similar to: bootstrap based confidence band

Displaying 20 results from an estimated 700 matches similar to: "bootstrap based confidence band"

2005 Dec 29
1
use of predict() with confidence/prediction bands
To my understanding, a confidence interval typically covers a single valued parameter. In contrast, a confidence band covers an entire line with a band. In regression, it is quite common to construct confidence and prediction bands. I have found that many people are connecting individual confidence/prediction interval values produced with predict(object,sd.fit=T,type="conf/pred") and
2007 Jun 08
1
pointwise confidence bands or interval values for a non parametric sm.regression
Dear all, Is there a way to plot / calculate pointwise confidence bands or interval values for a non parametric regression like sm.regression? Thank you in advance. Regards, Martin
2008 Jun 18
1
Pointwise Confidence Bounds on Logistic Regression
Hi all. I hope I have my terminology right here... For a simple lm, one can add ?pointwise confidence bounds? to a fitted line using something like >predict(results.lm, newdata = something, interval = "confidence") (I'm following DAAG page 154-155 for this) I would like to do the same thing for a glm of the logistic regression type, for instance, the example in MASS pg
2011 Mar 15
2
Pointwise division of two zoo objects?
Just trying to create returns from prices, and do something like: returns.z = tail(prices.z,-1)/head(prices.z,-1) - 1 # should be equivalent to returns = exp(diff(log(prices.z))) - 1 Curiously, I get a zoo object back with zeros everywhere and also with the index having one fewer element than it should. Does anyone know how to pointwise divide zoo objects, and what exactly "/" is
2010 Dec 22
3
How to integrate a function with additional argument being a vector or matrix?
Dear expeRts, I somehow don't see why the following does not work: integrand <- function(x, vec, mat, val) 1 # dummy return value A <- matrix(runif(16), ncol = 4) u <- c(0.4, 0.1, 0.2, 0.3) integrand(0.3, u, A, 4) integrate(integrand, lower = 0, upper = 1, vec = u, mat = A, val = 4) I would like to integrate a function ("integrand") which gets an "x" value (the
2008 Sep 01
1
Porting Swfdec to Mac OS X & Timing question
Hi all: I've been working on porting Swfdec to Mac OS X on my own time. So far I'm making some progress; stuff is displaying and events are working. I still have a bit of work to do, but in the meantime, I have some questions: 1. How does one get the ability to commit things to the repository? I don't expect this right away; I'll probably just distribute the Mac OS X
2003 Jun 04
2
gam()
Dear all, I've now spent a couple of days trying to learn R and, in particular, the gam() function, and I now have a few questions and reflections regarding the latter. Maybe these things are implemented in some way that I'm not yet aware of or have perhaps been decided by the R community to not be what's wanted. Of course, my lack of complete theoretical understanding of what
2010 Mar 27
0
data fitting and confidence band
Hello, I am fitting data using different methods e.g. Local Polynomial and Smoothing splines. The data is generated out of a true function model with added normally distributed noise. I would like to know "how often the confidence band for all points simultaneously contain all true values". I can answer the question for one point in the following way: e.g. #
2007 Jan 26
1
bootstrap bca confidence intervals for large number of statistics in one model; library("boot")
Sometimes one might like to obtain pointwise bootstrap bias-corrected, accelerated (BCA) confidence intervals for a large number of statistics computed from a single dataset. For instance, one might like to get (so as to plot graphically) bootstrap confidence bands for the fitted values in a regression model. (Example: Chiu S et al., Early Acceleration of Head Circumference in Children with
2004 Mar 12
1
confidence interval in local polynomial regression
Dear all, Is there any package or function can do the pointwise confidence interval and confidence band for the local polynomial regression? Maybe the local linear regression is enough. Thanks. Regards, Zhen
2005 Aug 18
1
display of a loess fitted surface
Good morning, I am Marta Colombo,student at Politecnico,Milan. I am studying local regression models and I am using loess function. My problem is that when I have a loess object I don't know how to display the fitted surface; in fact, while in S when you have a loess object you can see it writing plot(object), in R this dosen't work. Also I'd like to know if there is something like the
2004 May 25
1
Bivariate interpolation
Hello, Is there any other bivariate pointwise interpolation command besides akima's interpp? I tried to search through the J. Baron's page without luck. The problem is that I have got regularly spaced data (in x and y) what is not acceptable for interpp. I am not very much interested in the method of interpolation as the data are dense and the error would not be to high. Thanks in
2006 Nov 04
1
Error when using cobs library
Dear R-Users, I have problems with the cobs library. When doing the cobs example, I get the folling error message: example(cobs) cobs> x <- seq(-1, 3, , 150) cobs> y <- (f.true <- pnorm(2 * x)) + rnorm(150)/10 cobs> con <- rbind(c(1, min(x), 0), c(-1, max(x), 1), c(0, 0, 0.5)) cobs> Rbs <- cobs(x, y, constraint = "increase", pointwise = con)
2009 May 18
1
Generic 'diff'
I would like to apply a function 'f' to the lagged version of a vector and the vector itself. This is easy to do explicitly: mapply( f, v[-1], v[-length(v)] ) or in the case of a pointwise vector function, simply f( v[-1], v[-length(v)] ) This is essentially the same as 'diff' but with an arbitrary function, not '-'. Is there a standard way to do this? Is
2007 Feb 01
3
Help with efficient double sum of max (X_i, Y_i) (X & Y vectors)
Greetings. For R gurus this may be a no brainer, but I could not find pointers to efficient computation of this beast in past help files. Background - I wish to implement a Cramer-von Mises type test statistic which involves double sums of max(X_i,Y_j) where X and Y are vectors of differing length. I am currently using ifelse pointwise in a vector, but have a nagging suspicion that there is a
2009 Apr 28
2
attempted upgrade this weekend
Morning, This weekend I attempted an upgrade of my primary samba server from 3.0.24 to 3.3.3. When testing this primary server after the upgrade I had a few issues, so rolled back the upgrade until I can find solutions. This server also has the OpenLDAP server local to and co-located with samba. The two things that initially didn't seem right are that each time I logged into a windows XP box
2003 Jul 21
3
Confidence Band for empirical distribution function
Hi, I was trying to draw an empirical distribution function with uniform confidence bands. So I tried to find a way to calculate values of the Kolmogorov-Smirnov Distribution but failed. I guess it must be hidden somewhere (since the ks-test is implemented), but I was unable to find it. Is there any way to do this? Thanks Leif Boysen
2005 Sep 28
1
confidence variability bands for kernel estimators
I'm using nonparametric regression (packeges ksmooth and ks). My question: is there any way to compute confidence bands (or variability bands) with R. Confidence bands for functions are intervals [CLO(x);CUP(x)] such that with probability 1-alpha the true curve is covered by the band [CLO(x);CUP(x)]. Thanks very much for any help you can offer. Michael G??lger
2010 Oct 06
4
problem with abline
Hi All, I am running a scatter plot and trying to add a best fit line. I use an abline function, but get no line drawn over the points. I also get no error. I arm using V 2.10.0 on Windows 7. Here is my code, including the SAS transport file import: require (foreign) require (chron) require (Hmisc) require (lattice) clin <- sasxport.get("y:\\temp\\subset.xpt") attach(clin)
2013 Jun 05
0
[R-pkgs] bpcp package for pointwise confidence intervals for a survival distribution
Hi all, I just uploaded a new version of the bpcp package. It calculates confidence intervals for a survival distribution for right-censored data using the newly developed beta product confidence procedure. Previously developed methods can have substantial error rate inflation for the lower limit, especially at the right end of the curves when there are small numbers of events. The bpcp method