similar to: confusing about contrasts concept [long]

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 try to move from SPSS to R/S and am trying to reproduce the results of SPSS in R. I calculated a one-way anova with "spk" as experimental factor and erp as depended variable. The result of the Anova are the same concearning the mean square, F and p values. But I also wanted to caculate the contr.sdif(4) contrast on spk. The results are completely different now. I hope anybody can

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

Hello, I have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

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

Dear R-devel'ers Below is an outline for a set of routines to improve support for multivariate linear models and "classical" repeated measurements analysis. Nothing has been coded yet, so everything is subject to change as loose ideas get confronted by the harsh realities of programming. Comments are welcome. They might even influence the implementation... -pd General

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",

Hi all, Where can I find a thorough explanation of Contrasts and Contrasts Matrices? I read some help but still confused. Thank you, Tian [[alternative HTML version deleted]]

Hi R-users I'm trying to do multivariate analysis of variance of a experiment with 3 treatments, 2 variables and 5 replicates. The procedure adopted in SAS is as follow, but I'm having difficulty in to implement the contrasts for comparison of all treatments in R. I have already read manuals and other materials about manova in R, but nothing about specific contrasts were found in them,

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').

Looking for help kind of in a hurry. I've been searching google but not finding any options. Is there any way to fix missing /dev paths to luns without rebooting? For example, see the output from lsscsi below. The only way I know to fix this is with a reboot, but I REALLY Need to avoid that if possible. Thanks James [2:0:1:150] disk DataCore Virtual Disk DCS - [2:0:1:151]

Hello all R-helpers, I've performed an experiment to test for differential effects of elevated temperatures on three different groups of corals. I'm currently performing a cox proportional hazards regression with censoring on the survivorship (days to mortality) of each individual in the experiment with two factors: Temperature Treatment (2 levels: ambient and elevated) and

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")

R developers, I have a first attempt to make an aov function. Eventually I want to build in Error() structure, but first I am trying to get this presentable for balanced data with only a single stratum, just using residual error. I am following R. M. Heiberger's Computation for the Analysis of Designed Experiments, Wiley (1989) I a using a wrapper (aov.bal) to call the

Martin, I fully agree. This becomes an issue when you have big matrices. (Note that there are awesome methods for actually only computing a small number of PCs (unlike your code which uses svn which gets all of them); these are available in various CRAN packages). Best, Kasper On Thu, Mar 24, 2016 at 1:09 PM, Martin Maechler <maechler at stat.math.ethz.ch > wrote: > Following from

Following from the R-help thread of March 22 on "Memory usage in prcomp", I've started looking into adding an optional 'rank.' argument to prcomp allowing to more efficiently get only a few PCs instead of the full p PCs, say when p = 1000 and you know you only want 5 PCs. (https://stat.ethz.ch/pipermail/r-help/2016-March/437228.html As it was mentioned, we already

For ordered factor the natural contrast coding would be to parametrize by the succsessive differences between levels, which does not assume equal spacing of factor levels as does the polynomial contrasts (implicitly at least). This requires the contr.cum, which could be: contr.cum <- function (n, contrasts = TRUE) { if (is.numeric(n) && length(n) == 1) levs <- 1:n

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

Hi R people I am using regsubsets() to fit large numbers of models and collect summary statistics in order to perform a Bayesian analysis of multi-way ANOVA with specific prior information. In general the variables have differing numbers of levels >=2. This works well but with variable of more than 2 levels there are naturally some arbitrary decisions about which treatment contrasts to

I am simulating an ANOVA model and get a strange behavior from the summary function. To be more specific: please run the following code and see for yourself: the summary()[["r.squared"]] values of two identical models are quite different!! ## 3 x 3 ANOVA of two factors x and z on outcome y s.size <- 300 # the sample size p.z <- c(0.25, 0.5, 0.25) # the probabilities of factor z ##