similar to: confusing about contrasts concept

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

Hi all, I am trying to do a ordered probit regression using polr(), replicating a result from SAS. >polr(y ~ x, dat, method='probit') suppose the model is y ~ x, where y is a factor with 3 levels and x is a factor with 5 levels, To get coefficients, SAS by default use the last level as reference, R by default use the first level (correct me if I was wrong), The result I got is a

Hi all, I'm new to R and have the following problem: I have a 2 factor design (a has 2 levels, b has 3 levels). I have an object kidney.aov which is an aov(y ~ a*b), and when I ask for model.tables(kidney.avo, se=T) I get the following message along with the table of effects: Design is unbalanced - use se.contrast() for se's but the design is NOT unbalanced... each fator level

Kia ora list members: I'm having a little difficulty getting the correct standard errors from a glm.object (R 1.9.0 under Windows XP 5.1). predict() will gives standard errors of the predicted values, but I am wanting the standard errors of the mean. To clarify: Assume I have a 4x3x2 factorial with 2 complete replications (i.e. 48 observations, I've appended a dummy set of data at the

To whom it may concern, I happened to run the following R code just to check the layout of the output, but found that the code doesn't work the way I thought it should work. '' > lm(rnorm(100) ~ rnorm(100)) Call: lm(formula = rnorm(100) ~ rnorm(100)) Coefficients: (Intercept) -0.07966 Warning messages: 1: In model.matrix.default(mt, mf, contrasts) : the response appeared

Hi I have a data.frame with dim = 18638 (rows) 6 (cols) names(dat) [1] "id" "long" "lat" "species" "type" "size" Variable "species" and "type" are factors. Species has 5 levels "BOV" "CAP" "CER" "OVI" "POR" Variable "type" has 11 levels

Hi, I'm trying to figure out how anova works in R by translating the examples in Sokal And Rohlf's (1995 3rd edition) Biometry. I've hit a snag with planned comparisons, their box 9.4 and section 9.6. It's a basic anova design: treatment <- factor(rep(c("control", "glucose", "fructose", "gluc+fruct",

Dear List members: I'm using R1.7.1 (Windows 2000) and having difficulty with scoping. I've studied the FAQ and on-line manuals and think I have identified the source of my difficulty, but cannot work out the solution. For the purposes of illustration. I have three functions as defined below: fnA <- function(my.x) { list(first=as.character(substitute(my.x)), second=sqrt(my.x)) }

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

> On Jan 31, 2017, at 10:59 PM, Tian, Xinmin <xinmin.tian at intel.com> wrote: > > >   <> > From: mehdi.amini at apple.com <mailto:mehdi.amini at apple.com> [mailto:mehdi.amini at apple.com <mailto:mehdi.amini at apple.com>] > Sent: Tuesday, January 31, 2017 9:03 PM > To: Tian, Xinmin <xinmin.tian at intel.com <mailto:xinmin.tian at

> On Jan 31, 2017, at 7:53 PM, Tian, Xinmin <xinmin.tian at intel.com> wrote: > > In this case, inliner is educated to add all local variables to the tag of enclosing parallel region, if there is enclosing parallel region. So isn’t it a good example that shows that your intrinsic *cannot* be opaque and that IR passes need to be modified to handle not only the IR-region

Hi all, I post it on both r-help and r-finance since I don't know where is most appropriate for this topic. Sorry if it bothers you. I did garch fitting on S&P500 monthly returns with garchFit from fSeries. I got same coefficients from all cond.dist except normal. I thought that is probabaly usual for the data. But when I play with it, I got another question. I plot skew normal with

From: mehdi.amini at apple.com [mailto:mehdi.amini at apple.com] Sent: Tuesday, January 31, 2017 9:03 PM To: Tian, Xinmin <xinmin.tian at intel.com> Cc: Sanjoy Das <sanjoy at playingwithpointers.com>; Adve, Vikram Sadanand <vadve at illinois.edu>; llvm-dev at lists.llvm.org; llvm-dev-request at lists.llvm.org Subject: Re: [llvm-dev] [RFC] IR-level Region Annotations On Jan 31,

In this case, inliner is educated to add all local variables to the tag of enclosing parallel region, if there is enclosing parallel region. In our icc implementation, it is even simple, as we have routine level symbol table, the inliner adds ”private” attribute to those local variables w/o checking enclosing scope, the parallelizer does check and use it. Xinmin From: mehdi.amini at apple.com

I was trying "quantreg" package, lm1 <- lm(y~x) rq1 <- rq(y~x) plot(summary(rq1)) #then got a warning says singular value, etc. but this line can be omited plot(lm1) #crash here It happened every time on my PC, Windows XP Pro Serv. Pack 1, Pentium(4) 3.00G. [[alternative HTML version deleted]]

F.Y.I >>> "Tian, Kevin" <kevin.tian@intel.com> 08.11.27. 11:50 >>>Sorry for a typo. I did mean domU instead of dom0. :-) The point here is that time_resume will sync to new system time and wall clock at restore, and thus pv guest should be able to continue... Xen system time is not wallclock time which just counts up from power up. As Keir points out, only its

Yes, I think the 2 outcomes are: - the current spec is unclear and will be clarified - in order to support safelen() and even the simd construct itself, LLVM will require infrastructure work to know when a lexically backwards dependence may have been introduced. Jon -----Original Message----- From: Tian, Xinmin [mailto:xinmin.tian at intel.com] Sent: Monday, September 29, 2014 10:43 AM To:

For example, Type 2 case, scalar-foo used call by value while vector-foo used call by ref. The question Johannes is asking is whether we can decipher that after the fact, only by looking at the two function signatures, or need some more info (what kind, what's minimal)? I think we need to list up cases of interest, and for each vector ABI of interest, we need to work on the requirements and

And “map” and “firstprivate” … are represented as MDString, right? Thanks. From: Hongbin Zheng [mailto:etherzhhb at gmail.com] Sent: Wednesday, January 11, 2017 3:58 PM To: Tian, Xinmin <xinmin.tian at intel.com> Cc: David Majnemer <david.majnemer at gmail.com>; Hal Finkel <hfinkel at anl.gov>; llvm-dev at lists.llvm.org Subject: Re: [llvm-dev] [RFC] IR-level Region

Hi all, I am using polr(). The resulting coefficients of first levels are always 0. What to do if I wnat to get the coefficients of the last level 0. For example, suppose x has 3 levels, 1, 2, 3 probit <- plor(y ~ x, data1, method='probit') will get coefficients of level 2, 3 of x, but I want coefficients of level 1, 2 Thank you, Tian [[alternative HTML version deleted]]