similar to: bivariate non-parametric smoothing

Hi, there, I have 5 datasets. I would like to choose a basis spline with same knots in GAM function in order to obtain same basis function for 5 datasets. Moreover, the basis spline is used to for an interaction of two covarites. I used "cr" in one covariate, but it can only smooth w.r.t 1 covariate. Can anyone give me some suggestion about how to choose a proper smoothing spline

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

Hi everyone, I am fitting a bivariate smoothing model by using gam. But I got an error message like this: "Error in eigen(hess1, symmetric = TRUE) : 0 x 0 matrix" If anyone know how to figure it out, pleaselet me know. Thanks very much. [[alternative HTML version deleted]]

Hello: I sent this question the other day with the wrong subject heading and couple typos, with no response. So, here I go again, having made those corrections. I would like to estimate, for lack of a better description, a partially additive non-parametric model with the following structure: z~ f(x,y):w1 + g(x,y):w2 + e In other words, I'd like to estimate the marginals with respect to

I just heard a talk about a semi-parametric model. I was quite excited by the idea. This model is fitted y= xB + g(z) + e where x is a data matrix, B a column vector, z is another data matrix, and g is a smooth model fitted by a Kernel Smoothing regression (I got the idea any smoother would do as well). The speaker said that when z is considered as a "control" variable, and there is

Hi there, How may I smooth spline two vectors with the smoothing parameter selected by generalized maximum likelihood (GML) .? Thanks a lot. -- View this message in context: http://r.789695.n4.nabble.com/Smoothing-spline-with-smoothing-parameters-selected-by-generalized-maximum-likelihood-tp4210679p4210679.html Sent from the R help mailing list archive at Nabble.com.

Dear list, I do apologize if these are basic questions. I am fitting some GAM models using the mgcv package and following the model selection criteria proposed by Wood and Augustin (2002, Ecol. Model. 157, p. 157-177). One criterion to decide if a term should be dropped from a model is if the estimated degrees of freedom (EDF) for the term are close to their lower limit. What would be the

Hello everone,            Anyone who knows how to force a cubic smoothing spline to pass through a particular point?            I found on website  someone said that we can use "cobs package" to force the spline pass through certain points or impose shape           constraints (increasing, decreasing). However,  this package is using  B-spline and can only do linear and quadratic

Hi, I am using the KernSmooth package to estimate nonparametrically bivariate density functions. However, it seems that the bandwidths (one for each co-ordinate direction) have to be selected manually. This does not apply for the univariate case, for which dpik (included in KernSmooth) uses up-to-date plug-in rules. Does anyone know about a package, or function, which estimates bandwidths

Dear All, I would like to extract the smoothing matrix of the fitted GAM, \hat{y} = Sy. I can't seem to find the function or am I missing something? Thanks, any help is greatly appreciated Man Zhang [[alternative HTML version deleted]]

Hi all, I'm using the ssanova function from the gss package to fit smoothing spline anovas, and am running into some difficulty. For my data, I have measurements at 2 milisecond intervals for every observation. Every observation does not have the same duration, so I have scaled the times for each observation to a scale between 0 and 1. I would like to smooth over time, and the following

Hello - I am currently simulating bivariate AR(1) time series data and have the following line in my code: Px=spec.pgram(ts.union(X,XX),spans=c(?,?)) The spans option is where I enter in the vector containing the Daniell smoother numbers, but I don't know what a Daniell smoother is (hence the question marks). Can somebody please tell me? Is there another option where I can simply enter in

Is there code for bivariate kernel density estimation? For bivariate kernels there is kde2d in MASS kde2d.g in GRASS KernSur in GenKern (list probably incomplete) but none of them seems to accept a weight parameter (like density does since R 2.2.0) -- Erich Neuwirth, University of Vienna Faculty of Computer Science Computer Supported Didactics Working Group Visit our SunSITE at

Dear list I have a vector of values that allegedly have a bivariate normal distribution. I want to create a plot that shows the values I have obtained, and the bivariate normal distribution curve for the data. Is there a way of doing this in R? Many thanks for your help, Sarah. --------------------------------- [[alternative HTML version deleted]]

Dear all, I have a bivariate dataset from a preliminary study. I want to do two things: (1) estimate the probability density of this bivariate distribution using some nonparametric method (kernel, spline etc); (2) sample a big dataset from this bivariate distribution for a simulation study. Is there any good method or package I can use in R for my work? I don?t want parametric models like

Dear all, I fitted a non-parametric model using GAM function in R. i.e., gam(y~s(x1)+s(x2)) #where s() is the smooth function Then I obtained the coefficients(a and b) for the non-parametric terms. i.e., y=a*s(x1)+b*s(x2) Now if I want to use this estimated model to do optimization or sensitivity analysis, I am not sure how to incorporate the smooth function since s() may not

Hi R-users, I'd like to smooth a matrix to dismiss spikes and to interpolate in plane example of a matrix: Map[1:3,1:3] [,1] [,2] [,3]... [1,] 34.4 34.2 35.1 [2,] 33.4 34.2 35.4 [3,] 34.1 33.2 32.1 .... dim(Map)[1] =/= dim(Map)[2] What functions can I use? Thanks a lot for any response, M [[alternative HTML version deleted]]

Hi there, Does R has built-in codes for nonpara. bivariate regression so that I can estimate the joint distribution of two variables as a function of some covariates? Thanks a lot. --------------------------------------------------- Ximing Wu Department of Economics University of Guelph Guelph, Ontario, Canada, N1G 2W1 Tel: (519) 842-4120, ext 53014 Fax: (519) 763-8497 email: xiwu at

Dear all, The other day I was reading this post [1] that slightly surprised me: "To reject the null of no correlation, an hypothsis test based on the normal distribution. If normality is not the base assumption your working from then p-values, significance tests and conf. intervals dont mean much (the value of the coefficient is not reliable) " (BOB SAMOHYL). To me this implied that in

I have estimated a multiple nonparametric regression using the loess command in R. I have also estimated an additive version of the model using the gam function. Is there a way of using the output of these two models to test the restrictions imposed by the additive model?