similar to: Compact Indicator Matrices

On 13.2.2015 16:59, Kevin Laurie wrote: > Dear Jiri, > > I tried the following to try to get the inbox deduplicated. My inbox is > quite large and urgently need to remove the duplicated messages. > Is there an easy way to do this? > Sorry for being so presistant but I need help. > > The command I tried:- > > doveadm deduplicate -u user at domain.net mailbox inbox

Why not fix the new names instead to be like the old names? Seems like that would be way simpler... On Feb 15, 2016 12:38 AM, "Ben Skeggs" <skeggsb at gmail.com> wrote: > From: Ben Skeggs <bskeggs at redhat.com> > > We've previously had identical naming between vertex and texture > formats, so it mostly made sense to define these together. > > However,

The green book tells:"The basic technique is classic :keep it simple ."A long ,complicated expression or function is less fravorable than" a relatively small computations that combines calls to a few other functions to perform its tasks." But I don't get the point totally.Can anyone give me an example to make me understand this rules totally? ps: Is it mean that f1 is

From: Ben Skeggs <bskeggs at redhat.com> Signed-off-by: Ben Skeggs <bskeggs at redhat.com> --- src/gallium/drivers/nouveau/nv50/g80_defs.xml.h | 279 ++++++++++++++++++++++++ 1 file changed, 279 insertions(+) create mode 100644 src/gallium/drivers/nouveau/nv50/g80_defs.xml.h diff --git a/src/gallium/drivers/nouveau/nv50/g80_defs.xml.h

From: Ben Skeggs <bskeggs at redhat.com> We've previously had identical naming between vertex and texture formats, so it mostly made sense to define these together. However, upcoming patches are going to transition the driver over to using updated texture header definitions using NVIDIA's naming, and this will no longer be the case. Signed-off-by: Ben Skeggs <bskeggs at

Hello! Maybe "frame" is not the right term in this context. I explain my problem by example code: fun2 <- function(objName, add) { ## the object "objName" should be increased by "add", ## but the evaluation should be done in the calling function (here: fun1) ## ...... what's the right code?? } fun1 <- function() { x <- 1 fun2("x",

Hi, I would like to eliminate all the load instructions and replace their uses with the stored values in the following program. The stores and loads are in the same basic block. Is there an optimization pass in LLVM 3.0 that can do this? define void @testFunc() { entry: %sVal = alloca %sTy %f1 = getelementptr %sTy* %sVal, i32 0, i32 0 store i32 789, i32* %f1 %f2 = getelementptr %sTy*

Hello, I am trying to run a logistic regression with random effects on proportional data in glmmBUGS. I am a newcomer to this package, and wondered if anyone could help me specify the model correctly. I am trying to specify the response variable, /yseed/, as # of successes out of total observations... but I suspect that given the error below, that is not correct. Also, Newsect should be a

Dear all, I have a quasipoisson glm for which I need confidence bands in a graphic: gm6 <- glm(num_leaves ~ b_dist_min_new, family = quasipoisson, data = beva) summary(gm6) library('VIM') b_dist_min_new <- as.numeric(prepare(beva$dist_min, scaling="classical", transformation="logarithm")). My first steps for the solution are following: range(b_dist_min_new)

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

I'm a beginner in R, and trying to fit linear models with different intercepts per group, of the type y ~ A*x1 + B, where x1 is a numerical variable. I cannot understand whether I should use y1 ~ x1 +1 or y1 ~ order(x1) + 1 Although in the toy example included it makes a small difference, in models with many groups the models without order() converge slower if at all!

Hi, I am performing meta-regression using linear mixed-effect model with the lme() function that has two fixed effect variables;one as a log transformed variable (x) and one as factor (y) variable, and two nested random intercept terms. I want to save the predicted values from that model and show the log curve in a plot ; predicted~log(x) mod<-lme(B~log(x)+as.factor(y),

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In Venables \& Ripley 3rd edition (p. 231) the proportional odds model is described as: logit(p<=k) = zeta_k + eta but polr apparently thinks there is a minus in front of eta, as is apprent below. Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version

Hi (R 2.13.1, OSX 10.6.8) I am trying to use predict.rma with continuous and categorical variables. The argument newmods in predict.rma seems to handle coviariates, but appears to falter on factors. While I realise that the coefficients for factors provide the answers, the goal is to eventually use predict.rma with ANCOVA type model with an interaction. Here is a self contained example

Hi, there I'm currently trying to figure out how to keep my "factor" levels for a variable when moving it from one data frame or matrix to another. Example below: vec1<-(rep("10",5)) vec2<-(rep("30",5)) vec3<-(rep("80",5)) vecs<-c(vec1, vec2, vec3) resp<-rnorm(2,15) dat<-as.data.frame(cbind(resp, vecs))

Hi there, I'm sure there's an easy answer to this, and I can't wait to see it. The question: is there an easy way to sort a data frame by it's row names? My dilemma: I've had to pull apart a data frame, run it through a loop to do some calculations and generate new variables, and then re-construct the chunks back into a data frame at the end. Doing this preserves the row

Hello, I'm analysing reaction time data from a linguistic experiment (a variant of a lexical decision task). To ascertain that the data was normally distributed, I used *shapiro.test *for each participant (see commands below), but only one out of 21 returns a p value above p.0 05. > f = function(dfr) return(shapiro.test(dfr$Target.RTinv)$p.value) > p = as.vector(by(newdat,

Dear Alina If I understand you correctly you cannot just have a single predicted curve but one for each level of your factor. On 09/08/2017 16:24, Alina Vodonos Zilberg wrote: > Hi, > > I am performing meta-regression using linear mixed-effect model with the > lme() function that has two fixed effect variables;one as a log > transformed variable (x) and one as factor (y)

Dear Ben, you answerd to Nancy Shackelford about Clarks 2Dt function. Since the thread ended just after your reply, I would like to ask, if you have an idea how to use this function in R I defined it the following way: function(x , p, u) { (p/(pi*u))*(1+(x^2/u))^(p+1) } and would like to fit this one to my obeservational data (count) [,1] [,2] [1,] 15 12 [2,] 45 13 [3,]