similar to: How to get predicted values of y for different x values?

Dear list, Hello to everybody, I´m interested in finding a package for statistical analysis of binary data, I have a matrix with the following structure: Case1 Case2 Case 3 ....... CaseX Control1 Control2 Control3 ....... ControlY Pep1 1 0 1 1 0 0 0 1 Pep2 1 1 1 1 1 0 0 1 Pep3 0 1 1 1 1 0 0 1 ... Pepz 1 1 1 1 1 0 0 1 I would like to know some R package to calculte similarity matrix or

Can someone show me some code to do normalization by the median of some control genes for the example below? Many Many Thanks in advance This strategy selects a subset of genes (called ?control genes?) and makes the median of their data distribution similar across arrays. ??? ??? id1??? id2??? id3 control1??? 0.8??? 0.7??? 0.6 control2??? 0.6??? 0.2??? 0.4 probe1??? ??? 0.3??? 0.2??? 0.5

I don't know if this is the best place to post this question but I will try anyway. I have two experiements for which I use one-way matched-randomized ANOVA for the analysis and I would like to compare different treatments in the two experiments. The only common group in the two experiments are the controls. Is there any ANOVA design that allows me to make this comparison taking into

I haven't verified what Matt described is what actually happens, but assuming it is - that is a known issue in the x86 cost model. Vectorizing interleaved memory accesses on x86 was, until recently, disabled by default. It's been enabled since r284779, but the cost model is very conservative, and basically assumes we're going to scalarize interleaved ops. I believe Farhana is working

Hi Michael- Since you bring up libquantum performance can you let me know what the IR will look like for this small code snippet (libquantum-like) with –enable-cond-stores-vec ? I ask because I don’t see vectorization kicking in unless -force-vector-width=<> is specified. Let me know if I am missing something. -Thx struct nodeTy { unsigned int c1; unsigned int c2; unsigned

Hi Dibyendu, Are you using a recent compiler? What architecture are you targeting? The target will determine whether the vectorizer thinks vectorization is profitable without having to manually force the vector width. For example, top-of-trunk vectorizes your snippet with "clang -O2 -mllvm -enable-cond-stores-vec" and "--target=aarch64-unknown-linux-gnu". However, with

Hi Matt- Yeah I used a pretty recent llvm (post 3.9) on an x86-64 ( both AMD and Intel ). -dibyendu From: Matthew Simpson [mailto:mssimpso at codeaurora.org] Sent: Wednesday, December 14, 2016 10:03 PM To: Das, Dibyendu <Dibyendu.Das at amd.com> Cc: Michael Kuperstein <mkuper at google.com>; llvm-dev at lists.llvm.org Subject: Re: [llvm-dev] Enabling scalarized conditional stores in

If there are no objections, I'll submit a patch for review that sets the default value of "-enable-cond-stores-vec" to "true". Thanks! -- Matt On Wed, Dec 14, 2016 at 12:55 PM, Michael Kuperstein via llvm-dev < llvm-dev at lists.llvm.org> wrote: > I haven't verified what Matt described is what actually happens, but > assuming it is - that is a known

Thanks Michael and Dibyendu for doing the experimentation and bringing this up to our attention. It might be the case what Matt described here. I will take a look at it. Farhana From: Michael Kuperstein [mailto:mkuper at google.com] Sent: Wednesday, December 14, 2016 9:56 AM To: Das, Dibyendu <Dibyendu.Das at amd.com>; Aleen, Farhana A <farhana.a.aleen at intel.com> Cc: Matthew

R-experts, I have fitted many different lines. The fast-tau estimator (yellow line) seems strange to me?because this yellow line is not at all in agreement with the other lines (reverse slope, I mean the yellow line has a positive slope and the other ones have negative slope). Is there something wrong in my R code ? Is it because the Y variable is 1 vector and should be a matrix ? Here is the

You need to pay attention to the documentation more closely. If you don't know what something means, that is usually a signal that you need to study more... in this case about the difference between an input variable and a design (model) matrix. This is a concept from the standard linear algebra formulation for regression equations. (Note that I have never used RobPer, nor do I regularly

I have data set up like the following: control1 <- sample(1:75, 3947398, replace=TRUE) control2 <- sample(1:75, 28793, replace=TRUE) control3 <- sample(1:100, 392733, replace=TRUE) control4 <- sample(1:75, 858383, replace=TRUE) patient1 <- sample(1:100, 28048, replace=TRUE) patient2 <- sample(1:50, 80400, replace=TRUE) patient3 <- sample(1:100, 48239, replace=TRUE) control

Hi Michael, Thanks for testing this on your benchmarks and target. I think the results will help guide the direction we go. I tested the feature with spec2k/2k6 on AArch64/Kryo and saw minor performance swings, aside from a large (30%) improvement in spec2k6/libquantum. The primary loop in that benchmark has a conditional store, so I expected it to benefit. Regarding the cost model, I think the

I am running a regression: ols.reg1 <- lm(y ~ x1 + x2 + x3 + x4) on a data.frame and then generating fitted values: y.hat <- ols.reg1$fitted.values Then I would like to add these fitted values to the data.frame as a new variable. The problem is that when the values are predicted the resulting output has too few rows. for some reason certian observations do not get predicted values. So

I have a puzzle.... When I include an interaction in the model, many predicted probabilities are above 1. Is that a problem with my model? I thought the predicted prob can't be bigger than 1... Any help would be really appreciated! Thanks! K. reg1<-glm(pyea~male+edu+married+inc+relig+factor(time)+ factor(time)*male, data=mydata, family=binomial(link="logit"))

I am relatively new to R and object oriented programming. I have relied on SAS for most of my data analysis. I teach an introductory undergraduate forecasting course using the Diebold text and I am considering using R in addition to SAS and Eviews in the course. I work primarily with univariate or multivariate time series data. I am having a great deal of difficulty understanding and working with

Hi, See below I reply your message for <https://stat.ethz.ch/pipermail/r-help/2008-April/160966.html>[R] predict.glm & newdata posted on Fri Apr 4 21:02:24 CEST 2008 You say it ##works fine but it does not: if you look at the length of yhat2, you will find 100 and not 200 as expected. In fact predict(reg1, data=x2) gives the same results as predict(reg1). So I am still looking for

Hi all, In several of our tests, I have noticed that the register allocator allocates to virtual registers in a different order when compiling with the clang option -g. Before entering the register allocator, the code is identical when compiling with and without -g (with the exception of " DBG_VALUE" instructions). The only difference I can see is the value assigned to the slot index

Dear colleagues, I have a question regarding controlling the jitter when plotting predictions in the rms package. Below I've simulated some data that reflect what I'm working with. The model predicts a continuous variable with an ordinal score, a two-level group, and a continuous covariate. Of primary interest is a plot of the group by score interaction, where the score is the ordinal

Hi list, in our LLVM-based-project we are writing a backend for our processor. The architecture is a quite straight-forward RISC, but it does not have hardware interlocks, i.e. data hazards involving memory access must be resolved by the compiler, either by scheduling unrelated instructions or by inserting NOOPs into the load delay slots: ---- For example, code which looks like that: load