Koenker, and numerous users for their valuable feedback whi...

...to evaluate on data without bandwidths. * npsigtest interface brought in line with other functions. * significance tests can now be performed on npreg outputs. * added a vignette and faq. * summary on npconmode now properly retrieves names from bandwidth objects. * fixed the 6th and 8th order epanechnikov kernels. * fixed some quietness issues. * npplot now returns data upon request for conditional densities. * npreg and npcdens now take the appropriate limits in some pathological cases. We are grateful to John Fox, Achim Zeilies, Roger Koenker, and numerous users for their valuable feedback whi...

...o.com> Content-Type: text/plain Hi, R users ???br> I need to use the function (locpoly) to fit a local poynomial regression model, The defult for kernal function is " normal" , but???I need to use different kernal functions such as :Uniform, Triangular, Epanechnikov, ...... Could someone help me define these functions to fit local polynomial regression model?. Email:[66]assaedi76 at yahoo.com ???br> ???br> Thanks alot [[alternative HTML version deleted]] ------------------------------ Message: 7 Date: Sat, 24 Jul 2...