similar to: Helmert contrasts for repeated measures and split-plot expts

Dear List Apologies for such a simple question: I have a vector of 738 elements, coded with values between 1 and 16 (but not containing 7, 10, 11 or 13) and wish to recode value 14 to 1, 4 to 2, 1 to 3, 2 to 4 and all other values to 5. I've been trying to use the replace function (in base) and %in%, but without success. Many thanks Roy

Hi, perhaps this is a stupid question, but i need some help about Helmert contrasts in the Cox model. I have a survival data frame with an unordered factor `group' with levels 0 ... 5. Calculating the Cox model with Helmert contrasts, i expected that the first coefficient would be the same as if i had used treatment contrasts, but this is not true. I this a error in reasoning, or is it

Hi, for example one could do it this way: v <- summary(fit)$coefficients[,4] the coefficient attribute is a matrix, and with the "4" you refere to the pvalue (at least in lm - don't know if summary(glm) produces sligthely different output). to skip the intercept (1st row): v <- summary(glmfit)$coefficients[-1,4] hope this helps, Arne -- Arne Muller, Ph.D.

Hello I'm very new to R (which is excellent), so apologies if this has already been raised. In the spdep package, I'm trying to undertake an autoregressive mixed model using the lagsarlm function. This is working fine, but there does not appear to be a method of including an explanatory variable without it automatically being included as a lagged term. I'm after something along the

Hello I'm very much a beginner on meta-analysis, so apologies if this is a trivial posting. I've been sent a set data from separate experimental studies, Treatment and Control, but no measure of the variance of effect sizes, numbers of replicates etc. Instead, for each study, all I have is the mean value for the treatment and control (but not the SD). As far as I can tell, this forces

I am new to statistics, R, and this list, so apologies in advance for the errors etiquette I am certain to make (in spite of reading the posting guide, help on various commands, etc.). ?Any help is greatly appreciated. Here is my data: fatigue = c(3,2,2,3,2,3,4,3,2,4,5,3,3,2,4,5,4,5,5,6,4,6,9,8,4,3,5,5,6,6,6,7,9,10,12,9) n <- 3 train <- gl(3, 4*n, labels=c("6wks",

I'm wondering which textbook discussed the various contrast matrices mentioned in the help page of 'contr.helmert'. Could somebody let me know? BTW, in R version 2.9.1, there is a typo on the help page of 'contr.helmert' ('cont.helmert' should be 'contr.helmert').

Hi, I found that using different contrasts (e.g. contr.helmert vs. contr.treatment) will generate different fitted probabilities from multinomial logistic regression using multinom(); while the fitted probabilities from binary logistic regression seem to be the same. Why is that? and for multinomial logisitc regression, what contrast should be used? I guess it's helmert? here is an example

I am using RW 1.2.3. on an IBM PC 300GL. Using the data bp.dat which accompanies Helen Brown and Robin Prescott 1999 Applied Mixed Models in Medicine. Statistics in Practice. John Wiley & Sons, Inc., New York, NY, USA which is also found at www.med.ed.ac.uk/phs/mixed. The data file was opened and initialized with > dat <- read.table("bp.dat") >

I am running a two-way anova with Type III sums of squares and would like to be able to understand what the different SS mean when I use different contrasts, e.g. treatment contrasts vs helmert contrasts. I have read John Fox's "An R and S-Plus Companion to Applied Regression" approach -p. 140- suggesting that treatment contrasts do not usually result in meaningful results with Type

Hi Folks, I've encountered something I hadn't been consciously aware of previously, and I'm wondering what the explanation might be. In (on another list) using R to demonstrate the difference between different contrasts in 'lm' I set up an example where Y is sampled from three different normal distributions according to the levels ("A","B","C")

On 6/23/05, RenE J.V. Bertin <rjvbertin at gmail.com> wrote: > Hello, > > I was just having a look at the aov function source code, and see that when the model used does not have an Error term, Helmert contrasts are imposed: > > if (is.null(indError)) { > ... > } > else { > opcons <- options("contrasts") >

Hi all, I have dataset with 2 independent variable, one (x1) is continuous, the other (x2) is a categorical variable with 2 levels. The dependent variable (y) is continuous. When I run linear regression y~x1*x2, I found that the p value for the continuous independent variable x1 changes when different contrasts was used (helmert vs. treatment), while the p values for the categorical x2 and

Tian It appears the attachment might not have worked so I'll embed Bill's message at the end. Peter Alspach > -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Peter Alspach > Sent: Thursday, 17 August 2006 8:02 a.m. > To: T Mu; R-Help > Subject: Re: [R] confusing about contrasts concept

I think your idea to remove the warnings is excellent, and a good compromise. Characters already work fine in modeling functions except for the silly warning. It is interesting how often the defaults for a program reflect the data sets in use at the time the defaults were chosen. There are some such in my own survival package whose proper value is no longer as "obvious" as it was

> On Feb 3, 2020, at 2:50 PM, Nadav Amit <namit at vmware.com> wrote: > >> On Feb 3, 2020, at 8:34 AM, David Hildenbrand <david at redhat.com> wrote: >> >> On 03.02.20 17:18, Alexander Duyck wrote: >>> On Mon, 2020-02-03 at 08:11 -0500, Michael S. Tsirkin wrote: >>>> On Thu, Jan 30, 2020 at 11:59:46AM -0800, Tyler Sanderson wrote:

> On Feb 3, 2020, at 2:50 PM, Nadav Amit <namit at vmware.com> wrote: > >> On Feb 3, 2020, at 8:34 AM, David Hildenbrand <david at redhat.com> wrote: >> >> On 03.02.20 17:18, Alexander Duyck wrote: >>> On Mon, 2020-02-03 at 08:11 -0500, Michael S. Tsirkin wrote: >>>> On Thu, Jan 30, 2020 at 11:59:46AM -0800, Tyler Sanderson wrote:

On 08.03.20 05:47, Tyler Sanderson wrote: > Tested-by: Tyler Sanderson <tysand at google.com> > > Test setup: VM with 16 CPU, 64GB RAM. Running Debian 10. We have a 42 > GB file full of random bytes that we continually cat to /dev/null. > This fills the page cache as the file is read. Meanwhile we trigger > the balloon to inflate, with a target size of 53 GB. This setup

On 08.03.20 05:47, Tyler Sanderson wrote: > Tested-by: Tyler Sanderson <tysand at google.com> > > Test setup: VM with 16 CPU, 64GB RAM. Running Debian 10. We have a 42 > GB file full of random bytes that we continually cat to /dev/null. > This fills the page cache as the file is read. Meanwhile we trigger > the balloon to inflate, with a target size of 53 GB. This setup

Hi! I would like to suggest to make it possible, in one way or another, to get meaningful contrast names when using contr.sum(). Currently, when using contr.treatment(), one gets factor levels as contrast names; but when using contr.sum(), contrasts are merely numbered, which is not practical and can lead to mistakes (see code at the end of this message). This issue was discussed quickly in 2005