similar to: Univariate kernel density estimation with boundary correction

Hi, I found that the R package "KernSmooth" can deal with only 1D and 2D data. But now I have a collection of 4-dimensional data (x1,x2,x3,x4) and would like to estimate the "mode" of the underlying density. What can I do about it ? Thanks a lot. -- Ying-Chao Hung Assistant Professor Graduate Institute of Statistics National Central University Chung-Li, Taiwan TEL:

Hey, Does anyone know of a package in R that provides univariate kernel density estimation with boundary correction ? or how to easily extend an existing bivariate kernel density estimation function (e.g. lambdahat in the spatialkernel package) with boundary corrections to allow univariate density estimation? Thanks a lot, Steve B. -- View this message in context:

To anyone who can help me: I found that the function "smooth.ppp" in the package "spatstat" provides the kernel estimate for the intensity function of a "two-dimensional" point process with marks. Does anyone know that which package can do this for simply a "one- dimensional" point process with marks? I've been searching all over the R site but still

Hi, I previously received help in extract data from a shapefile and now my question is about kernel density estimation. My objective is to have 3 kernel density plots; 2 for the each set of cases and the 3rd is the difference in kernel densities between the 2 sets of cases. Previously, I used the density function from the stats package, which worked but I wanted finer control of the bandwidth.

Dear All, A method for the estimation is univariate unimodal densities (with unknown mode) is described in "Statistical Inference under Order Restrictions" by Barlow et al.. Would anyone know whether there is an R-implementation (preferably with reference) for the estimation of univariate discrete unimodal densities (with unknown mode)? Thanks in advance for your help. Kind

Has anyone developed a defensible method of estimating percentiles from a univariate kernel density estimate? I am working on a problem in which the density estimate is of interest, but I would also like to estimate the value of the variable for which the distribution was, say, 0.20. I spent some time searching the archives and found some message from 2006 that implied such a method was not

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

Hi, I've estimated a simple kernel density of a univariate variable with density(), but after I would like to find out the CDF at specific values. How can I do it? thanks for your help, with it I am very close to finish my first little bit more serious work in R, Viktor

Hi the list *** New package *** R to LaTeX : Univariate analyses r2lUniv *URL:*http://www.r-project.org, http://christophe.genolini.free.fr/r2lUniv *** Description *** r2lUniv performs some basic analyses, then generates a code to be included in a LaTeX document to print the analyses in a (so nice!) LaTeX way. r2lUniv performs the analyses automatically according to the classical statistics

Hi the list *** New package *** R to LaTeX : Univariate analyses r2lUniv *URL:*http://www.r-project.org, http://christophe.genolini.free.fr/r2lUniv *** Description *** r2lUniv performs some basic analyses, then generates a code to be included in a LaTeX document to print the analyses in a (so nice!) LaTeX way. r2lUniv performs the analyses automatically according to the classical statistics

How to test the statistical significance of the difference of two univariate Linear Regression betas? Hi all, There are two samples of data: D1 and D2. On data D1 we do a univariate Linear Regression and get the coefficient beta1. On data D2 we do a univariate Linear Regression and get the coefficient beta2. How do I test the statistical significance of (beta1-beta2)? Could you please

This is a cryptographically signed message in MIME format. --------------ms010908060700000604050108 Content-Type: text/plain; charset=ISO-8859-1; format=flowed Content-Transfer-Encoding: 7bit Sorry. You're right about the univariate numeric operators. My bad. However, I was expecting !x to return a time series, just like the binary logical operators do. For example: > b <-

Dear R users, I use the excelent Anova function of the library car because the easy way to get sphericity correction. Unless I use the scan function. I have not been able to access the values ​​of sum squares and degrees of freedom for each effect in the univariate summary table. Example of the car library for Anova function: library(car) phase <- factor(rep(c("pretest",

Dear R helpers I am having recovery rates as given below and I am trying to estimate the Loss Given Default (LGD) and for this I am using Kernel Density estimation method. recovery_rates = c(0.61,0.12,0.10,0.68,0.87,0.19,0.84,0.81,0.87,0.54,0.08,0.65,0.91, 0.56,0.52,0.30,0.41,0.24,0.66,0.35,0.36,0.64,0.55,0.43,0.36,0.28,0.89,0.11,0.23,0.07,

This seems like a FAQ, but I can't figure it out. I have a mv ts object: R > tsp(pg) [1] 1982 2003 1 R > dim(pg) [1] 22 12 and a univariate ts: R > tsp(rw) [1] 1690 1996 1 Yet, when I try to intersect them: R > tsp(ts.intersect(rw, pg)) [1] 1982 2176 1 the process goes awry. How to I get rw and pg to be one ts that runs from 1982 to 1996 and has 13 univariate time

Hello! I want to realise an univariate time series analysis in R, can someone help me for the first steps? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Univariate-Time-Series-Analysis-in-R-tp4638538.html Sent from the R help mailing list archive at Nabble.com.

I would like to estimate a 95% highest density area for a multivariate parameter space (In the context of anova). Unfortunately I have only experience with univariate kernel density estimation, which is remarkebly easier :) Using Gibbs, i have sampled from a posterior distirbution of an Anova model with k means (mu) and 1 common residual variance (s2). The means are independent of eachother, but

hi everyone! i want to check the autocorrelation function for a univariate time series (streamflow) in a data frame as below: < DF <- read.table("D:/file path....") < DF year jan feb mar apr ...... dec 1966 0.504 0.406 0.740 0.241 0.429 1967 0.683 0.529 0.780 0.443 0.503 . . . . what i first tried is: acf (DF, plot = TRUE)

A quick question that I've had only partial success in answering. I have a multivariate dataset, and would like to extract some simple univariate information from it grouped by treatments, etc. I am encountering two problems however Note: I am importing my data with my_data <- read.csv("/path/data.csv") 1) Scoping of unstack If I attempt sorted_data <- unstack(response,

I am doing analysis on hourly precipitation on a file that is disorganized. However, I managed to clean it up and store it in a dataframe (called CA1) which takes the form as followed: Station_ID Guage_Type Lat Long Date Time_Zone Time_Frame H0 H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 1