similar to: Bootstrapping confidence intervals

Hi! I'm comparing the differences in contaminant concentration between 2 different groups of people ( N=36, N=37). When using a simple linear regression model I found no differences between groups, but when evaluating the diagnostic plots of the residuals I found my independent variable to have deviations from normality (even after log transformation). Therefore I have used bootstrap on

I did some serach around. It seems that ROC curve computation is not supported on R. Anyone has some leads? Thanks. Jason ===== Jason G. Liao, Ph.D. Division of Biometrics UMDNJ School of Public Health 335 George Street, Suite 2200 New Brunswick, NJ 08903-2688 phone (732) 235-9748, fax (732) 235-9777 http://www.geocities.com/jg_liao __________________________________________________ Great

Hi, Sorry for repeated question. I performed logistic regression using lrm and penalized it with pentrace function. I wanted to get confidence intervals of odds ratio of each predictor and summary(MyModel) gave them. I also tried to get bootstrapping standard errors in the logistic regression. bootcov function in rms package provided them. Then, I found that the confidence intervals provided by

Hi. I am using Mandrake 8.1, and the version of Samba is 2.2. I have a small network, using a pc with win 98, a laptop using ME, and the linux machine. The network "pings" each other ok. I have read just about most things as to how to get the network to see each other but, I cannott get any machine to see the Linux machine at all. I would like to set up the Linux

Hi all, New to R, so this may be obvious to some. I've been trying to figure this out for a while, I have a dataset "events" that looks something like this: Area NAME DATE X Xn Y 1 X 1/10/10 1 1 0 1 Y 1/11/10 0 0 1 1 X 1/12/10 1 0 0 1 X 1/12/10 1 0 0 1 X 1/12/10 1 0 0 2 X 2/12/10 1 1 0

I apologize for this post. I am new to R (two days) and I have tried and tried to calculated confidence intervals for medians. Can someone help me? Here is my data: institution1 0.21 0.16 0.32 0.69 1.15 0.9 0.87 0.87 0.73 The first four observations compose group 1 and observations 5 through 9 compose group 2. I would like to create a bootstrapped 90% confidence interval on the difference of

Good morning, I am trying to find a S-Plus code which shows how to find a confidence interval using a bootstrapping on a corrected correlation value of a two bivariate variables. If you happen to know one, please shows me. I am greatly appreciated your help. Have a wonderful day, Minh -- View this message in context:

I'm about to get two fully configured servers dumped into my lap ... both of them are currently *stably* running Linux, and are to be reformated (apparently tomorrow) with FreeBSD 4.8 ... Are there any known issues, like the 'over 2G' issue with the Adaptec 2120s card, with the Intel ZCR Raid controllers that should make me wary of accepting these servers? Is anyone using them in

I have already obtained my confidence intervals from a bootstrapping procedure and now I want to plot the estimates and the confidence intervals similar to the plots obtained when the geom_smooth function is used in ggplot2. Thanks -- View this message in context: http://r.789695.n4.nabble.com/Plotting-Confidence-Intervals-with-a-proiri-calculated-Intervals-tp4322581p4322581.html Sent from the R

Hi, I know this is not exactly an R question but may be someone will nice enough to give me a pointer. I'm using nnet to fit a non linear univariate function. I wonder if there is a way to estimate a confidence interval for the predicted output beside using the residuals? The data set I use is relatively small and I use only a few (5:10) of these as test set or should I say validation set.

Hi, I'm regressing various body dimensions upon body mass using the 'lmodel2' function, as I'm keen to obtain both OLS and RMA slope values. I also wish to create a plot of the regressions, with the 95% confidence interval of both the slope and intercept. I know how to plot 95% ci bands of the OLS slope using lm with the 'predict' function and 'matlines'. Does

I'm using the plotmeans function to plot the means and 95% CI of my groups, however I also want to get the actual numbers of the CI, i.e. the upper and lower bounds. Is there anyway to get that information out of plotmeans? If not, is there an easy way to calculate the CIs? -- View this message in context: http://old.nabble.com/Confidence-intervals-tp26225647p26225647.html Sent from the R

fixed @ R-forge. New version should appear on CRAN soon. Thanks for the report! Torsten > > ---------- Forwarded message ---------- > Date: Sat, 3 Sep 2011 23:56:35 +0200 > From: Ulrich Halekoh <Ulrich.Halekoh at agrsci.dk> > To: "r-help at r-project.org" <r-help at r-project.org> > Subject: [R] glht (multcomp): NA's for confidence intervals using

Dear All, Is it possible to get confidence intervals for Harrell's concordance index or, equivalently, Somer's D using the rms package or in some other way ? I have survival data it would be the c-index in the Cox model setting Many thanks Dr Bernard North Statistical Consultant Statistical Advisory Service Advice and Courses on Research Design and Methodology Imperial College South

Hi all! I would like to estimate confidence intervals for a non lm model. For example, I use a mixed model of the form: md=lme(y~x1+I(x1^2)+x2 ...) Parameters x1+I(x1^2) are fixed effects and I would like to plot the predicted (partial) curve corresponding to these ones, along with 90% CI bands. Thus, I simulate (partial) predictions: Lx=c() Ux=c() Mx=c() z=c() for (j in

Dear all, I have read various descriptions of employing resampling techniques, such as the bootstrap, to estimate the uncertainties of the eigenvectors computed by PCA. When I try

Hi, does anyone know how do I plot confidence intervals as a shaded band around a curve, rather than as errors bars? many thanks, ulisses. [[alternative HTML version deleted]]

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

I would have thought this to be relatively elementary, but I can't find it mentioned in any of my stats texts. Please consider the following: library(fitdistrplus) fp = fitdist(y,"exp"); rate = fp$estimate; sd = fp$sd fOneWeek = exp(-rate*7); #fraction that happens within a week - y is measured in days fr = exp(-rate*dt); #fraction remaining - dt = elapsed time from

How can I performing Bootstrap Confidence Intervals for the estimates of nonparametric regression y=f(x) such as loess and spline smoothing Thanks in advance [[alternative HTML version deleted]]