similar to: Total least squares linear regression

I don't find how to do what I need to do in Dalgaard or 'R Cookbook', so I'm asking here. I have a data frame with water chemistry data and I want to start exploring these data. There are three factors (site, date, chemical) associated with each measurement. The data frame looks like this: > summary(chemdata) site_id.sample_date.param.quant

Hi all, I am looking for a clever way to create the following graph using R: I got information on the shares of some subgroups over time (summing up to 1 in each year). The graph I want to create should display the development of the individual shares over time by shading rectangulars for each share in a different color. Is there a clever of doing this? Thanks for your help Stefan

Dear all, I am a SAS user, who's trying R. I am a little bit lost for graphics. What is the simplest way for plotting y as a function of x with one symbol (line or dot) for each level of a class variable z (for SAS langage= plot x*y=z). Can I add vertical bars for standard deviation stored in a separate variable (say ystd). Finally, is it possible to do all this with xyplot function

Full_Name: Gregory R. Warnes Version: R 1.3.1 OS: Solaris 2.8 Submission from: (NULL) (192.77.198.200) rpart library version 3.1-2 Error message: > plotcp(fit.thirds.1,ylim=c(0.7,1.5)); Error in plot.default(ns, xerror, axes = FALSE, xlab = "cp", ylab = "X-val Relative Error", : formal argument "ylim" matched by multiple actual arguments > This can be

I am writing code in C and would like to call R's functions samin and vmmin (optimization routines: simulated annealing and BFGS) I do not understand how to create and pass in the function (as well as the extra arguments it needs) I am optimizing. I have read the R Extensions manual but it is still unclear to me. Could you give me some pointers and/or direct me to some example code which

Hi All: I was recently working with a dataset on arsenic poisoning. Among the variables in the dataset, I used the following three variables to produce crosstabulations (variable names: FOLSTAT, GENDER, ASBIN; all three were categorical variables, FOLSTAT denoted follow up status for the subjects and had seven levels, GENDER denoted sex (two levels: male,female), and ASBIN denoted binarized

Hi, I try to boxplot following data on the subset of (V1,V3,V5,V7) and (V2,V4,V6,V8) V1 V2 V3 V4 V5 V6 V7 V8 2 4 6 7 12 33 43 53 how can I use boxplot function to plot it? thanks, William

Hi all. Has anyone ever done a Deming Regression in R? I'm wondering if there's a simple way to do it. Thanks for all your help! -Ed -- View this message in context: http://www.nabble.com/Deming-Regression-tp17949318p17949318.html Sent from the R help mailing list archive at Nabble.com.

Hello. I want to get the proper influence function for the glm coefficients in R. This is supposed to be inv(information)*(y-yhat)*x. So I am wondering what is the exact mathematical formula for the output that the functions: dfbeta() OR lm.influence()$coefficients return for a glm model. I am confused because: 1. Their columns don't sum to zero as influences should. 2. They

Hello, I'm using mvpart option xv="1se" to compute a regression tree of good size with the 1-SE rule. To better understand 1-SE rule, I took a look on its coding in mvpart, which is : Let z be a rpart object , xerror <- z$cptable[, 4] xstd <- z$cptable[, 5] splt <- min(seq(along = xerror)[xerror <= min(xerror) + xvse * xstd]) I interprete this as following: the

I am trying to clear up all the "NOTE"s before a CRAN submission, but am a bit confused about this one.?? What is it complaining about -- that it doesn't like my name? ... * checking for file ?deming/DESCRIPTION? ... OK * this is package ?deming? version ?1.4-1? * checking CRAN incoming feasibility ... [7s/18s] NOTE Maintainer: ?Terry Therneau <therneau.terry at mayo.edu>?

Hi All, I have some questions on using library rpart. Given my data below, the plotcp gives me increasing 'xerrors' across different cp's with huge xstd (plot attached). What causes the problem or it's not a problem at all? I am thinking 'xerror's should be decreasing when 'cp' gets smaller. Also what the 'xstd' really tells us? If the error bars for

I am using the Unix version of R (version 1.7.0), installed via fink on a G4 Macintosh. I recently upgraded from version 1.6.0 and found that the "car" library now has a problem: ---Begin transcript--- >library(car) Attaching package 'car': The following object(s) are masked from package:base : dfbeta dfbeta.lm dfbetas dfbetas.lm hatvalues hatvalues.lm

Hi All: I am working with a dataset on Arsenic toxicity, and I am trying to calculate the 20th, 40th, 60th, 80th, and highest percentiles for a variable, dietary Moisture (variable name dMoist). The inbuilt function quantile(dMoist) would print 0, 25th, 50th, 75th, and 100th percentile. Does there exist a function that can calculate xth percentile (where x = 10th, 20th, ... etc) values?

Hi All: Greetings, and best wishes from the festive times here at Kolkata, India -- the time of Durga Puja celebrations. I seek your advice as I try plotting lines for my data. The problem: I have created a dataframe that looks like this (name: myFrame): lowest second third fourth highest significance INAS 0.107 0.115 0.123 0.115 0.166 0.000 MMA 0.091 0.107 0.115

Hi, I have a dataset which has var1 from 1 sourse and var2 from 2 different methods. I need a new variable such that var2 values from both methods can beused as 1 variable. I believe deming regression can be used to do this. I just don't know how to do it. My data looks like: idvar1var2method1var2method2 11.22.1NA 21.62.4NA 31.52.2NA 41.3NA2.8 51.6NA3.1 61.4NA2.7 Please help. Sincerely,

Hi, I am using coxph with weights to represent sampling fraction of subjects. Our simulation results show that the robust SE of beta systematically under-estimate the empirical SD of beta. Does anyone know how the robust SE are estimated in coxph using weights? Is there any analytical formula for the “weighted” robust SE? Any help is appreciated! Thanks so much in advance Willy

Hi, I've looked around but can't find a clear answer to the difference for these two? Any help? Thanks! -- View this message in context: http://old.nabble.com/dfbetas-vs-dfbeta-tp26331704p26331704.html Sent from the R help mailing list archive at Nabble.com.

I've prepared preliminary versions of two packages that I plan eventually to contribute to CRAN: car (for "Companion to Applied Regression") is a package that provides a variety of functions in support of linear and generalized linear models, including regression diagnostics (e.g., studentized residuals, hat-values, Cook's distances, dfbeta, dfbetas, added-variable plots,

I've prepared preliminary versions of two packages that I plan eventually to contribute to CRAN: car (for "Companion to Applied Regression") is a package that provides a variety of functions in support of linear and generalized linear models, including regression diagnostics (e.g., studentized residuals, hat-values, Cook's distances, dfbeta, dfbetas, added-variable plots,