similar to: NA as the output of ksmooth

Dear R Users, Before running ksmooth( ), a suitable bandwidth selection is needed. I use some functions for this task and receive these results for my data: width.SJ(y,nb=100,method="ste") : 40.25 bcv(y,nb=100) : 40.53 ucv(y) : 41.26 bandwidth.nrd(y) : 45.43 After implementing the function ksmooth(x,y, bandwidth= each of abovementioned bandwidths), I have some NAs

Hi Every Body, I don't know why some regression functions have no related prediction function. For example lowess, loess, lokerns, lpridge, lpepa, and ksmooth. What could help? Is there any global or wrapper function so that can help? Regards, Amir Safari --------------------------------- [[alternative HTML version deleted]]

Dear Sir, Madam, or to whom this may concern, my name is Jan Failenschmid and I am a Ph.D. student at Tilburg University. For my project I have been looking into different types of kernel regression estimators and corresponding R functions. While comparing different functions I noticed that stats::ksmooth returned different estimates for the same bandwidth as other kernel regression estimators

Apologies in advance if my comments don't help, in which case, no need to respond, but I noted in ?ksmooth: "bandwidth the bandwidth. The kernels are scaled so that their quartiles (viewed as probability densities) are at ? 0.25*bandwidth." So, could this be a source of the discrepancies you cited? Given that ?ksmooth explicitly says: "Note: This function was implemented for

Dear R Users, For running wavelet functions using dwt( ), modwt( ), and mra( ), a wavelet filter algorithm is applied. For all these functions, default is "la8" and other possibility is "haar". In related documents, another possibilities like as symlet and coiflet ... are not cited. Besides "la8" and "haar", which wavelet filters can be used? Thank

Dear R Users, Suppose we are interested for generating a new vector ( x ) from a current vector (y) of length 1000 so that x includes the sum of every 5 values in y respectively from the first to the end of length y. The same length of y for x is desired, so that other 4 positions (indices) in x are filled out with NA. For generating such a new vector, I have no idea. I tried in some

Hello all, I would like to use the Nadaraya-Watson estimator assuming a Gaussian kernel: So far I sued the library(sm) library(sm) x<-runif(5000) y<-rnorm(5000) plot(x,y,col='black') h1<-h.select(x,y,method='aicc') lines(ksmooth(x,y,bandwidth=h1)) which works fine. What if my data were clustered requiring a bandwidth that varies with x? How can I do that? Thanks in

Dear R Users, Suppose we want to creat a new vector ( x ) from a current vector (y) of length 1000. The current vector y includes negative, zero and positive values. We want our new vector x includes the negative values in y, otherwise NA with the same length as y. For this, we have x=y[y<0] . Now x includes a subset of y with shorter length than y. With x=match(y,x) we would

I'd like to superimpose a ksmooth() onto a barplot(). My data is: > d 2009-06-20 2009-06-21 2009-06-22 2009-06-23 2009-06-24 2009-06-25 2009-06-26 2009-06-27 2009-06-28 2009-06-29 2009-06-30 2009-07-01 2009-07-02 Same Breed (B) 12.64 21.08 13.52 12.51 13.71 9.91 14.24 7.18 11.81 5.92 12.04 17.96

Hi R Users, I don't know how much is difficult my problem and even it is possible to solve in R or not. Given a vector with 2000 observations. I want to creat a new vector from that vector so that new vector be the sum of every 5 observations sequently. That is , each new observation is sum of every 5 sequent observations, from the first observation of previous vector to the end. So

Dear R Users, My question is overall and not necessarily related to R. Suppose we face to a situation in which MSE( Mean Squared Error) shows desired results but Bias shows undesired ones, Or in advers. How can we evaluate the results. And suppose, Both MSE and Bias are important for us. The ecact question is that, whether there is any combined measure of two above metrics. Thank you so

I am working with a model that I have to estimate a nonparametric function. The model is partial linear i.e. Y=X$\beta$ + f(z) + $\epsilon$ I am using the ' double residual methods' Robinson (1988) Speckman (1988) where I estimate a nonparametric function for each of the parametric variables in terms of the nonparametric one i.e. X[,i]=g(Z)+ u this is done because I need the $E(

Dear R Friends , I want to name my variables( more than 2 variables in a single plot) within a plot to distinct them from each other, but I cann't. How it is possible? I don't mean x and y axis using xlab or ylab. At the below , it follows some lines, only as an example that you could try please, if it is possible. I really thanks for your attention. Amir library(graphics) y<-

Dear R Users, Could somebody please compare the packages of unit root test ( Uroot, Ucra, tseries and fseries ) regarding the type of test ( without constant and trend, with constant , and with constant and trend ) ? Regards, Amir Safari --------------------------------- Rekindle the Rivalries. Sign up for Fantasy Football [[alternative HTML version deleted]]

Dear All , Since dissimilarity is one of quality measures in clustering , I'm trying to access to the sum of dissimilarity as a whole measure. But after running my data using CLARA I obtain : 1128 dissimilarities, summarized : Min. 1st Qu. Median Mean 3rd Qu. Max. 0.033155 0.934630 2.257000 2.941600 4.876600 8.943700 But I can not find the sum of dissimilarity.How can i

Hi Dear All, I want to instal some packages, but I can not find them in both lists of Package Index in internet ( http://www.maths.lth.se/help/R/.R/doc/html/packages.html ) and of Instalation of Packages window in the softwar. For example "modreg". Are they included in larger packages ? Thanks for your help. Best Regards, Amir Safari __________________________________________________

Hi Dear R users, For a pair plotting, usaully we use par( ) function. Apparently it does not work anywhere. I want to have 3 plots in a single figure, like this: par(mfrow=c(3,1)) densityplot( a) densityplot(b) densityplot(c) But it does not work. How is it possible to have such a figure with densityplot( ) in a single figure? So many thanks for any help. Amir Safari

Hi dear R users, Is it basically correct that a problem is ( linearly on nonlinearly ) modeled so that the slack variable is bounded by an upper bound ? If so, how it can be handled and coded practically ? for example: x1+ x2 =< b so ----> x1 + x2 + s=b s=b- x1 - x2 b- x1 - x2 =< upper value But algorithms can not calculate b- x1 - x2 , because

I realized that I may not have answered the question you were asking and that no one else has responded. I can across a similar problem and may have an answer to your question now. If you have both the wavelet coefficients and the scaling coefficients then create a fake sequence of the same length as the original and decompose that sequence using wd form wavethersh with the same wavelet family

Dear R users, Sometimes it is needed to read only some columns from a table, in particulare for high frequency data. How it is possible to read just some certain columns using read.table ( ). The reason could be keeping space in R and in particular accelerating in reading data when the number of rows are huge and some of them are not needed. Thank you very much, Amir