similar to: multivariate bandwidth: regband

Hi, I want to calculate the bandwith of a multivariate local polynomial regression such as the model is: Y=m(X,Z)+u. Please, somebody knows how I can do it in a multivariate case? In an univariate case I use the instrucction "regband" of the locfit package, but it is impossible to find a way to do it in the bivariate case. Please, help me. Thanks you in advance Alexandra

Dear memberships, I'm trying to estimate the following multivariate local regression model using the "locfit" package: BMI=m1(RCC)+m2(WCC) where (m1) and (m2) are unknown smooth functions. My problem is that once I get the regression done I cannot get the fitted values of each of this smooth functions (m1) and (m2). What I write is the following library(locfit) data(ais)

Hello, I want to create a matrix of N random numbers with a uniform distributions. Later, I want to repeat T times each row of this matrix. For this I do the following loop: N<-45 T<-10 n<-N*T a<-matrix(runif(N,min=-1,max=1),nr=N) mymat<-matrix(rep(NA,n),nr=n,nc=1) for(i in i:N){ b<-rep(a[i,],T) mymat[i,]<-b } Mi problem is that with this loop I can see the output of the

Dear memberships, I'm trying to estimate the following multivariate local regression model using the "locfit" package: BMI=m1(RCC)+m2(WCC) where (m1) and (m2) are unknown smooth functions. My problem is that once I get the regression done I cannot get the fitted values of each of this smooth functions (m1) and (m2). My program is the following: library(locfit)

Hello, I want to create two random variables (x1,x2) both with uniform distribution bounded by (-1) and (1) that has a correlation of 0.6 between them. Does somebody know how I can do it? For normal random variables I known how to implement it with the rmvnorm command but I don't know how to do it with variables uniformly distributed. Thanks a lot. Alexandra [[alternative HTML

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

Hello all! I have recently started using the LOCFIT package, together with Clive Loader's book. I need to implement some method for automatic (plug-in) bandwidth selection in a multivariate kernel regression. From the book, and the LOCFIT documentation, it is not clear whether this is possible. As far as I can see, the only time the various automated procedures, e.g., SJPI, are used is in

I've been trying to run locfit on data with 6 inputs and 1 output in R. Whenever I make a prediction for the same exact data that the model was built on though, I get significant discrepancies between the fitted outputs of the prediction and the actual data. I have scaled the inputs, tweaked the alpha parameter, and played around with a lot of the other variables as well. Is their some kind

You can try `locfit', though it does local likelihood, rather than garden-variety kernel density estimation. Here's an example: library(locfit) data(cldem) den.fit <- locfit(~ x1 + x2, data=cltrain) predict(den.fit, newdata=cltrain) Andy From: Strickland, Matthew > > Hi, > > My data consists of a set of point locations (x,y). > > I would like to know if there

I have a dataset of pregnancy values for multiple years (and ages, not included) with missing years. I would like to use local polynomial regression to smooth the values and estimate for the missing years. I would also like to use GCV to justify the smoothing parameter selection. When using locfit() with lp() I found that the gcvplot function does not work as it is looking for an alpha value to

Hi list, This was asked a couple of years ago but I can't find a resolution. Is there any way to get the coefficients from one of the local polynomial fits in locfit. I realize that locfit only constructs polynomials at a handful of intelligently selected points and uses interpolation to predict any other points. I would like to know the terms of the polynomials at these points. It seems

As I think it is not spam but helpful, let me repeat my stats.stackexchange.com question here, from http://stats.stackexchange.com/questions/16346/difference-between-lp-or-simply-in-rs-locfit I am not sure I see the difference between different examples for local logistic regression in the documentation of the gold standard locfit package for R:

Dear R-users, One of the main reasons I moved from GAUSS to R (as an econometrician) was because of the existence of the library LOCFIT for local polynomial regression. While doing some checking between my former `GAUSS code' and my new `R code', I came to realize LOCFIT is not quite doing what I want. I wrote the following example script:

Dear R-devel, I'm working on Prof. Loader's new version of locfit to try to get it pass R CMD check. I'm almost there, but I have a problem with some Rd files that I hope some one can help me resolve. Here's an example: In the package there's a function called locfit.censor(). This function can be used in a few different ways: locfit.censor(x, y, cens, ...)

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

I was wondering if someone could help me with a problem getting locfit running. I am running Digital Unix 3.2D on an alphastation, with R 0.61.1 I downloaded the CRAN distribution of locfit. I had no problems installing it with R INSTALL (I installed it into my library rather than the default base library; the output is attached at the end of this email message). I then tried to test it with the

The version of locfit on the web site mentioned apparently has been revised by Prof. Loader, and is newer than the CRAN version that I have been maintaining. If Prof. Loader is OK with it, I will take a look and see if I can get the new version into CRAN-conforming form and upload to CRAN. Meanwhile, make sure you're using the package from Prof. Loader's web page, instead of the one on

Hello!! At first thank you all for your suggestions and your help. But I would like to get another group of datas from the density estimation. I don't know if these information are provided by locfit or I should use an other function like plot(). Description: Each number between 1 and 100 (only integer) for example should have only one density value. But I also would like to have the density

Apologies for the coming to this late... 1. By now I hope Somkiat has realized that R-bugs is not the place to report problems in contributed packages. Please direct such reports to the package maintainer. 2. This is really user error. predict() expect the newdata to be a data frame containing variables with the same names as those used in the fitting process. E.g., you fitted the model with

Hello, Here is a problem I have had trying to install locfit, the package for R. I have already contacted Clive Loader who thinks the problem has more to do with R than locfit, so here is the point : The version of R I am currently running is R-base-0.90 that I installed using the R-base-0.90.1-1.i386.rpm package. I dowloaded the locfit package available at the CRAN site and ran : R INSTALL