similar to: Processing a list of fit objects

I have been trying to specify a different color palette to the image method in plot.Design. My model has crossed two rcs() arguments and one two-level gender argument. The goal which appears to have been mostly achieved is to produce separate bivariate plots for men and women The call to plot does produce a level plot but it appears only with the default color palette despite various

I have been attempting to use cph models to get better calibration of my models for which I had originally used logistic regression. I tried running with 40 repetitions and got an error. I then tried 500 repetitions (thinking that the NaNs in the output below might be caused by that choice) and then let my computer crunch for several hours and got only the same error message and

On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > Ralf, et al., > > > > Attached is the latest version of my autovectorization patch. llvmdev > > has been CC'd (as had been suggested to me); this e-mail contains > > additional benchmark results. > > > > First, these are preliminary

On Sat, 2011-10-29 at 15:16 -0500, Hal Finkel wrote: > On Sat, 2011-10-29 at 14:02 -0500, Hal Finkel wrote: > > On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > > > Ralf, et al., > > > > > > Attached is the latest version of my autovectorization patch. llvmdev > > > has been CC'd (as had been suggested to me); this e-mail contains > >

On Sat, 2011-10-29 at 12:30 -0500, Hal Finkel wrote: > Ralf, et al., > > Attached is the latest version of my autovectorization patch. llvmdev > has been CC'd (as had been suggested to me); this e-mail contains > additional benchmark results. > > First, these are preliminary results because I did not do the things > necessary to make them real (explicitly quiet the

Ralf, et al., Attached is the latest version of my autovectorization patch. llvmdev has been CC'd (as had been suggested to me); this e-mail contains additional benchmark results. First, these are preliminary results because I did not do the things necessary to make them real (explicitly quiet the machine, bind the processes to one cpu, etc.). But they should be good enough for discussion.

I have a data set like this I want to assign "outward" to Y if sc <90 and assign "inward" to Y if sc>=90. then cbind(p1982,Y) to get like these p aa as ms cur sc Y 1 154l_aa ARG 152.04 108.83 -0.1020140 92.10410 inward 2 154l_aa THR 15.86 28.32 0.2563560 103.67100 inward 3 154l_aa ASP 65.13 59.16 0.0312137 7.27311 outward 4 154l_aa CYS 57.20 49.85

Hi R list, I imported values from Excel, there is a column with numbers like 45%, 65%, 12%. I want to find its mean. What should I use? strisplit() split() parse() Data from dput(), structure(c(78L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("", "-0.15%", "-0.34%", "-1.3%", "-10.77%", "-100.00%", "-11.45%",

is there a way to use the actual index value for plotting zoo objects this is the way that the index is set up and a sample range of what I would like to plot 01/01/06 00:00:00 - 01/01/06 23:45:00 { library(zoo) # chron library(chron) fmt.chron <- function(x) { chron(sub(" .*", "", x), gsub(".* (.*)", "\\1:00", x)) }} x <- structure(c(15.57, 15.5,

Hi all, I'm trying to work out the following problem, but I can't imagine how. I have the following (much reduced & oversimplified) dataset My.df <- cbind.data.frame(PPM=c(15.78, 15.81, 15.87, 15.83, 15.81, 15.84, 15.91, 15.90, 15.83, 15.81, 15.93, 15.83, 15.70, 15.92, 15.76, 15.81, 15.91, 15.75, 15.84, 15.86, 15.82, 15.79,

Duncan, With the commit from http://lists.cs.uiuc.edu/pipermail/llvm-commits/Week-of-Mon-20121203/158488.html, the Polyhedron 2005 benchmarks complete again on x86_64-apple-darwin12. The result are similar to what were seen with FSF gcc 4.6.2svn and llvm/dragonegg 3.0 (which was the last release that passed pb05) http://lists.cs.uiuc.edu/pipermail/llvmdev/2011-October/044091.html. Jack

Is anyone else having major I/O peaks due to logrotate or other jobs running simultaneously across multiple guests. I have one KVM server running Centos 5.4 with local disk that is seriously suffering as most of the guests rotate their syslog at the same time. Looking at the KVM server I'm seeing 11:00:01 PM CPU %user %nice %system %iowait %steal %idle 03:40:01 AM

I am have a nice result whith blazer_ser driver! respect! # ./nut start Network UPS Tools - UPS driver controller 2.4.1 Network UPS Tools - Megatec/Q1 protocol serial driver 1.51 (2.4.1) Supported UPS detected with megatec protocol Vendor information read in 1 tries Battery runtime will not be calculated (runtimecal not set) <================= !!! Starting nut. Network UPS Tools upsd 2.4.1

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

Hello I'm using logistic regression from the Design library (lrm), then fastbw to undertake a backward selection and create a reduced model, before trying to make predictions against an independent set of data using predict.lrm with the reduced model. I wouldn't normally use this method, but I'm contrasting the results with an AIC/MMI approach. The script contains: # Determine full

Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <-

Hi, I am running a logistic regression model using lrm library and I get the following error when I run the command: mod1 <- lrm(death ~ factor(score), x=T, y=T, data = env1) Unable to fit model using ?lrm.fit? where score is a numeric variable from 0 to 6. LRM executes fine for the following commands: mod1 <- lrm(death ~ score, x=T, y=T, data = env1) mod1<- lrm(death ~

Hi R-helpers I have a dataframe DF (lets say with the variables, y, x1, x2, x3, ..., clust) containing relatively many NAs. When I fit an ordinal regression model with the function lrm from the Design library: model.lrm <- lrm(y ~ x1 + x2, data=DF, x=TRUE, y=TRUE) it will by default delete missing values in the variables y, x1, x2. Based on model.lrm, I want to apply the robust covariance

> On Sep 14, 2017, at 12:30 AM, Bonnett, Laura <L.J.Bonnett at liverpool.ac.uk> wrote: > > Dear all, > > I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk > > I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have

Fixed 'maxiter' in the help file. Thanks. Please give the original source of that dataset. That dataset is a tiny sample of GUSTO-I and not large enough to fit this model very reliably. A nomogram using the full dataset (not publicly available to my knowledge) is already available in http://biostat.mc.vanderbilt.edu/tmp/bbr.pdf Use lrm, not lrm.fit for this. Adding maxit=20 will