similar to: kernel smoothing of weighted data

Dear R-users, I intend to do a spatial analysis on the genetic structuring within a population. For this I had thought to prepare a kernel density estimate map showing the spatial distribution of individuals, while incorporating the genetic distances among individuals. I have a dataset of locations of N unique individuals (XY-coordinates) and an NxN matrix with the genetic distances given as a

Hi, Does anyone know how to use weights and generate error bounds for Savitzky-Golay? I have a (smallish) set of points y equally spaced each with a known error and would like to smooth them using S-G but so as to take into account the error already have and construct new error bounds around them that take into account the errors they had at the beginning and the erros they get as a result

Hi, I was wondering if there is a function in R which performs bivariate non parametric smoothing which allows for the possibility of including some weights in the smoothing (for each data points in my grid I have some predefined weights that I would like to include in the smoothing). Thanks, Ben _________________________________________________________________ [[alternative HTML

Dear list members, I am trying to understand this output from the smoothing package locfit (1.5-4, running on R 2.8.1 on Windows Vista 64 bit). # sample code x<-1:100 y<-rnorm(100) fit<-locfit(y~x,family="gaussian") #default parameters are fine plot(fit,band="global") #plot seems "reasonable", confidence bands use a global estimate of variance

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 <-

Dear UseRs: Recently, I have read an article regarding the association between age and lymph node metastases. http://jco.ascopubs.org/content/27/18/2931.long In statistical analysis, the authors stated "Because a nonlinear relationship between age and lymph node involvement was expected based on existing literature, lymph node involvement was also regressed on age using nonparametric

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

Hi, just a quick question: Should weighted.mean be able to cope with the specific case where one weight is Inf? I came across this when trying to implement a simple weighted moving average algorithm for surface smoothing: these algorithms often result in a single infinite weight when predicting the surface value at known data points. e.g. > weighted.mean(c(77,88,99), c(Inf, 1, 2))

Dear All, I've been?searching for?appropriate codes to compute the rate of change and the curvature?of ?nonparametric regression model whish was denoted by a smooth function?but?unfortunately?don't manage to?do?it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators. The following are the example of fitting a

Hello: I have been using sm.binomial() in the Bowman and Azzalini's sm package to smooth and plot estimated probabilities as a function of a covariate. I am concerned about my choice of bandwidth, and I was hoping there was another method available in some other package, perhaps with an automatic choice of smoothing parameter. Does anyone know of one? Thanks in advance. Tom Richards

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

I'm trying to plot a series of densities using/comparing differing weights. I see the reference to weights and subscripts, but I don't understand how to implement that. My data are of the form: I, J, Actual, Distance, Subset, Weight1, Weight2, ... I'm trying to see the effect of the distance distribution (Actual by Distance) compared to the various weighted distributions

In the HistData package, I have a data frame, PearsonLee, containing observations on heights of parent and child, in weighted form: library(HistData) > str(PearsonLee) 'data.frame': 746 obs. of 6 variables: $ child : num 59.5 59.5 59.5 60.5 60.5 61.5 61.5 61.5 61.5 61.5 ... $ parent : num 62.5 63.5 64.5 62.5 66.5 59.5 60.5 62.5 63.5 64.5 ... $ frequency: num 0.5 0.5

Hi all, I'm looking for to interpolate hourly temperature date collected from more than 140 automatic weather station (irregularly spaced) using 4 independent variable: 1-2) geografic coordinates (x,y) (from DEM - 40m) 3) altitude (z) (from DEM - 40m) 4) solar radiation (from a model calculated with grass: r.sun) In addition, I would like to use also "time" variable (e.g.: hours).

R List, I am seeking help in the area of kernel smoothing. I am attempting to use LSCV values to conduct 2d and weighted 2d kernel smoothing (imported from an ArcMap shapefile), and have been unable to find tools to conduct such analysis in R. My biggest problem is determining the LSCV value and finding a tool that does weighted smoothing, as there are obviously many different packages that will

DeaR list, I'm trying to represent some information via 3D plots. My data and session info are at the end of this message. So far, I have tried scatterplot3d (scatterplot3d), persp3d (rgl), persp (graphics) and scatter3d (Rmcdr) but any of them gave me what I'd like to have as final result (please see [1] for a similar 3D plot changing PF by ypred, pdn by h4 and pup by h11). In general

Dear All, I'm sure this is not the first time this question comes up but I couldn't find the keywords that would point me out to it - so apologies if this is a re-post. Basically I've got thousands of points, each depending on three variables: x, y, and z. if I do a plot(x,y, col=z), I get something very messy. So I would like to smooth the values of z according to the values of

Hi ilai Thank you for your suggestions. I do not know what happened yesterday I must have omitted a few changes out in going from R to email and apologies for the double posting - I had troubles sending it as my ISP gave a message of not being connected for email but was for the web I was trying to get panel.Locfit to work in a number of situations. 1. Conditioned by Farm (3 panels) with 2

Hello, In reading the loess description I see: span: the parameter alpha which controls the degree of smoothing. The default seems to be 0.75. Would it be possible to expand on this decription so I can avoid trail and error? Can I increase this pass 'span' > 1? Qualitatively to what degree changing this value affects the smoothing of the data? Thank you. Kevin

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)