search for: tricube

Hi I am trying to get the tricube weights from the loess outputs as I need to calculate an error function which requires the weight. So I have used the following example from the R: cars.lo <- loess(dist ~ speed, cars, span=0.5, degree=1, family="symmetric") Then i try to get the weights: cars.lo$weights [1] 1 1 1...

The lowess() help page refers to documentation in "src/appl/lowess.doc". This was moved to "src/library/stats/src/lowess.doc" in 2007. This patch fixes it: Index: src/library/stats/man/lowess.Rd =================================================================== --- src/library/stats/man/lowess.Rd (revision 79137) +++ src/library/stats/man/lowess.Rd (working copy) @@

...he data set according to these deviations. This way the first row has the (x0, NA) value. 4. I drop the first row. 5. I divide all the deviations by the m-th one, where m is the number of points used in local fitting - m = floor(n*span) where n is the number of points. 6. I calculate the "tricube" weights and assign 0's to the negative ones. This eliminates all the points except the m points of interest. 7. I fit a linear weighted regression using lm. 8. I predict y(x0) from this linear model. This is basically the same procedure I use to predict at the x values from the data set...

Hi all, I 've got a question about the usage of loess in the modreg package. The documentation (loess.html) states that the smoothing window is either set by span or enp.target. If span is used, the details section of the docs state... <SNIP> DETAILS Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their

Hi all, I 've got a question about the usage of loess in the modreg package. The documentation (loess.html) states that the smoothing window is either set by span or enp.target. If span is used, the details section of the docs state... <SNIP> DETAILS Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their

Hi, I posted a message earlier entitled "How to fit a line through the "Mountain crest" ..." I figured loess is probably the best way, but it seems that the problem is the robustness of the fit. Below I paste an example to illustrate the problem: tmp=rnorm(2000) X.background = 5+tmp; Y.background = 5+ (10*tmp+rnorm(2000)) X.specific = 3.5+3*runif(1000);

...each element in w I want to find a good match (subscript number) of an element in x. x and w can be long. Instead of just finding the closest match I want to use weighted multinomial sampling (which I've already figured out once I have the probabilities) where the probabilities come from the tricube function of absolute differences between donor and target values, but normalized to sum to one, and using the maximum absolute difference as the scaling factor. This is similar to the loess weighting function with f=1. Here's code that works, to get the probability matrix to use for sampling:...

SAS proc rank has ties options of high and low that would allow producing ranks of the type found in the sports pages, e.g., rank (c(1,1,2,2,2,2,3)) == 1 1 3 3 3 3 7 Could R support these ties.methods?

I am trying to (semi) calculate the confidence intervals for a loess smoother (function: loess()), but have been thus far unsuccessful. The CI for the loess predicted values, yhat, are apparently yhat +- t*s * sqrt(w^2), where s is the residual sum of squares and w is the weight function Correct me of I'm wrong, but R uses the tricubic function (1-abs(z)^3)^3, where z = (x-xi)/h, where h

...each element in w I want to find a good match (subscript number) of an element in x. x and w can be long. Instead of just finding the closest match I want to use weighted multinomial sampling (which I've already figured out once I have the probabilities) where the probabilities come from the tricube function of absolute differences between donor and target values, but normalized to sum to one, and using the maximum absolute difference as the scaling factor. This is similar to the loess weighting function with f=1. Here's code that works, to get the probability matrix to use for sampling:...