similar to: npreg: plotting out of sample, extremely large bandwidths

Dear all, I am using npreg from the np library to run a Kernel regression. My dataset is relatively large and has about 3000 observations. The dependent variable is continuous and I have a total of six independent variables -- two continuous, two ordinal and two categorical. The model converges without problems but it takes a very long time to do so (nearly one hour). Is there any way to speed

I am calculating the mean of each column grouped by the variable 'id'. I do this using aggregate, data.table, and plyr. My aggregate results do not match the other two, and I am trying to figure out what is incorrect with my syntax. Any suggestions? Thanks. Here is the data. myData <- structure(list(var1 = c(31.59, 32.21, 31.78, 31.34, 31.61, 31.61, 30.59, 30.84, 30.98, 30.79, 30.79,

Dear all, I am using np package in order to estimate a model with Klein and Spady estimator. To estimate the model I use KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2) and to estimate beta hats standard errors I use KSi <- npindex(KS, gradients=T, boot.num=300) vcov(KSi) This is

Dear R-helpers, First I apologize if my question is quite simple I have a large datasets which more 100 variables. For a research I need to change all name of variables with add one or more letters on each variables. For example, > data(Pima.tr) > Pima.tr[1:5,] npreg glu bp skin bmi ped age type 1 5 86 68 28 30.2 0.364 24 No 2 7 195 70 33 25.1 0.163 55 Yes 3 5

Hello. One of my teaching assistants was experimenting and encountered unexpected behaviour with the lrtest function in the rms package. It appears that when you have a pair of non-nested models that employ an RCS, the error checking for non-nested models appears not to work. Here is a reproducible example. > library(rms) Loading required package: Hmisc Loading required package: lattice

Dear list, I have been comparing the outputs of two packages for latent class regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that the flexmix package appears to come up with a much better fit than the mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has anyone else observed such behaviour? Has anyone else been successful in using the mmlcr

Hi all, This is a very basic question, but I just can't figure out why R is handling a loop I'm writing the way it is. Here is the script I have written: grid_2_series<-function(gage_handle,data_type,filename) series_name<-paste(gage_handle,data_type,sep="_") data_grid<-read.table(file=paste(filename,".txt",sep=""))

Hello all, I am unsure of how to interpret the output from a Generalized Estimating Equation analysis of an ordinal response. I hope someone can enlighten me. The analysis was done using package 'repolr'. The data consists of a Score on a 3-point scale from 56 Subjects after repeatedly washing their hands with soap. Two soap Products were tested, each panelist washed 10 times = 10

Hi, I'm trying to write a simple function like case1 <- function (m, cov, Q, R) { theta <- (acos(R/sqrt(Q^3))) beta <- (-2)*sqrt(Q)*cos(theta/3)+m[1]/3 rho1 <- (-2)*sqrt(Q)*cos((theta+2*pi)/3)+m[1]/3 rho2 <- (-2)*sqrt(Q)*cos((theta-2*pi)/3)+m[1]/3 stderrb <- deltamethod( ~(-2)*sqrt(Q)*cos(theta/3)+x1/3,m,cov) stderrr1 <- deltamethod(

Dear R-users, I am fitting a kernel regression model of the form y ~ x1 + factor(x2) + factor(x3) and am using the function npregbw in the np-package to find the optimal bandwidths. My dataset is relatively large and the optimization takes quite long. When testing different specifications I have noticed that the optimal bw for x3 is always very close to zero (around 10^-12 or so). I am

#I wish to create a "list of three dataframes" ("results2") from a "list of two dataframes" (temp) and a dataframe ("c")? #Please advise. a <- data.frame(x=c(1,2,3),y=c(5,7,9)) b <- data.frame(x=c(2,4,7,9),y=c(2,3,5,4)) c <- data.frame(x=c(22,34,7,9),y=c(52,63,5,4)) results1 <- list(a,b,c) #what I want #but this is how I need to get there

Dear all For my research, I am learning classification now. I was trying some example about classification tree pakages, such as tree and rpart, for instance, in Pima.te dataset have 8 variables (include class=type): library(rpart) library(datasets) pima.rpart <- rpart(type ~ npreg+glu+bp+skin+bmi+ped+age,data=Pima.te, method='class') plot(pima.rpart, uniform=TRUE) text(pima.rpart)

Hello All, Okay i think I''m finally getting all of what i want out of ferret working, thanks mostly to reading this forum and also getting ALOT of questions answered, thanks alot everyone. Anyway my last ferret task is too get the results sorted by a field called date_registered and have this working with pagination. here is what i''m doing at the moment:

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 porting some existing code of mine to CVS HEAD, and don't understand something about new RegisterClass TableGen definition: class RegisterClass<string namespace, list<ValueType> regTypes,........ { ..... string Namespace = namespace; What is this "namespace" thing? It looks like it should contain the name of backend, right? // RegType

Just to inform: I posted that before in R-sig-ecology but as it might be interesting also for other useRs, I post it also to the general r-user list: Hello Alexandre, thank you very much. I also found another way to extract summarizing information from lm results over e.g. 1000 repeated model runs: results2 <- t(as.data.frame(results)) summary(results2) Although some questions popped up in

Hi,there, I am using R package "boot" to bootstrap. I have one question here: does anybody possibly know how the boot package generates the "indices" which is used in the statistic function? I thought "indices = sample(data, replace=TRUE)", but when I replaced "indices" with this command and used "boot", I got different results. Specifically,

On Sat, 13 May 2006, Vladimir Prus wrote: > I'm porting some existing code of mine to CVS HEAD, and don't understand > something about new RegisterClass TableGen definition: > > class RegisterClass<string namespace, list<ValueType> regTypes,........ > { > ..... > string Namespace = namespace; > > What is this "namespace"

Hello, I have a ferret index with 2 fields: Acts_as_ferret :fields => [:client, :content] If I do model.find_by_contents(query) I obtain all results by the query but I would like to obtain 3 results for each client. Any ideas? Thanks for all. -- Posted via http://www.ruby-forum.com/.

Martyn, you write > I don't think restart() is necessary - it just seems like an invitation to > bad programming to me. It's needed when comparing, say, the time to convergence (if at all) of various iterative processes, for each of a few hundred simulated values. For example one wants basically function(data){ summary<-rep(NA,1000) for(i in 1:1000){