search for: confid

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...

...e client. However, it changes the filesize to the size of the message on disk, not the message's uncompressed size. For subsequent clients, the FETCH works, but dovecot logs an error (and maybe re-indexes the mailbox?) and the message UID changes. Jan 14 18:40:56 imap01 dovecot: imap(bub at confidence.com): Error: Cached message size larger than expected (1612 > 1556) Jan 14 18:40:56 imap01 dovecot: imap(bub at confidence.com): Error: Maildir filename has wrong S value, renamed the file from /mail/mailstore01/505/236/bub at confidence.com/.Sent Items/cur/1168058702.93517743273399070372.fe...

Hi all, I am trying to plot the one-sided confidence limits for the regression line. It seems it is ok to use predict function to compute the two sided confidence limits. Does any one know a easy way to compute the one sided confidence limits? Thank you very much in advance. Hannah [[alternative HTML version deleted]]

John Fox wrote: >The confidence ellipse for a pair of coefficients is just (with an >adjustment for size) the perpendicular shadow of the joint ellipsoid, in >the same sense that the confidence intervals for individual coefficients >are shadows of the joint ellipsoid or ellipse. The usual confidence ellipse >for 2...

...ike this (typing into mail; the actual fit code is complicated because it involves step-down and so forth): x_sq <- x * x x_cb <- x * x * x x_qt <- x * x * x * x model <- glm(outcome ~ x + x_sq + x_cb + x_qt, binomial) This works great, and I get a nice fit. My question regards the confidence intervals on that fit. I am calling: cis <- confint.default(model, level=0.95) to get the confidence intervals for each coefficient. This confint.default() call gives me a table like this (where "dispersal" is the real name of what I'm calling "x" above):...

I am studying statistics using R and a book "Understandable Statistics", by Brase and Brase. The book has two worked examples for calculating a confidence interval around a predicted value from a linear model. The answers to the two examples in the book differ from those I get from R. The regression line, the standard error, and the predicted value in R and the book all agree for the examples. Hence I gather that R and the book use different f...

Dear List I?m using the ?lmomRFA? package to fit different distributions to my data sample. To calculate the error bounds I used: regsimq(?) and sitequantbounds(?) So my questions are: Are error bounds and confidence intervals the same thing? And: Does regsimq(? boundprob = c(0.05, 0.95)) calculate the 90 or the 95% confidence interval? If error bounds and confidence intervals are not equal: Is there a way to calcu late confidence intervals for my fitted distributions (gev, weibull, gumbel?)? Thank...

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver anot...

Dear all, I created a bivariate normal distribution: set.seed(138813) n<-100 x<-rnorm(n); y<-rnorm(n) and plotted a scatterplot of it: plot(x,y) Now I'd like to add the 2D-standard deviation. I found a thread regarding plotting arbitrary confidence boundaries from Pascal H?nggi http://www.mail-archive.com/r-help at r-project.org/msg24013.html which cites the even older thread http://tolstoy.newcastle.edu.au/R/help/03b/5384.html As I am unfortunately only a very poor R programmer, the code of Pascal H?nggi is a myth to me and I am not sur...

Hi, I am again asking a generic question and the general response for such questions is cold. I am a beginner but use and write simple R scripts. I am looking for some ideas to calculate the confidence intervals based on this excerpt from the paper. Moreover it would help if someone points to material to read about degrees of freedom and any related concepts. Thanks, Mohan Cutting Corners: Workbench Automation for Server Benchmarking APPENDIX: Confidence Intervals Given...

...5 12 10 12 100 100 100 > 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 is: modellogis<-nls(y~SSlogis(x,a,b,c)) It runs OK. Then I calculate confidence intervals for the actual data using: dataci<-predict(as.lm(modellogis), interval = "confidence") BUt I don?t get smooth curves when plotting it, so I want to get other "confidence vectors" based on a new x vector by defining a new data to do predictions: x0 <- seq...

Hi all!! I am trying to plot several confidence intervals in a unique plot. That is, for each x, I have a confidence interval for a parameter related to x and I would like to plot them in the same plot, in order to compare them. The plot would look like some parallel vertical lines, each one corresponding to a x value. Their extrem points wo...

Hello all, I'm trying to establish some confidence intervals on predictions I am making using >predict(nls(...)) and predict.nls (unfortunately) does not utilize the se.fit option. A little more background is that I am trying to match the output with older SAS routines to maintain consistency. Because predict.nls does not provide se's...

Hello all, I have to run a 2 Sample Confidence Interval on some data; the command for such intervals is "confint(...)", but in the help documentation it says that you need a "fitted model object" in order to run this command. What does that mean? The data is very small, it's: x=c(8,12,10,14,2,0,0) y=c(-6,0,1,2,-3,...

I have a 3xN matrix of parameters obtained from N regressions where the 3 parameters are jointly statistically significant. I would like to reproduce a 3D confidence ellipsoid projecting 2D ellipses onto the XY plane as in Figure 5.2 in this text<http://books.google.com/books?id=PMeJGeXA09EC&pg=PA172&dq=confidence+ellipsoid&hl=en&sa=X&ei=oWQxT4OgBYLSiALW85WtAw&ved=0CEIQ6AEwAg#v=onepage&q=confidence%20ellipsoid&f=false>...

I am sure there is something simple here I am missing, so please bear with me. It concerns the computation of the confidence interval for a population mean. The data are 125 measurements of Cs137 radation, a sample data set from Davis "Statistics and Data Analysis in Geology" 3rd ed. (CROATRAD.TXT) ------------------ method 1: using textbook definitions: mean \pm se_mean * t-value mu <- mean(Cs137); n...

Hello Folks- Is there a way to create confidence bands with 'glmmPQL' ??? I am performing a stroke study for Northwestern University in Chicago, Illinois. I am trying to decide a way to best plot the model which we created with the glmmPQL function in R. I would like to plot my actual averaged data points within 95 % confidence...

Hi, I understand bootstrap can be used to estimate 95% confidence interval for some statistics, e.g. variance, median, etc. I have someone suggesting that by resampling certain proportion of the total samples (e.g. 80%) without replacement, we can also get the estimate of confidence intervals. Here we have an example of 1000 obsevations, we would like to esti...

Hi, I found myself trying to figure out the type of confidence interval used for the coefficients of the logistic regression fit by using glm(family=binomial)... I suspect it is Wald confidence interval but am not sure...Does anybody know? Also, if so, how can I ask for likelihood ratio and/or score-based confidence intervals? Yours, Michael ~~~~~~~~~~~~...

Dear R-users! I?m faced with following problem: Given is a sample where the sample size is 12, the sample mean is 30, and standard deviation is 4.1. Based on a Student-t distribution i?d like to simulate randomly 500 possible mean values within a two-tailed 95% confidence interval. Calculation of the lower and upper limit of the two-tailed confidence interval is the easy part. m <- 30 #sample mean s <- 4.1 #standard deviation n <- 12 #sample size quant <- qt(0.975,df=11)*s/sqrt(n)#student-t with two tailed )95% confidence interval l <- m-quant# l...