similar to: Comparing samples with widely different uncertainties

Hi I need some help getting results from multiple linear models into a dataframe. Let me explain the problem. I have a dataframe with ejection fraction results measured over a number of quartiles and grouped by base_study. My dataframe (800 different base_studies) looks like > afvtprelvefs basestudy quartile ef ef_std entropy CBP0908020 1 21.6 0.53 3.27

Hi I'm sure this question has been asked before but I can't find it in the archives. I have a data frame which includes interval and ordered nominal results. It looks something like "Measured" "Eyeball" 46.5 Normal 43.5 Mild 56.2 Normal 41.1 Mild 37.8 Moderate 12.6 Severe 17.3 Moderate 39.1 Normal 26.7 Mild NULL Normal 27.9 NULL 68.1

Hi With ggplot2 I can very easily create beautiful histograms but I would like to put two histograms on the same plot. The histograms may be over-lapping. When they are overlapping the bars are shown on top of each other (so that the overall height is the sum of the two). Is there any way to get them to display overlapping (with smaller value in front, larger value behind) so that the overall

Hi I have a long data set on which I want to do Bland-Altman style plots for each rhythm type Using ggplot2, when I use geom_hline with facet_grid I get an extra set of empty panels. I can't get it to do it with the "Diamonds" data supplied with the package so here is a (much abbreviated) example: > lvexs cvd_basestudy ecd_rhythm fixed_time variable_time 1 CBP05J02

Hi I am a newbie to R but have tried a number of ways in R to do this and can't find a good solution. (I could do it out of R in perl or awk but would like to know how to do this in R). I have a large data frame 49 variables and 7000 observations however for simplicity I can express it in the following data frame Base, Image, LVEF, ES_Time A, 1, 4.32, 0.89 A, 2, 4.98, 0.67 A, 3, 3.7, 0.5

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 limits,

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

I'm having trouble with a simple application with metRology. I need to estimate the uncertainty of the density thickness of seven sheets of film. This is calculated from measurements of mass, 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

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 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.

Hi all. This is perhaps more a statistics question but I'm hoping someone can help me. I have a group of patients for whom I'm looking at beat to beat RR interval changes. I have plotted the difference between one beat length and the next against the difference between the previous beat length and the current one. This gives me a plot with four quadrants: the bottom left corresponding to

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 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

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

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

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

Hello list, i did try to assign permission to folder for some users in samba4 as BDC, for example chown sandy Temp/ chown: invalid user: `sandy' when sandy is a users create in active directory, why sayme that sandy is invalid users????

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