search for: mstats

Hi All, I have a four panel xyplot. I wish to plot each point as an open or filled circle depending on the value of an indicator variable. I assume I need to use panel.superpose(), but I can't figure out the syntax from lattice documentation. Running R 2.1 under Mac OS X 10.4.2. Any suggestions would be appreciated. Nathan Nathan Leon Pace, MD, MStat University of Utah Salt Lake

To all: I currently download the R binaries for Redhat 7.x Linux. There is considerable turmoil in the vendors of Linux. Redhat apparently is changing it's business model to paid versions. This might motivate my department to use a different vendor of Linux. Is there anything predictable about which vendors/versions of Linux will have R binaries in the future? Thanks, Nathan PS I

Dear Friends, I'm trying to learn to use parallel computation using snow & rpvm Now, I have two boxes: 1) Pentium 4, pvm 3.4.5, R 2.2.0 with FreeBSD 5.4 (box uffbsd); 2) PowerPC, pvm 3.4.5, R 2.1.1 with Debian Linux latest stable (box powerpclinux); I'm using the instructions in (found in snow.pdf) http://www.stat.uiowa. edu/~luke/R/cluster/cluster.html. Now ** from the box

Hi, I using the boot package 1.2-20 on R 2.0.1. My statistics function estimates 6 parameters. In a small percentage of resampled data sets my statistics function doesn't produce an estimate for one parameter and the boot function stops with an error. I can write an ifelse(exists('parameter.estimate'), parameter.estimate, NA) statement within the statistic function to substitute

In the non linear mixed effects package a groupedData object can be created to facilitate modeling. The gD object includes a formula of the form 'response variable' ~ 'primary covariate' | 'grouping factor'. In experiments creating response surfaces there are 2 or more primary covariates. Is there any way to use the groupedData() function to include 2 primary

Hi, Has anyone written code for isotonic regression on ordered rectangular grids? Nathan Nathan Leon Pace, MD, MStat University of Utah Salt Lake City, UT 84132 Office: 801.581.6393 Fax: 801.581.4367 Cell: 801.558.3987 Pager: 801.291.9019 Home: 801.467.2925 [[alternative text/enriched version deleted]]

Hi, I've got a linear mixed model created using lmer: A6mlm <- lmer(Score ~ division + (1|school), data=Age6m) (To those of you to whom this model looks familiar, thanks for your patience with this & my other questions.) Anyway, I was trying this to look at the significance of my fixed effects: A6post <- mcmcsamp(A6mlm, 50000) library(coda) HPDinterval(A6post) ..but I got this

Hi, I was wondering: I've got a dataset where I've got student 'project's nested within 'school's, and 'division' (elementary, junior, or senior) at the student project level. (Division is at the student level and not nested within schools because some students are registered as juniors & others as seniors within the same school.) So schools are random,

This is a question of clarification. IN 2009 Higgins, Thompson and Spiegelhalter (J R Statist Soc A 172:137-159) gave WinBUGs code to get credible intervals from random effects meta analysis for the prediction interval of a new study. It appears that the predict.rma function creates approximate credible intervals (pending a function revision by the author) for that purpose. Is my assumption

Hi, Is the array mstats returned by virDomainMemoryStats call cummulative or snapshot of the counter at that particular time ? I was of the opinion that is cummulative like the /proc counters but this counter seems to go backwards ie next snapshot value is less than the current one which makes me think these are snapshot...

Running under Redhat 7.3 Linux I have installed R Version 1.8.0 (2003-10-08) from a binary download using the rpm manager. Using packageStatus(), I am attempted to upgrade several packages. This is unsuccessful with the following error messages: * Installing *source* package 'MASS' ... ** libs gcc -I/usr/lib/R/include -I/usr/local/include -D__NO_MATH_INLINES -mieee-fp -fPIC -O2

I am running R 2.1.1 on a Mac g5 under Mac OS 10.4.2. I have an xyplot with a single conditioning variable (8 levels) . Here is the code for the conditioning variable used in the formula argument of xyplot: factor( drugauthoryear, levels = c( 'bupicapogna1999', 'levobenhamou2003', 'ropicapogna1999',

...OK ] AddressSanitizerMac.CFStringCreateCopy (506 ms) [ RUN ] AddressSanitizerMac.NSObjectOOB [ OK ] AddressSanitizerMac.NSObjectOOB (2041 ms) [ RUN ] AddressSanitizerMac.NSURLDeallocation [ OK ] AddressSanitizerMac.NSURLDeallocation (165 ms) [ RUN ] AddressSanitizerMac.Mstats [ OK ] AddressSanitizerMac.Mstats (0 ms) [----------] 17 tests from AddressSanitizerMac (66383 ms total) [----------] Global test environment tear-down [==========] 109 tests from 3 test cases ran. (1350454 ms total) [ PASSED ] 109 tests. YOU HAVE 27 DISABLED TESTS > >> Unfo...

I have a function (see below) that does some bootstrapping (I am happy to expand offline why I could use existing functions.) I put my results into and empty matrix and add a row of results with each iteration. My problem is i am a new user to R and I don't understand data frames, matrices, elements, and vectors well. What I would like is to have a data frame I can manipulate outside of the

Hi All, I'm new at programming in R. Some functions create objects for which extractor functions are written to pull out some partial result. I wish to extract partial results from functions without extractor functions: for example, to pass a vector of results to another function. Even after looking at V&R S Programming I don't see a general approach. Specifically, how would I

> Hello, > > I'm running R 1.8.1 on a Mac G4 with Dual 800 MHz CPU, 1.5 GB RAM, and > sufficient disk space. > > I have loaded the boot package version 1.2-15. > > I defined a function to pass to the statistic argument which estimates > a vector (length = 6) of parameter estimates; none are variances. > > I defined a function to pass to the ran.gen argument

(Posted also to debian-user) In my office network I have access to a debian powerpc server and 2 freebsd 6 servers (actually one of them is my notebook). Experienced user of the statistical software R, I have now a go at parallel computation via (r-)pvm and snow under R to enhance the performance of a heavy duty statistical problem involving many iteration on the calculation of models. I moved

In a generalized linear model with k covariates, there are 2(kth power) - 1 possible models (excluding interactions). Awhile ago a posting to R-help suggested Model Selection and Multimodel Inference, 2nd ed, by Burnham and Anderson as a good source for understanding model selection. They recommend (page 71) computing AIC differences over all candidate models in the set of possible models. After

I apologize if this has been answered. I have researched this to the best of my ability, that's not to say the answer isn't in the archives just I am a new user and I don't know the proper terms to search under. I have an object: f <- mpr100 ~ time + nhb + hispanic + other + rural + hrural + factor(age) + factor(gender) +

I've setup a samba-3.0.11 share with [General] guest account = ftp security = domain [pub] comment = pub path = /pub read only = yes browseable = no guest ok = yes And attempts to access the share from WinXP(sp2) clients anonymously fail, Appended is the (log level 2) samba.log entries (names changed to protect the innocent): [2005/02/08 13:34:30, 2]