search for: uncertainti

...the measurements in data set A are higher but they have a large uncertainty (~20%) while the measurements in data set Bare lower but have a small uncertainty (~4%). I believe, from the physiology, that the true value is likely to be nearer the value of A than of B. I need to show that, despite the uncertainties in the measurements (which are not themselves normally distributed), there is (or is not) a difference between the two groups, (a straight Wilcoxon signed ranks test shows a difference but it cannot include that uncertainty data). Can anybody suggest what I should be looking at? Is there a langu...

Hello, I have some measurements that I am trying to fit a model to. I also have uncertainties for these measurements. Some of the measurements are not well detected, so I'd like to use a limit instead of the actual measurement. (I am always dealing with upper limits, i.e. left censored data.) I have successfully run survreg using the combination of well detected measurements and li...

R-Listers. The following is a rant originally sent privately to Frank Harrell in response to remarks he made on this list. The ideas are not new or original, but he suggested I share it with the list, as he felt that it might be of wider interest, nonetheless. I have real doubts about this, and I apologize in advance to those who agree that I should have kept my remarks private. In view of this,

Hello everyone Suppose I have an S3 class "dog" and a function plot.dog() which looks like this: plot.dog <- function(x,show.uncertainty, ...){ <do a simple plot> if (show.uncertainty){ <perform complicated combinatorial stuff that takes 20 minutes and superimpose the results on the simple plot> } } I think that it would be better to somehow

please reply to hdgbyi@public.guangzhou.gd.cn or bgpgong@hotmail.com, thank you. UNCERTAINTY PRINCIPLE IS UNTENABLE By reanalysing the experiment of Heisenberg Gamma-Ray Microscope and one of ideal experiment from which uncertainty principle is derived , it is found that actually uncertainty principle can not be obtained from these two ideal experiments . And it is found that

please reply to hdgbyi@public.guangzhou.gd.cn or bcpgong@hotmail.com, thank you. UNCERTAINTY PRINCIPLE IS UNTENABLE By reanalysing the experiment of Heisenberg Gamma-Ray Microscope and one of ideal experiment from which uncertainty principle is derived , it is found that actually uncertainty principle can not be obtained from these two ideal experiments . And it is found that

Hello. R-devel, r61697. I am having difficulty interpreting section 1.4 "Writing package vignettes" of the R-exts manual. Specifically, I want to use system.file() in some of my packages to locate a bib file, uncertainty.bib, which is part of the emulator package. I only want to maintain a single .bib file. R-exts says: "All other files needed to re-make the vignette PDFs (such

...ass, length and width of rectangular samples of film. It's not too hard to calculate the whole thing with a little Monte Carlo loop. I get about 0.07 with this: #sample area L<-5*2.54 #cm W<-8*2.54 #cm #sample mass m<-0.2543*1000 #mg #uncertainties L.u<-(1/16)*2.54 #cm (nearest 16th inch) W.u<-(1/16)*2.54 #cm m.u<-0.006*1000 #mg scale calibration data denth<-c(0,0,0) singth<-c(0,0,0) for(i in 1:1e5) { #denth[i]<-7*dt+sum(rnorm(7,0,dt.u)) for(j in 1:7)...

I have measurements with uncorrelated uncertainties on both axes. I would like to get the uncertainties on the intercept and the slope of the weighted linear regression model taking into account the uncertainties of the measurements. Is these any way to do that in R? Thanks- Nicolas

One of the assumptions, on which the (General) Linear Modelling is based is that the response variable is measured with some uncertainties (or weighted), but the explanatory variables are fixed. Is it possible to extend the model by assigning the weights to the explanatory variables as well? Is there a package for doing such a model fit? Thanks

hi, When I try to use the function coordProj {mclust} " coordProj(diabetes[,-1],dimens=c(2,3),what="uncertainy",uncertainty=diabetesModel$uncertainty,parameters=diabetesModel$parameters) " to identify uncertainty, it errors and send this warning message: " Warning message: In coordProj(diabetes[, -1], dimens = c(2, 3), what = "uncertainy", : what

Dear R Helpers, I was looking at the email help threads in trying to find a calculation in R of Thiel's uncertainty coefficient. One of the writers offered to send the function in custom code to the inquirer. Can I get a copy of that code, or does anyone know if the calculation is now available in an R package? Please advise. Many thanks. --John J. Sparks, Ph.D.

Dear sir Schwartz, In response to a granted online request to receive R code in order to generate Theil's Uncertainty coefficient, I was hoping I could receive the same favor. https://stat.ethz.ch/pipermail/r-help/2011-May/279210.html Thank you in advance, I hope to hear from you. Kind regards, Jos? Snoep Stagiair Universitair | MC ES - SOFY +31 6 13060740 Snoep.Jose at

I have a small model running under R. This is basically running various power-law relations on a variable (in this case water level in a river) changing spatially and through time. I'd like to include some kind of error propagation to this. My first intention was to use a kind of monte carlo routine and run the model many times by changing the power law parameters. These power laws were

Dear colleagues in R, Has anybody implemented the 1) (Goodman & Kruskal) lambda or the 2) (Thiel's) uncertainty coefficient statistics (in the asymmetric and symmetric forms), or is anyone aware that they might reside in some package? A search in the R archives does indicate that they are (somehow) part of the CoCo package, but I would rather not start learning how to transform my

Dear all, Very simple question, but apparently uneasy to solve in R: I have a sampling of a variable x: (3, 4. 5, 2, ...) I want to know: - the mean <x> -> mean(x) - the uncertainty on <x> -> std.error(x) ? Or sd(x)? - the standard deviation of x -> ? - the uncertainty on the standard deviation -> ? Anyone has an idea? Thanks in advance,

Dear R friends I´m interested into apply a Jackknife analysis to in order to quantify the uncertainty of my coefficients estimated by the logistic regression. I´m using a glm(family=’binomial’) because my independent variable is in 0 - 1 format. My dataset has 76000 obs, and I´m using 7 independent variables plus an offset. The idea involves to split the data in let’s say 5 random subsets and

Hi there, I'm new to R but I thought this is the most likely place I could get advice or hints w.r.t the following problem: I have a series of measurements xi with associated uncertainties dxi. I would like to construct the probability density histogram of this data where each density estimate has an associated error that is derived from the dxi. In other words, for large dxi the histogram should also display large uncertainties and vice versa. I need this for a curve fitting a...

As this forum proved to be very helpful, I got another question... I'd like to fit data points on which I have an error, dx and dy, on each x and y. What would be the common procedure to fit this data by a linear model taking into account uncertainty on each point? Would weighting each point by 1/sqrt(dx2+dy2) (and taking dx and dy as relative errors) in a lm() fit do the job? I would like to

I need to know of my version of Postfix supports a feature, given rh version numbers don't really tell you much I was trying to find an errata on postfix or anything to let me know the real version of it. How does one deal with this scenario? Is there a source of info to determine this info? Thanks! jlc -------------- next part -------------- An HTML attachment was scrubbed... URL: