similar to: [RFC/PATCH 10/15] kvm-s390: intercepts for diagnose instructions

From: Carsten Otte <cotte at de.ibm.com> From: Christian Borntraeger <borntraeger at de.ibm.com> This patch introduces in-kernel handling of _some_ sigp interprocessor signals (similar to ipi). kvm_s390_handle_sigp() decodes the sigp instruction and calls individual handlers depending on the operation requested: - sigp sense tries to retrieve information such as existence or running

From: Carsten Otte <cotte at de.ibm.com> From: Christian Borntraeger <borntraeger at de.ibm.com> This path introduces handling of sie intercepts in three flavors: Intercepts are either handled completely in-kernel by kvm_handle_sie_intercept(), or passed to userspace with corresponding data in struct kvm_run in case kvm_handle_sie_intercept() returns -ENOTSUPP. In case of partial

From: Carsten Otte <cotte at de.ibm.com> From: Christian Borntraeger <borntraeger at de.ibm.com> This path introduces handling of sie intercepts in three flavors: Intercepts are either handled completely in-kernel by kvm_handle_sie_intercept(), or passed to userspace with corresponding data in struct kvm_run in case kvm_handle_sie_intercept() returns -ENOTSUPP. In case of partial

From: Carsten Otte <cotte at de.ibm.com> From: Christian Borntraeger <borntraeger at de.ibm.com> This patch introduces in-kernel handling of some intercepts for privileged instructions: handle_set_prefix() sets the prefix register of the local cpu handle_store_prefix() stores the content of the prefix register to memory handle_store_cpu_address() stores the cpu number of

This patch series introduces a backend for kvm to run on IBM System z machines that uses the mainframe's sie virtualization capability. This work runs 64bit guests on z800/z890/z900/z990/z9/z10 class machines with a 64bit linux host. Userspace will follow once we're done brushing it over. The patch queue consists of the following patches, which can be applied in sequence on top of kvm.git

This patch series introduces a backend for kvm to run on IBM System z machines that uses the mainframe's sie virtualization capability. This work runs 64bit guests on z800/z890/z900/z990/z9/z10 class machines with a 64bit linux host. Userspace will follow once we're done brushing it over. The patch queue consists of the following patches, which can be applied in sequence on top of kvm.git

From: Carsten Otte <cotte at de.ibm.com> This patch contains the s390 interrupt subsystem (similar to in kernel apic) including timer interrupts (similar to in-kernel-pit) and enabled wait (similar to in kernel hlt). In order to achieve that, this patch also introduces intercept handling for instruction intercepts, and it implements load control instructions. This patch introduces an

From: Carsten Otte <cotte at de.ibm.com> From: Christian Borntraeger <borntraeger at de.ibm.com> From: Heiko Carstens <heiko.carstens at de.ibm.com> This patch contains the port of Qumranet's kvm kernel module to IBM zSeries (aka s390x, mainframe) architecture. It uses the mainframe's virtualization instruction SIE to run virtual machines with up to 64 virtual CPUs

From: Carsten Otte <cotte at de.ibm.com> From: Christian Borntraeger <borntraeger at de.ibm.com> From: Heiko Carstens <heiko.carstens at de.ibm.com> This patch contains the port of Qumranet's kvm kernel module to IBM zSeries (aka s390x, mainframe) architecture. It uses the mainframe's virtualization instruction SIE to run virtual machines with up to 64 virtual CPUs

Hello all, I need to infer if the slopes and intercept of each group of my lme analyze, showed below, are different from each other. Like a Tukey test. I don't need to compare the slope and interceptof each subject of my fixed data, but the slope and intercept of each group of them. > m1<-lme(fvfm~term*sp*env*est*time,random=~term|id,table) You know how can I make it? Thanks for

Hi, I search online and find rpar argument to specify random effect for independent variables. Is there a way to specify that for intercepts too? Thanks, Aaron Management, PhD Stanford University [[alternative HTML version deleted]]

David Stoffer describes some challenges with R's output when fitting ARIMA models for different orders (see Issue 2 at http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm). R doesn't fit an intercept in the model if there is any differencing. David describes a workaround using the xreg parameter to force R to calculate an intercept. Assume I have a variable y and 3 explanatory variables a,

Hi I'm getting the intercepts of the Random effects as 0. Please help me to understand why this is coming Zero This is my R code Data<- read.csv("C:/FE and RE.csv") Formula="Y~X2+X3+X4 + (1|State) + (0+X5|State)" fit=lmer(formula=Formula,data=Data) ranef(fit). My sample Data State Year Y X2 X3 X4 X5 X6 S2 1960 27.8 397.5 42.2 50.7 78.3 65.8 S1 1960 29.9 413.3 38.1

mgcv: Constructing probabilities for binomial GAM with crossed random intercepts and factor by variable Hello, (I'm sorry if this has been discussed elsewhere; I may not have been looking in the right places.) I ran a binomial GAM in which "Correct" is modelled in terms of the participant's age and the modality in which the stimulus is presented (written vs spoken).

Hi all: I perform the linear mixed model for 300 persons, y is CD4 count,x is time. I randomized slope and intercept,so I can get 300 slopes and 300 intercepts.Now I wanna test wheter the variance of 300 slopes and 300 intercepts differs from zero. If the variance of 300 slopes(or intercepts) differs from zero at 0.05 significant level,I should randomize the slope(or intercept), and if not,I

Hi everyone, I have been using REML to derive intercepts and coeficients for each individual in a growth study. So the code is m2 <- lmer(change.wt ~ newwt+(newwt|id), data = grow) Calling coef(model.lmer) gives a matrix with this information which is what I want. However, as a test I looked at each individual on its own and used a simple linear regression to obtain the same information, then

A colleague asked me if there is a way to specify with a ***variable*** (say ``cflag'') whether there is an intercept in a linear model. She had in mind something like lm(y ~ x - cflag) where cflag could be 0 or 1; if it's 0 an intercept should be fitted, if it's 1 then no intercept. This doesn't work ``of course''. The cflag just gets treated as another predictor

I posted this question way down at teh end of another thread realted to an error in step, but that was stupid since it really is another matter altogether. I should have posted it separately, as I have now done. The code below creates a data.frame comprising three marginally noisy surfaces. The code below (including a fix courtesy of David Winsemius that avoids a step function error through use

I fit the model fit<-lm(thresh~cond*Ne) where thresh is the reponse cond is a factor with levels a, b, and c Ne is a continuous indep var I think of this full model as having three lines: thresh as a function of Ne for each condition. Thus we have slopea, slopeb, slopec, inta, intb, intc. lm output my params ------------------------- (Intercept) inta condb intb - inta condc

Can you provide the summary(m2) results? > -----Original Message----- > From: Simon Pickett [mailto:S.Pickett at exeter.ac.uk] > Sent: Wednesday, August 16, 2006 7:14 AM > To: Doran, Harold > Cc: r-help at stat.math.ethz.ch > Subject: [SPAM] - RE: [R] REML with random slopes and random > intercepts giving strange results - Bayesian Filter detected spam > > Hi again,