similar to: Manually calculating values from aov() result

Hi, Thanks for this information. Is there any way to force R to use Type-1 SS? I think most textbooks use this only. Thanks and regards, On Wed, 7 Aug 2024 at 17:00, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > > On 2024-08-07 6:06 a.m., Brian Smith wrote: > > Hi, > > > > I have performed ANOVA as below > > > > dat = data.frame( > >

On 2024-08-07 6:06 a.m., Brian Smith wrote: > Hi, > > I have performed ANOVA as below > > dat = data.frame( > 'A' = c(-0.3960025, -0.3492880, -1.5893792, -1.4579074, -4.9214873, > -0.8575018, -2.5551363, -0.9366557, -1.4307489, -0.3943704), > 'B' = c(2,1,2,2,1,2,2,2,2,2), > 'C' = c(0,1,1,1,1,1,1,0,1,1)) > > summary(aov(A ~ B * C, dat))

Dear Brian, As Duncan mentioned, the terms type-I, II, and III sums of squares originated in SAS. The type-II and III SSs computed by the Anova() function in the car package take a different computational approach than in SAS, but in almost all cases produce the same results. (I slightly regret using the "type-*" terminology for car::Anova() because of the lack of exact

Dear R users,I am looking for an easy (i.e., direct) way in obtaining the F and p values from the intercept in anovas with within-subject designs. Data are from a psychophysics expeirment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject.I would like to know not only whether there is an effect of modality, but also wether the main effect is

Dear R users, I am looking for an easy (i.e., direct) way of obtaining the F and p values from the intercept in anovas with within-subject designs. My data are from a psychophysics experiment where I am using d' (d-prime) values obtained from 3 modalities of presentation in each subject. I would like to know not only whether there is an effect of modality, but also whether the main effect is

Looking for more ways to speed up R, I've found that large improvements are possible in the speed of "sum" and "prod" for long real vectors. Here is a little test with R version 2.11.1 on an Intel Linux system > a <- seq(0,1,length=1000) > system.time({for (i in 1:1000000) b <- sum(a)}) user system elapsed 4.800 0.010 4.817 > system.time({for (i

I was wondering if somebody could explain why I get different results here: >treats[,2]<-as.factor(treats[,2]) >treats[,5]<-as.factor(treats[,5]) >treats[,4]<-as.factor(treats[,4]) #there are 'c' on more days than I have 'h2o2', where treats[,4] is the day. I only want 'c' that correspond to the same days that I have a 'h2o2' also.

Hello R-help subscribers, I am analyzing a data set using a mixed logit model, and I have recently discovered some curious behavior. I am hoping you all can help. I first ran the following model in December 2012. lmer(Response.binary ~ ItemType.c * Block + (1 | Subject) + (1 | Word), data=lexdec, family="binomial") I then took a break from the data for the holidays. I returned to

On 2019/5/10 ??8:58, Stefano Garzarella wrote: > While I was testing this new series (v2) I discovered an huge use of memory > and a memory leak in the virtio-vsock driver in the guest when I sent > 1-byte packets to the guest. > > These issues are present since the introduction of the virtio-vsock > driver. I added the patches 1 and 2 to fix them in this series in order > to

While I was testing this new series (v2) I discovered an huge use of memory and a memory leak in the virtio-vsock driver in the guest when I sent 1-byte packets to the guest. These issues are present since the introduction of the virtio-vsock driver. I added the patches 1 and 2 to fix them in this series in order to better track the performance trends. v1:

While I was testing this new series (v2) I discovered an huge use of memory and a memory leak in the virtio-vsock driver in the guest when I sent 1-byte packets to the guest. These issues are present since the introduction of the virtio-vsock driver. I added the patches 1 and 2 to fix them in this series in order to better track the performance trends. v1:

Hello to the whole group. I am a newbie to R, but I got my way through and think it is a lot easier to handle than other software packages (far less clicks necessary). However, I have a problem with respect to the summary of regression results. The summary function gives sth like: Residuals: Min 1Q Median 3Q Max -0.46743 -0.09772 0.01810 0.11175 0.42252