similar to: linear regression: evaluating the result Q

It looks just like the classical F-test for lack-of-fit, using estimate of `pure errors' from replicates, doesn't it? This should be in most applied regression books. The power (i.e., probability of finding lack-of-fit when it exists) of such tests will depend on the data. Andy > From: RenE J.V. Bertin > > Hello, > > I'd like to come back to this question I

I want to generate a lattice plot of a multiple linear regression. I'm using the code: xyplot(y ~ x1 + x2 | status, data=datam, xlab="Peak separation",ylab="G/W",main="G/W vs Fuzzy peak separation: Threshold=1.8", groups=Fuzzy.gw.t.score>1.8, subset=(status %in% c("control","patient","sibling")),

You need to pay attention to the documentation more closely. If you don't know what something means, that is usually a signal that you need to study more... in this case about the difference between an input variable and a design (model) matrix. This is a concept from the standard linear algebra formulation for regression equations. (Note that I have never used RobPer, nor do I regularly

RenE J.V. Bertin wrote: > Hello, and (somewhat belated) best wishes for 2005. > > Can one order not to draw outliers in bwplot, or at least exclude them from the vertical axis scaling? If so, how (or what doc do I need to consult)? > The options that have this effect in boxplot() do not appear to have any effect with bwplot (although outline=FALSE in boxplot does *not* change the

R-experts, I have fitted many different lines. The fast-tau estimator (yellow line) seems strange to me?because this yellow line is not at all in agreement with the other lines (reverse slope, I mean the yellow line has a positive slope and the other ones have negative slope). Is there something wrong in my R code ? Is it because the Y variable is 1 vector and should be a matrix ? Here is the

Dear list, I have encountered a special case for searching a linear regression where I'm not satisfied with the results obtained using the traditional least squares method (sometimes called OLS) for estimating/optimizing the residues to the regression line (see code below). Basically, a group of my x-y data are a bit off the diagonal line (in my case the diagonal represents the ideal or

Hello, I'm trying to do a little rlm of some data that looks like this: UNIT COHORT perdo adjodds 1010 96 0.39890 1.06894 1010 97 0.48113 1.57500 1010 98 0.36328 1.21498 1010 99 0.44391 1.38608 It works fine like this: rlm(perdo ~ COHORT, psi=psisquare) But the problem is that I have about 100 UNITs, and I want to do a

Hi, I would appreciate of someone could explain how the residual standard error is computed for rlm models (MASS package). Usually, one would expect to get the residual standard error by > sqrt(sum((y-fitted(fm))^2)/(n-2)) where y is the response, fm a linear model with an intercept and slope for x and n the number of observations. This does not seem to work for rlm models and I am wondering

On Friday 17 September 2004 13:52, RenE J.V. Bertin wrote: > Hello again, > > I am doing regressions (using panel.lmline() (and panel.abline( > rlm(...))) ) inside a panel method which I pass to bwplot(). > > What I would like to do is create a boxplot of categorised data > (binned on the independent variable), and superpose a regression line > which is calculated using the

On 31/03/2018 11:57 AM, varin sacha via R-help wrote: > Dear R-experts, > > Here below my reproducible R code. I want to add many straight lines to a plot using "abline" > The last fit (fast Tau-estimator, color yellow) will not appear on the plot. What is going wrong ? > Many thanks for your reply. > It's not quite reproducible: you forgot the line to create

Hello, I have a simple xyplot with rlm lines. I would like to add the a and b coefficients (y=ax+b) of the rlm calculation in each panel. I know I can do it 'outside' the xyplot command but I would like to do all at the same time. I found some posts with the same question, but no answer. Is it impossible ? Thanks in advance for your help. Ptit Bleu. x11(15,12) xyplot(df1$col2 ~

>From: Patrick Connolly <p.connolly@hortresearch.co.nz> >To: "RenE J.V. Bertin" <rjvbertin@hotmail.com> >Subject: Re: [R] changing (core) function argument defaults? >Date: Thu, 18 Nov 2004 11:43:10 +1300 > >On Wed, 20-Oct-2004 at 07:48PM +0200, RenE J.V. Bertin wrote: > >|> Hello,

>From: Patrick Connolly <p.connolly@hortresearch.co.nz> >To: "RenE J.V. Bertin" <rjvbertin@hotmail.com> >Subject: Re: [R] changing (core) function argument defaults? >Date: Thu, 18 Nov 2004 11:43:10 +1300 > >On Wed, 20-Oct-2004 at 07:48PM +0200, RenE J.V. Bertin wrote: > >|> Hello,

Dear List, I used rlm to calculate two regression models for two data sets (rlm due to two outlying values in one of the data sets). Now I want to compare the two regression slopes. I came across some R-code of Spencer Graves in reply to a similar problem: http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg06666.html The code was: > df1 <- data.frame(x=1:10, y=1:10+rnorm(10))

It doesn't print anything: the summary.aov (or summary.aovlist) print method does. ?summary.aov tells you the structure of the objects they return. On Tue, 15 Feb 2005, RenE J.V. Bertin wrote: > I'd like to annotate a plot with the output of summary(aov(model)), > ideally just with the significant effects. I don't find a means to > redirect what that command prints into

It's in the FAQ, Q7.12. On Wed, 19 Nov 2003, RenE J.V. Bertin wrote: > After converting a numeric variable into a factor, is there a way to convert it back to the original values? as.double() doesn't do that correctly, for evident reasons (I guess) and as shown below. -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics,

Rene, Look at ?format. Sean On Sep 17, 2004, at 9:21 AM, RenE J.V. Bertin wrote: > Hello, > > I can't seem to find the way to modify the precision with which > paste() prints its floating point numbers, more precisely the number > of decimal digits printed. This is apparently not controlled by > options( digits= ), and there is no appropriate argument to paste() >

Hello, I try to find a function for M-estimation in multivariate linear regression model (function that can estimate betas in my model: y=x * beta + e, where y is a matrix). I´ve searched R-site for a long time, but I am hopeless. I would like to ask, if there is any function for M-estimation in multivariate linear regression model in R. I know I can estimate betas in my model by rlm() function

On Sun, 10 Oct 2004, RenE J.V. Bertin wrote: > Could I ask some hints/help in interpreting the following ANOVA results, > please? This concerns an experiment where I study the incidence and > severity of motion sickness. I have Sickness.norm, a subjective > discomfort/sickness estimate, normalised to 0..1, the session time T > (normalised to 0..1 and binned in 0.2 wide bins) and a

Hi all, I have probably a basic question, but I can't seem to find the answer in the literature or in the R-archives. I would like to do a robust ANCOVA (using either rlm or lmRob of the MASS and robust packages) - my response variable deviates slightly from normal and I have some "outliers". The data consist of 2 factor variables and 3-5 covariates (fdepending on the model).