similar to: npudens(np) Error missing value where TRUE/FALSE needed

Hi List, I'm trying to get a density estimate for a point of interest from an npudens object created for a sample of points. I'm working with 4 variables in total (3 continuous and 1 unordered discrete - the discrete variable is the character column in training.csv). When I try to evaluate the density for a point that was not used in the training dataset, and when I extract the fitted

Hi all, I want to use the npudens() function in the np package (multivariate kernel density estimation), but was confused by the several functions in the following codes,expand.grid(),array(),image() and npudensbw(). This confusion will only be generated in >=3 dimensions. I marked the four places with confusion1-4. I think there should be some kind of correspondence in those four

Hi - I'm just geting started with R, specifically needing to plot some 3D joint distribution functions estimated from data. Happily, the "np" package includes example code which gets most of the way there. The following tweak of the doc code gives me a nice cdf: library(np) library(datasets) data(faithful) f <- npudens(~eruptions+waiting,data=faithful) plot(f,cdf = TRUE,

Hi, I'd like to use a three-dimensional dataset to build a kernel density and then sample from the distribution. I already used the npudens function in the np package to estimate the density and plot it: fit<-npudens(~x+y+z) plot(fit) It takes some time but appears to work well. How can I use this to evaluate the fitted function at a certain point, e.g. (x=1, y=1, z=1)?

(disclaimer: I'm in physics, not stats... ) I have a multivariate problem. One variable, call it R1, and 3 "predictor" variables, P1, P2, P3. My goal is to take a load of training data (I know R1,P1,P2,P3 for about 700 total points), and then predict R1 for a new set of data for which I have all the predictors. Simple, no? I understand how to calculate bandwidths, and I have a

Hi, I need to export a rotating plot like the one generated from the following function into a PPT slide or something that can be viewed by reviewers of a journal: library(np) library(datasets) data(faithful) f <- npudens(~eruptions+waiting,data=faithful) plot(f,view="rotate",neval=100,phi=30,main="",xtrim=-0.2) Thanks! Gabriele Zoppoli, MD Ph.D. Fellow,

I am trying to graphically represent a large set of data who's result is not strictly uniform. http://n4.nabble.com/file/n961629/egraph_rules_list_2.png The scatter plot to the left has all of the data rising in steps however I know that there are cases within my data that do not fit the dotted line. temp<-edat[edat$R1SC>0,,]

I read through the import/export manual a few times and did not see this mentioned and checked the archives as well. I am dealing with data sheets generated by Eprime (a software package for generating experimental psychology paradigms) that output subject responses in a proprietary Edat file format. All individual subject response spread sheets can be merged to form one long file of all