similar to: glm beta hypothesis testing

I have a vector that has 1,974 elements and each element is one of the following (B, F, N, Y). How do I recreate that vector accept in the place of N put 0 and in the place of B, F or Y put a 1? Thanks, Jacob [[alternative HTML version deleted]]

Following p.206 of "Statistical Models in S", I wish to change the code for summary.glm() so that it estimates the dispersion for binomial & poisson models when the parameter dispersion is set to zero. The following changes [insertion of ||dispersion==0 at one point; and !is.null(dispersion) at another] will do the trick: "summary.glm" <- function(object, dispersion =

On 23 February 2015 at 11:22, David Jones <djones at xtreme-eda.com> wrote: > You have not installed the DataLayout in the Module, as I had pointed out > earlier. > Hi David, I reported earlier that I tried this but there was no improvement. Well I ran another test to be sure. The results are below. As you can see the loads are still present. ; ModuleID =

Hi, I am generating following code: %base = getelementptr inbounds %ravi.CallInfo* %6, i32 0, i32 4, i32 0 %7 = load %ravi.TValue** %base %8 = bitcast %ravi.TValue* %7 to i8* %9 = bitcast %ravi.TValue* %5 to i8* call void @llvm.memcpy.p0i8.p0i8.i32(i8* %8, i8* %9, i32 16, i32 8, i1 false) %10 = load %ravi.CallInfo** %L_ci %base1 = getelementptr inbounds %ravi.CallInfo* %10, i32 0,

On 22 February 2015 at 22:54, Hal Finkel <hfinkel at anl.gov> wrote: >> I tried setting the module's DataLayout to the engine's DataLayout. >> Don't see any improvement. >> The memcpy() is to perform a struct assign, so I tried replacing that >> with member by member store. >> But even then the loads are not being eliminated so I guess the >>

Hi, I have been using this site ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm) to help me with some ARIMA modelling in R. Unfortunately the methods mentioned do not appear to work with second order differencing; arima(*, 2, *). I have used some dummy data to illustrate my point. When I use the xreg=... method, the estimate of intercept is *way* off. This can be seen by the high s.e but I

There may be a bug in the anova.glm function. deathstar[32] R R : Copyright 2004, The R Foundation for Statistical Computing Version 2.0.1 (2004-11-15), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual

Dear all, Being an economics student, I am trying to put together a little tutorial/FAQ/... for those who have (more) background in econometrics rather than in statistics -- just as I have. I'v been looking for a hypothesis testing tool in R (just linear or also nonlinear) for model parameters, but could not find anything so far. I did find a similar but unanswered question in the archives,

Hi Community, I'm trying to reconcile Cook's Distances computed in glm. The following snippet of code shows that the Cook's Distances contours on the plot of Residuals v Leverage do not seem to be the same as the values produced by cooks.distance() or in the Cook's Distance against observation number plot. counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9)

Hi, a small update on this. We have migrated the virtualized host to use the virtio drivers and now the drive performance is improved so that we can see a constant transfer rate. Before it used to be the same rate but regularly dropped to a few bytes/sec for a few seconds and then was fast again. However we still observe that the following fails regularily: $ virsh snapshot-create-as --domain

I have an automatic process setup. It's still pretty new and obviously in need of better error handling because now I find myself in a bad state. I run snapshot-create-as across all my vms, then do zfs replication to the target backup system, then blockcommit everything. virsh snapshot-create-as --domain $vm snap --diskspec $DISK,file=$VMPREFIX/"$vm"-snap.qcow2 --disk-only --atomic

Am 13.10.14 16:25, schrieb Eric Blake: > On 10/13/2014 03:56 AM, Thomas Stein wrote: >> Hello. >> >>> blockcommit vm1 vda --active --verbose --pivot >> >> That's where libvirt stops working at the moment, right? >> >> virsh # blockcommit puppet-test vda --active --verbose --pivot >> error: unsupported flags (0x4) in function

Dear all, I know there have been various questions posted over the years about loops but I'm afraid that I'm still stuck. I am using Windows XP and R 2.9.2. I am generating some data using the multivariate normal distribution (within the 'mnormt' package). [The numerical values of sanad and covmat are not important.] > datamat <-

Dear R-devel list members, Ben Fairbank draw it to my attention that factanal() (in the stats package) doesn't report factor correlations for oblique rotations. Looking at the source, I see that factanal also doesn't save the factor-transformation (rotation) matrix from which these correlations can be computed. I've modified the source, attached below, so that the transformation

Hi, While exploring how summary.lm generated its output I came across a section that left me puzzled. at around line 57 R <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) se <- sqrt(diag(R) * resvar) I'm hoping somebody could explain the logic of these to steps or alternatively point me in the direction of a text that will explain these steps. In particular I'm puzzled

the data file has such structure: 1992 6245 49 . . 20 1 0 0 8.739536 0 . . . . . . . . "alabama" . 0 . 1993 7677 58 . . 15 1 0 0

Hi, Is it possible to "pivot" to a new image without doing blockcopy or blockpull? I know how to use snapshots and blockpull to create a new image and pivot to using it live, but what I would like to do is to have a VM switch from using imageA.qcow2 to image2.qcow2 while running. I don't see why this wouldn't be possible since some of the existing libvirt tools can do this when

I don't have a copy of Dobson (1990) from which the glm.D93 example is taken in example("glm"), but I'm strongly suspecting that these are made-up data rather than real data; the means of the responses within each treatment are _identical_ (equal to 16 2/3), so two of the parameters are estimated as being zero (within machine tolerance). (At this moment I don't understand

Dear list, I've noticed that influence.measures and cooks.distance gives different results for non-gaussian GLMs. For example, using R-1.9.0 alpha (2003-03-17) under Windows: > ## Dobson (1990) Page 93: Randomized Controlled Trial : > counts <- c(18,17,15,20,10,20,25,13,12) > outcome <- gl(3,1,9) > treatment <- gl(3,3) > glm.D93 <- glm(counts ~ outcome +