similar to: Specify contrasts in R

I've been playing around with contrasts in lm by specifying the contrasts argument. So, I want to specify a specific contrast to be tested Say: > y _ rnorm(100) > x _ cut(rnorm(100, mean=y, sd=0.25),c(-3,-1.5,0,1.5,3)) > reg _ lm(y ~ x, contrasts=list(x=c(1,0,0,-1))) > coef(reg)[2] x1 -1.814101 I was surprised to see that I get a different estimate for the

Hey, I am modelling a linear regression Y=X*B+E. To compute the effect of ?group? the B-values of the regressors/columns that code the interaction effects (col. 5-8 and col. 11-14, see below) have to be weighted with non-zero elements within the contrast "Group 1" minus "Group 2" (see below). My first understanding was that the interaction effects add up to zero in each group.

>>> Maybe I was wrong, but according to Michael's comment it looks like he >>> want >>> check affinity_hint_set just for speculative tx polling on rx napi >>> instead >>> of disabling it at all. >>> >>> And I'm not convinced this is really needed, driver only provide affinity >>> hint instead of affinity, so it's

>>> Maybe I was wrong, but according to Michael's comment it looks like he >>> want >>> check affinity_hint_set just for speculative tx polling on rx napi >>> instead >>> of disabling it at all. >>> >>> And I'm not convinced this is really needed, driver only provide affinity >>> hint instead of affinity, so it's

On Thu, Apr 20, 2017 at 2:27 AM, Jason Wang <jasowang at redhat.com> wrote: > > > On 2017?04?19? 04:21, Willem de Bruijn wrote: >> >> +static void virtnet_napi_tx_enable(struct virtnet_info *vi, >> + struct virtqueue *vq, >> + struct napi_struct *napi) >> +{ >> + if

On Thu, Apr 20, 2017 at 2:27 AM, Jason Wang <jasowang at redhat.com> wrote: > > > On 2017?04?19? 04:21, Willem de Bruijn wrote: >> >> +static void virtnet_napi_tx_enable(struct virtnet_info *vi, >> + struct virtqueue *vq, >> + struct napi_struct *napi) >> +{ >> + if

Hello. Consider the following matrix: mp <- matrix(c(0,1/4,1/4,3/4,0,1/4,1/4,3/4,1/2),3,3,byrow=T) > mp [,1] [,2] [,3] [1,] 0.00 0.25 0.25 [2,] 0.75 0.00 0.25 [3,] 0.25 0.75 0.50 The eigenvectors of the previous matrix are 1, 0.25 and 0.25 and it is not a diagonalizable matrix. When you try to find the eigenvalues and eigenvectors with R, R responses: > eigen(mp) $values [1]

I think what's been missed is that zapsmall works relative to the absolute largest value in the vector. Hence if there's only one item in the vector, it is the largest, so its not zapped. The function's raison d'etre isn't to replace absolutely small values, but small values relative to the largest. Hence a vector of similar tiny values doesn't get zapped. Maybe the line

On Mon, Apr 24, 2017 at 12:40 PM, Michael S. Tsirkin <mst at redhat.com> wrote: > On Fri, Apr 21, 2017 at 10:50:12AM -0400, Willem de Bruijn wrote: >> >>> Maybe I was wrong, but according to Michael's comment it looks like he >> >>> want >> >>> check affinity_hint_set just for speculative tx polling on rx napi >> >>> instead

On Mon, Apr 24, 2017 at 12:40 PM, Michael S. Tsirkin <mst at redhat.com> wrote: > On Fri, Apr 21, 2017 at 10:50:12AM -0400, Willem de Bruijn wrote: >> >>> Maybe I was wrong, but according to Michael's comment it looks like he >> >>> want >> >>> check affinity_hint_set just for speculative tx polling on rx napi >> >>> instead

Dear all, I do not seem to grasp how contrasts are set for ordered factors. Perhaps someone can elighten me? When I work with ordered factors, I would often like to be able to reduce the used polynomial to a simpler one (where possible). Thus, I would like to explicetly code the polynomial but ideally, the intial model (thus, the full polynomial) would be identical to one with an ordered factor.

I was quite suprised to discover that applying `zapsmall` to a scalar value has no apparent effect. For example: > y <- 2.220446e-16 > zapsmall(y,) [1] 2.2204e-16 I was expecting zapsmall(x)` to act like > round(y, digits=getOption('digits')) [1] 0 Looking at the current source code, indicates that `zapsmall` is expecting a vector: zapsmall <- function (x, digits =

Isn?t that the correct outcome? The user can change the number of digits if they want to see small values? -- Change your thoughts and you change the world. --Dr. Norman Vincent Peale > On Dec 17, 2023, at 12:11?AM, Steve Martin <stevemartin041 at gmail.com> wrote: > > ?Zapping a vector of small numbers to zero would cause problems when > printing the results of summary().

Zapping a vector of small numbers to zero would cause problems when printing the results of summary(). For example, if zapsmall(c(2.220446e-16, ..., 2.220446e-16)) == c(0, ..., 0) then print(summary(2.220446e-16), digits = 7) would print Min. 1st Qu. Median Mean 3rd Qu. Max. 0 0 0 0 0 0 The same problem can also appear when

Why do x<-b%*%ginv(A) and x<-solve(A,b) give different results?. It seems that I am missing some basic feature of matrix indexing. e.g.: A<-matrix(c(0,-4,4,0),nrow=2,ncol=2) b<-c(-16,0) x<-b%*%ginv(A);x x<-solve(A,b);x Thanks in advance, Angel

Dear list, As a reply to my recent mail: > simint and TukeyHSD work for aov objects. > Can someone point me to similar functions for lme objects? Douglas Bates wrote There aren't multiple comparison methods for lme objects because it is not clear how to do multiple comparisons for these. I don't think the theory of multiple comparisons extends easily to lme models. One could

Le 17/12/2023 ? 18:26, Barry Rowlingson a ?crit?: > I think what's been missed is that zapsmall works relative to the absolute > largest value in the vector. Hence if there's only one > item in the vector, it is the largest, so its not zapped. The function's > raison d'etre isn't to replace absolutely small values, > but small values relative to the largest.

>>>>> Serguei Sokol via R-devel >>>>> on Mon, 18 Dec 2023 10:29:02 +0100 writes: > Le 17/12/2023 ? 18:26, Barry Rowlingson a ?crit?: >> I think what's been missed is that zapsmall works relative to the absolute >> largest value in the vector. Hence if there's only one >> item in the vector, it is the largest, so its

Le 18/12/2023 ? 11:24, Martin Maechler a ?crit?: >>>>>> Serguei Sokol via R-devel >>>>>> on Mon, 18 Dec 2023 10:29:02 +0100 writes: > > Le 17/12/2023 ? 18:26, Barry Rowlingson a ?crit?: > >> I think what's been missed is that zapsmall works relative to the absolute > >> largest value in the vector. Hence if

>>>>> Steve Martin >>>>> on Mon, 18 Dec 2023 07:56:46 -0500 writes: > Does mFUN() really need to be a function of x and the NA values of x? I > can't think of a case where it would be used on anything but the non-NA > values of x. > I think it would be easier to specify a different mFUN() (and document this > new argument)