similar to: visualizing bootstrapped dendrogram

Dear members, How can I read and plot an ArcGIS raster file into R ? The file has extension .aux and contains floating point bathymetry data. The purpose is to create a spatial model in R that uses ArcGIS map data. I have managed to read and plot various shape files into my R project, but I am stuck with this now. I am new to this list and also to R, so any help would be much appreciated. Many

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

Hi, I am trying to convert a colleague from using SPSS to R, but am having trouble generating a result that is similar enough to a bootstrapped cox regression analysis that was run in SPSS. I tried unsuccessfully with bootcens, but have had some success with the bootcov function in the rms package, which at least generates confidence intervals similar to what is observed in SPSS. However, the

Dear Friends, I'm trying to learn to how to get Bootstrapped standard errors for estimated coefficients from a regression. For instance suppose I have the following model logitmodel <- glm (y~X1+X2+X3, family=binomial(link="logit")) beta <- logitmodel$coef can somebody please guide me on how to use the package boot to obtain bootstrapped SE's for the associated betas.

I wonder if someone could help me adjusting the following code to parallelized snow code: #Creating a data set (not needed to be parallel) n<-100 p<-100 x<-matrix(rnorm(n*p),p) y<-rnorm(n) # Bootstrapping nboot<-1000 alpha<-0.05 rhoboot <- array(0, dim=c(p,nboot)) bootranks <- array(0, dim=c(p,nboot)) bootsamples <- array( floor(runif(n*nboot)*n+1), dim=c(n,nboot)) for

Dear R-users, I want to perform a One-Sample parametric bootstrapped Kolmogorov-Smirnov GoF test (note package "Matching" provides "ks.boot" which is a 2-sample non-parametric bootstrapped K-S version). So I wrote this code: ---[R Code] --- ks.test.bootnp <- function( x, dist, ..., alternative=c("two.sided", "less", "greater"), B = 1000 ) {

I am trying to work out a bootstrapped Tobit regression model. I get the coefficients all right, but they all have standard error zero. And I am unable to figure out why. I know the coefficients are correct because that's what I get when do a Tobit (without bootstrapping). Here's my code: # Bootstrap 95% CI for Tobit regression coefficients? library(boot) library(AER) # for the Affairs

Hi all I?m trying to get confidence intervals for parameters from nls modeling. I fitted a nls model to the following variables: > x [1] 2 1 1 5 4 6 13 11 13 101 101 101 > y [1] 1.281055090 1.563609934 0.001570796 2.291579783 0.841891853 [6] 6.553951324 14.243274230 14.519899320 15.066473610 21.728809880 [11] 18.553054450 23.722637370 The model fitted was:

R-help is not a free coding service. We expect users to make the effort to learn R and *may* provide help when they get stuck. Pay a local R programmer if you do not wish to make such an effort. Cheers, Bert On Oct 14, 2017 7:58 AM, "Janh Anni" <annijanh at gmail.com> wrote: Greetings! We are trying to obtain confidence and prediction intervals for a predicted Y value from

I run a multinomial regression on a data set with an outcome that has three values. First, I build an initial model, b.mod. Then I run a loop to bootstrap the coefficients. For the initial model, using "predict()", I can print the wrong/false predictions table. But how do I get this table (that is, the predictions) for the bootstrapped model? Thanks for hints, *S* df <-

Greetings! We are trying to obtain confidence and prediction intervals for a predicted Y value from bootstrapped linear regression using the boot function. Does anyone know how to code it? Greatly appreciated. Janh [[alternative HTML version deleted]]

Hello alls, I found in the literature a technique that has been evaluated as one of the more robust to assess statistically the significance of the loadings in a PCA: bootstrapping the eigenvector (Jackson, Ecology 1993, 74: 2204-2214; Peres-Neto and al. 2003. Ecology 84:2347-2363). However, I'm not able to transform by myself the following steps into a R program, yet? Can someone could help

Dear R-Helpers, I am trying to bootstrap the coefficient of variation on a suite of vectors, here I provide an example using one of the vectors in my study. When I ran this script with the vector x <-c(0.625, 0.071428571, 0.133333333, 0.125, 0), it returned CV(boot) [the second one], and stderr(boot) [the second one] without problem. However, when I ran it with the vector in the

Hi, Someone put me on to the Rails Application Visualiser http://rav.rubyforge.org/ Which is quite handy for visualizing how things in the application are related, but I''m interested to know if there is something similar available to deal with visualizing the relationships between views and controllers. It''d be really useful to find a way to see all the various views,

I have data on ~340000 cases where it is desired to predict binary outcome, Withdrawn, using up to 8, A:H, predictors in a 3 x 2^7 design, but where the frequencies in these 168 cells vary enormously (1--108000). As well, there are two additional variables, Agency and Office, and it is desired, among other things, to determine if the rates vary with Agency and Office controlling for A - H. I fit

A revised illustration that incorporates Meem''s earlier recommendations has been posted on our project page: http://opensolaris.org/os/project/clearview/cv-linkif.gif Aside from showing the interrelationships between the devices, links, and interfaces, it also shows how vanity naming might be applied to the configuration. The illustration continues to be a work in progress

Hi, I've added visualization of the speex noise cancellation to my program. I did this by taking st->noise[] and st->ps[], scale both by 1.0/(st->ps_size * 32768.0) (to get a value between 0.0 and 1.0), and then draw them as a realtime lineplot. This works well, and my users like being able to see roughly what frequency bands they have noise in and compare it to their input

Hi All, I don't know if such a thing exists, but I am looking for a way to better keep track of where data is going, how it is being modified, and what functions are acting upon it when I give the data over to a function that calls many subfunctions (as happens in my current package I am working on), or in an R script I am using to do multiple processing steps on data. Currently I find it

Warning: This is a bit off-topic. Using R, I'm trying to develop a way of visualizing a marketing campaign that is divided into lists, each with a mailing and control group. Within a mailing group, we have respondents and sales conversions (think "successes") and the overlap between the two. We also have conversions among those subjects in the list's control group, who were

Dear R users, we are glad to announce the release of our new R package ROCR, for visualizing the performance of scoring classifiers (available on CRAN). We hope that the package might be useful for those of you working on classification problems. For details, see the package description below, or the ROCR website: http://rocr.bioinf.mpi-sb.mpg.de. You can get a short overview by typing