similar to: formula(model.frame(..)) is misleading

Dear Martin, Since no one else has picked up on this, I?ll take a crack at it: The proposal is to define the S3 class of model-frame objects as c(?model.frame?, ?data.frame?) (not the formal class of these objects, even though this feature was coincidentally introduced in S4). That?s unlikely to do harm, since model frames would still ?inherit? data.frame methods. It's possible that some

I don't have a copy of SV4 (or SV3, where model.frame was introduced), but S+ 8.3 (based on SV4) puts the class "model.frame" on model.frame()'s return value but has no methods (in the default packages) for class "model.frame". Perhaps that is why R omitted the class. However, S+ 8.3's (and problably S's) formula.data.frame did look for a "terms"

Dear R-helpers: My question is how do I efficient and valid correct for multiple tests in a repeated measurement design: Suppose we measure at two distinct visits with repeated subjects a treatment difference on the same variable. The treatment differences are assessed with a mixed model and adjusted by two methods for multiple tests: # 1. Method: Adjustment with library(multcomp)

Dear R-Developers, The traffic today on s-news where Terry Therneau, I, and others are reporting some of the problems we have had using or converting applications to SV4 reminded me of something Duncan Temple Lang had mentioned to me a year ago that I wanted to follow up on. I recall that Duncan said either that if SV4 were to be implemented in R that it would not be the default behavior, or

Dear all, I have encountered some strange things when creating lm objects in R: model depends on the order of the terms specified in a formula. Let us consider the following simple example: > dat <- expand.grid(A = factor(c("a1", "a2")), + B = factor(paste("b", 1:4, sep="")), + rep = factor(1:2)) >

Dear R community, I've been writing simple functions for the past year and half and have come across a similar problem several times. The execution of a function within my own function produces NaN's or fails to execute as intended. My conundrum is that I can execute the function outside of my function without error, so it's difficult for me, as a novice functioneer, to figure out

Hello all, It seems that it is insufficient to rely on MemorySSA to correctly reorder load/stores. For example, we have this following code: v = load q store 10, p => store 10, p v = load q // This is incorrect if p and q may alias In the MemorySSA representation however, there's no syntactic dependency between load/store, because MemorySSA before transformation would look like this:

Hi, I've data from an incomplete fatorial design. One level of a factor doesn't has the levels of the other. When I use lm(), the summary() return NA for that non estimable parameters. Ok, I understant it. But I use contrast::contrast(), gmodels::estimable(), multcomp::glht() and all these fail when model has NA estimates. This is becouse vcov() and coef() has different dimensions. Is

Dear Contributors I have a problem with the collection of data from the results of a test. I need to perform a comparative test over groups of data , recall the value of the pvalue and create a table. My problem is in the way to replicate the analysis over and over again over subsets of data according to a condition. I have this database, called y: gg t1 t2 d 40 1 1

m0<-epxression((4*theta1*theta2-theta3^2)/(2*x*theta3^2)-0.5*theta1*x) params<-all.vars(m0) this reads all the params from m0 so theta1,2 and 3 correct? params<-params[-which(params=="x")] checks which params are multiplied by x? np<-length(params) for(i in 1:6){ esp<-get(sprintf("m%d",i-1))

Hi, I am trying to cross compile code for ARM Cortex m0, m3, and m4. For m0, I use: -target armv6--eabi -mcpu=cortex-m0 That seems to work. For m3 and m4, I use the following which does not work (fatal error: error in backend: CPU: 'cortex-m3' does not support ARM mode): -target armv7m--eabi -mcpu=cortex-m3 and -target armv7em--eabi -mcpu=cortex-m4 Who can help me with the

Hi, After building R 2.14.1 on a Linux system (SUSE Linux Enterprise Server 11 (x86_64) ), I ran "make check" but it halted with the foll error: Testing examples for package ?base? Error: testing 'base' failed Execution halted I looked in <build dir>/test/Examples/base-Ex.Rout.fail and found the foll: *******EXCERPT ***** ... > ### ------- Pickyness Flags :

Hi, all, I know about the "foot shooting" prevention in geom(4) when trying to update the MBR of a mounted disk. I have a system with ad4 and ad6 mirrored and fdisk partitions residing on the mirror to be booted alterningly: hd30# gmirror status Name Status Components mirror/m0 COMPLETE ad4 ad6 hd30# mount /dev/mirror/m0s1a on / (ufs, local, read-only)

Hi Steve, Thanks for the tip regarding MIOperandInfo, I didn't think of that part of the tablegen description. Sadly, I did actually mean: r1 = *(i0 += m0). So increment i0 by m0. Read memory the memory location "pointed" to by i0. Store in r1. Sadly I am not too familiar with compiler terminology, so I don't know if there is a proper term for such a load. On Thu, Oct 23,

Hi all, I have this, Weibull distribution n= 1000 > anova(pk.m0) Df Deviance Resid. Df -2*LL P(>|Chi|) NULL NA NA 999 1451.31 NA > anova(pk.m0,pk.m1) Terms Resid. Df -2*LL Test Df Deviance P(>|Chi|) 1 1 998 1451.310 NA NA NA 2 seed 996 1229.714 2 221.59557 7.605594e-49 With

Hi, I'm trying to calculate the value of the variable, dp, below, in the argument to the integral of dnorm(x-dp) * pnorm(x)^(m-1). This corresponds to the estimate of the sensitivity of an observer in an m-alternative forced choice experiment, given the probability of a correct response, Pc, a Gaussian assumption for the noise and no bias. The function that I wrote below gives me an error:

Hi Krzysztof, Sure, please see below. DAG.dump.() before and after, annotated with what I believe the DAG means. I've spent some time debugging the method but it's proving difficult to determine where the logic is misfiring. Disabling the entire combine causes a lot of failing x86-64 tests - I may have to learn an upstream vector ISA to make progress on this. Thank you >From your

Dear all, The help page on the t distribution says: The most used applications are power calculations for t-tests: Let T= (mX - m0) / (S/sqrt(n)) where mX is the `mean' and S the sample standard deviation (`sd') of X_1,X_2,...,X_n which are i.i.d. N(mu,sigma^2). Then T is distributed as non-centrally t with `df'= n-1 degrees of freedom and non-centrality

Dear all, predict.lme() throws an error when the fixed part consists of only an intercept and using newdata. See the reproducible example below. I've tracked the error down to asOneFormula() which returns in this case NULL instead of a formula. Changing NULL instead of ~1 in that function (see below) solves the problem in the case of an intercept only model (m1). It does not solve the problem

Hi, I am writing a backend and having issues with properly lowering the result of getElementPtr ( specifically the add node that it generates). If we take this IR: %struct.rectangle = type { i24, i24 } ; Function Attrs: nounwind readonly define i24 @area(%struct.rectangle* nocapture readonly %r) #0 { entry: %width = getelementptr inbounds %struct.rectangle* %r, i16 0, i32 0 %0 = load i24*