similar to: bkde() breaks

Hi, I have two separate questions relating to the KernSmooth package. I am using the dpik function from the KernSmooth package and receive the error Warning message: In kappam * Gcounts : longer object length is not a multiple of shorter object length I saw an earlier post , but the issue was using the bkde fxn and the person appeared to be using too small of a bandwidth.

Hi, I want to fit a kernel density estimator by bkde of library KernSmooth. I need only the density value at the point 0. I do not understand the following behaviour: > q <- rnorm(100) > bkq <- bkde(q, bandwidth=0.11, gridsize=1, range.x=c(0,0)) Error in 0:L : NA/NaN argument > bkq <- bkde(q, bandwidth=0.11, gridsize=1, range.x=c(-1,1)) > bkq $x [1] -1 $y [1] NA > bkq

Hi, I'm relatively new to R and R development, so please forgive me for any obvious errors. What I am trying to do is use the command dpik within the package KernSmooth to estimate bandwidth parameters for GPS telemetry data. I have been able to get this to work on a case by case basis without any problem, but would like to extend this so that I can batch process many different animals for

Thank you for your reply. I know the x in dpik() means the vector. But I don't know how to import into c() with a huge metadata (>1000). Following is my some try, and the h is: [1] 0.001180569, which seems to be feasible.

Hi there and thanks in advance. Nowadays I am working on the plug-in bandwidth selection with R. Firstly, my 1010 data is the return rate from Yahoo Finance. Secondly, my code is following: > r=read.table("/Users/user/Desktop/research/a.txt",sep=",",header=TRUE) > x<-r[8:1010,] > library(KernSmooth) >

Hi I hope this is going to the right place. I am trying to write a program which uses KernSmooth library to estimate mutual information between two time series at various different lags. At the moment it’s producing negative values, which is supposed to be impossible (something is fishy). I am summing across one row of the matrix to get p(value is in bin x) and summing across the columns to get

Any advice when to use denstity() and when the KernSmooth package bkde() to smooth a histogram? No specific problem to use either one, but I'm curious why there are two so similar implementations. Thanks! mario -- Ing. Mario Valle Data Analysis and Visualization Group | http://www.cscs.ch/~mvalle Swiss National Supercomputing Centre (CSCS) | Tel: +41 (91) 610.82.60 v.

Hello, after thorough searching of the R help files as well as S+-help, I'm coming to the list: Is there a possibility to compute partial correlation coefficients between multiple variables (correlation between two paired samples with the "effects of all other variables partialled out")? All I seem to find are the standard Pearson correlation coefficients (with cor()) and no clue

On 27 April 2017 at 12:01, Johannes Ranke wrote: | | > so it seems to me this must affect all packages in Debian sid that were | > built before the release of R 3.4.0! | | or rather before 14 April 2017, which is when R from revision r72510 was | uploaded to sid as pre-release candidate. Another example with KernSmooth: > library(KernSmooth) KernSmooth 2.23 loaded Copyright

Am Donnerstag, 27. April 2017, 06:32:13 schrieb Dirk Eddelbuettel: > On 27 April 2017 at 12:01, Johannes Ranke wrote: > | > so it seems to me this must affect all packages in Debian sid that were > | > built before the release of R 3.4.0! > | > | or rather before 14 April 2017, which is when R from revision r72510 was > | uploaded to sid as pre-release candidate. >

On 27 April 2017 at 13:58, Johannes Ranke wrote: | Am Donnerstag, 27. April 2017, 06:32:13 schrieb Dirk Eddelbuettel: | > On 27 April 2017 at 12:01, Johannes Ranke wrote: | > | > so it seems to me this must affect all packages in Debian sid that were | > | > built before the release of R 3.4.0! | > | | > | or rather before 14 April 2017, which is when R from revision r72510

Hi all, I have a data frame called "rst", see below: ------------------------------------------------------------------------------------------ # This is a paste able example # In case you don't have "KernSmooth" package installed, please uncomment below line. # install.packages("KernSmooth") library(KernSmooth) rst <- data.frame(hsp = rnorm(23), dal =

Hi List, I am trying to plot a grid with an overlayed height. I have a dataframe with four variables: x,y,gridvalue,height. The dataframe has 2.5mio observations (ie grid points), I assign colors through the gridvalue using map_color_gradient thus producing: x,y,gridvalue,height,gridcol as variables of the dataframe. The grid dimensions are 1253 x 2001 (=2507253 data points). My attempts with

Hi, I was using a density estimation function as follows: > est <- KernSmooth::bkde(x3, bandwidth=10) When setting bandwidth less than 5, I got the error "longer object length, is not a multiple of shorter object length in: kappa * gcounts ". I wonder if there is anybody who can explain the error for me? Thanks! Hui

Hi all, I've just raised a ticket <http://dev.rubyonrails.com/ticket/2826> with a patch for enabling 'snapToGrid' feature for draggables. I'm writing a framework on top of scriptaculous and it appeared to me that this would be something that could belong to core Draggable objects. To activate, just add to draggable options on creation. (By default gridSize is set to 10)

hello R-users.could you please help me on this one?i have 2 vectors x1 and y1 and both have the same size.i want to use the locpoly function from the kernsmooth package in order to estimate a regression function.at each estimation i want to leave one observation out,and i want my estimation to be at the observation i left out.i tried : for (j in 1:length(x1)) +

1. Please do make use of the subject line. 2. Please (re-)read the description of the `range.x' argument in ?locpoly: it's suppose to be a vector of min and max x values. 3. I hope you realize how grossly inefficient this computation is... Andy > From: klea lambrou > > > hello R-users.could you please help me on this one?i have > 2 vectors x1 > and y1 and

Dear list, I've got a question concerning difference between loess and locpoly. I have to use a plug-in method to chose a bandwith so I take locpoly method to fit a curve. My problem is:I know how to get predicted values in loess: m=loess(y~x) y_fitted=predict(m). But how to get the same in locpoly? I computed like this: bw=dpill(x, y, blockmax = 5, divisor = 20,trim = 0.01, proptrun =

There are 4 sets of array, each have 2 dimension data:x,y. And these data comes from bkde. I tried to plot them in one figure, but details of some lines, like peak and where it locate, have diminished since one set of array have fairly high y value.And another array have lots of y value near zero, like 1e-20, 1e-10, like a long tail, while the highest y value of these arrays is 1e-4. Does R have

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