search for: phi_i

...come July we will have fixed that.) Since this is one of the most frequent questions people ask me in direct email, too, let me try (again) to sort it out in some detail. A formula such as y ~ f, where f is a factor in principle generates a single classification model in the form *y_{ij} == mu + phi_i + e_{ij} Write the design matrix in the form X = [1 Xf], where, assuming f has p levels, Xf is the n x p dummy variable (ie binary) matrix corresponding to the phi_i's. So in matrix terms the model is written as *y = 1 mu + Xf phi + e (a) If you remove the intercept term, using y ~ f -1, the...

On Aug 5, 2011, at 10:57 AM, Peter Lawrence wrote: > However it seems that if a landingpad-block has multiple predecessors (often the case, > multiple InvokeInst in the main body of a try-statement all go to the same landingpad- > block), then you cannot move the LandingpadInst in order to break a critical edge unless > you do it for _all_ landingpad-block predecessor edges

Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]

Guys, on second thought... doesn't making the exception registers live from the InvokeInst to the LandingpadInst create problems for critical-edge-splitting ? if a landingpad-edge is critical and needs to be split, won't we be creating and inserting a new BB between the "invoke-block" and the "landingpad-block", and if we do then isn't there the

Hello! Is it possible to use R time series to identificate a process which is subjected to known input? I.e. I have 2 sequences - one is measurements of black box's state and the second is the "force" by which this black box is driven (which is known too) and I want to fit thist two series with AR-process. The "ar" procedure from stats package expects that the force is

Full_Name: Ian McLeod Version: 2.3.1 OS: Windows Submission from: (NULL) (129.100.76.136) > There is a simple bug in acf as shown below: > > z <- 1 > acf(z,lag.max=1,plot=FALSE) > Error in acf(z, lag.max = 1, plot = FALSE) : > 'lag.max' must be at least 1 > This is certainly a bug. There are two problems: (i) the error message is wrong since lag.max is

...> log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} > i=1,\cdots,N (subjects) > j=0,1 (two levels) > k=0,1 (two levels) > > treating the \phi_{i} as nuinsance parameters. If I am reading this piece correctly there should be no difference between a conditional treatment of phi_i in that model and results from the unconditional model one would get from fitting with glm(lambda ~ phi + alpha + beta ,family="poisson"). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.6.9679&rep=rep1&typ e=pdf (But I am always looking for corrections to my error...