similar to: summary(lm(x~y)) difference between R-2.2.1 and R-2.3.0

R Help - I'm trying to run a correlation matrix with a covariate of "age" and will at some point will also want to covary other variables concurrently. I'm using the "psych" package and have tried other methods such as writing a loop to extract semi-partial correlations, but it does not seem to be working. How can I accomplish this? library(psych) > set.cor(y =

Below are the results for the Polyhedron 2005 benchmarks compiled with the llvm/compiler-rt/dragonegg 3.3 release and 3.4svn against FSF gcc 4.8.2. The *-stock-de runs omit the -fplugin-arg-dragonegg-enable-gcc-optzns flag and the *-de-optnz runs include it. There seems to be little improvement between llvm 3.3 and 3.4 in the stock case which relies entirely on llvm for vectorization. Duncan, has

Current dragonegg svn has all of the -fplugin-arg-dragonegg-enable-gcc-optzns bugs for usage with -ffast-math -O3 addressed except for those related to PR2314. Using the -fno-tree-vectorize option, we can evaluate the current state of -fplugin-arg-dragonegg-enable-gcc-optzns with the Polyhedron 2005 benchmarks compared to stock dragonegg and stock gcc 4.5.4. The runtime benchmarks below show that

Hi Y'all, I'm seeing some interesting behavior that I was hoping someone could shed some light on. Basically I'm trying to rsync a lot of files, in a series of about 60 rsyncs, from one server to another. There are about 160 million files. I'm running 3 rsyncs concurrently to increase the speed, and as each one finishes, another starts, until all 60 are done. The machine

Hi Jack, thanks for these numbers. Can you also please measure compile times? I'm thinking of enabling gcc optimizations by default, but I don't want to increase compile times, which means choosing a value for the -fplugin-arg-dragonegg-llvm-ir-optimize option that is low enough to get good compile times, yet high enough to get fast code. It would be great if you could play around with

Duncan, Below are the tabulated compile times and executable sizes. A) gcc 4.5.4svn using -msse3 -ffast-math -O3 -fno-tree-vectorize B) gcc 4.5.4svn/dragonegg using -msse3 -ffast-math -O3 -fno-tree-vectorize -fplugin-arg-dragonegg-enable-gcc-optzns C) gcc 4.5.4svn/dragonegg using -msse3 -ffast-math -O3 -fno-tree-vectorize Compile time (seconds) Benchmark A) stock B) gcc 4.5.4/ C)

Hi Jack, thanks for doing this. > Below are the tabulated compile times and executable sizes. > > A) gcc 4.5.4svn using -msse3 -ffast-math -O3 -fno-tree-vectorize > B) gcc 4.5.4svn/dragonegg using -msse3 -ffast-math -O3 -fno-tree-vectorize -fplugin-arg-dragonegg-enable-gcc-optzns > C) gcc 4.5.4svn/dragonegg using -msse3 -ffast-math -O3 -fno-tree-vectorize These numbers really

The `1' and `2' columns are the error rates within those classes. E.g., the last row of the `1' column should correspond to the class.error for "-", and the last row of the `2' column to the class.error for "+". (I would have thought that that should be fairly obvious, but I guess not. It mimics what Breiman and Cutler's Fortran code does.) I suspect

I am having difficulty understanding how I would constrain a data set by filtering out 'records' based on certain criteria. Using SQL I could query using 'select * from my.data where LithClass in ('sand', 'clay')' or some such. Using subset, there seem to be ghosts left behind (that is, all of the LithClass *.Labels* remain after subset) > dput(tcc)

I've got a list of 5 matrices that are each 5 x 6. I'd like to end up with a 5 x 6 matrix that contains the mean value of the 5 original matrices. I can do this by brute force, but there must be a better way than making each matrix into a vector and then remaking a matrix thanks very much for any help david freedman ll=list(structure(c(9.7, 17.6, 20.8, 24.1, 33.8, 14.5, 25.7, 29.8,

Two time series questions: FITTING TRANSFER FUNCTIONS WITH LAGS: Consider the following toy example: > dates <- paste(11:21, "/01/2005", sep="") > Dates <- as.Date(dates, "%d/%m/%Y") > set.seed(1) > DF <- data.frame(date=Dates, y=rnorm(11), x=rnorm(11, 3)) > arima(DF$y, c(1,0,0), xreg=lag(DF$x, 1)) ar1 intercept lag(DF$x,

Hi, I've noticed huge write amplification problem with thinly provisioned logical volumes and I wondered if anyone can explain why it happens and if and how can be fixed. The behavior is the same on Centos 6.8 and Centos 7.2. I have a NVME card (Intel DC P3600 -2 TB) on which I create a thinly provisioned logical volume: pvcreate /dev/nvme0n1 vgcreate vgg /dev/nvme0n1 lvcreate

Hello Got strange problem with high system "%system load" and very slow user level programs (apache+php+mysql) behavior gstat shows 1.5-4% hard disk busy load but system shows about 20-30% load while user load is max 5%. vmstat shows from 2 to 35 process in "b" state. Now use 7.0-RELEASE-p5 , but the same problem was with 7.0-RELEASE. And have no idea what to do with this.

[cc'ing back to r-help: this is good etiquette so that the responses will be seen by others/ archived for future reference.] On 10-08-25 04:35 PM, Marino Taussig De Bodonia, Agnese wrote: > Yes, I meant "MuMIn" > > the global formula I introduced was: > > rc4.mod<-lm(central$hunting~ central$year + central$gender + central$hunter + central$k.score +

Can anyone work out why my callerid doesn't work on my isdn4Linux with asterisk (or without asterisk for that matter)... This used to work fine, and I am quite confident that the telco is sending callerid information (because they always do on all ISDN lines standard, only extra cost on POTS lines). This is the information from dmesg, whether asterisk is running or not: isdn_net: Incoming

Hi, I am trying to parse XML files and read them into R as a data frame, but have been unable to find examples which I could apply successfully. I'm afraid I don't know much about XML, which makes this all the more difficult. If someone could point me in the right direction to a resource (preferably with an example or two), it would be greatly appreciated. Here is a snippet from one of

On Thu, Jun 09, 2011 at 03:44:40PM +0200, Duncan Sands wrote: > Hi Jack, thanks for doing this. > >> Below are the tabulated compile times and executable sizes. >> >> A) gcc 4.5.4svn using -msse3 -ffast-math -O3 -fno-tree-vectorize >> B) gcc 4.5.4svn/dragonegg using -msse3 -ffast-math -O3 -fno-tree-vectorize -fplugin-arg-dragonegg-enable-gcc-optzns >> C)

Hi, I have an accounting data with "%" at the end and "()" for negative. E.g. if I read in with read.table, then R reads in as a factor: [11625] 4.61% ( 4.11%) 29.85% ( 1.27%) 16.04% 21.31% 14.09% 39.71% [11633] 3.03% 17.39% 80.13% 6648 Levels: ( 0.00%) ( 0.03%) ( 0.04%) ( 0.05%) ( 0.06%) ( 0.07%) ... 99.70% And I was wondering how to convert this column into

I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-

With current dragonegg svn, the Polyhedron 2005 benchmarks all now pass when using the compiler plugin under FSF gcc 4.6.1 on x86_64-apple-darwin11. The differences between the runtime and compile times when using the same plugin under FSF gcc 4.5.4svn are small. Once new vector_select feature is available in llvm and -fplugin-arg-dragonegg-enable-gcc-optzns supports -ftree-vectorizer we might see