search for: 0.0038

Displaying 20 results from an estimated 37 matches for "0.0038".

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2020 Aug 13
2
Accumulating CPU load from Xorg process with DRI3
I observed this bug for quite some time, but so far I workarounded it with just setting DRI2 (default) in xorg.conf.d/20-nouveau.conf Now with two GPU i iwsh to use DRI3, so right now it set up like this: cat /etc/X11/xorg.conf.d/20-nouveau.conf Section "Device" Identifier "Card0" Driver "nouveau" Option "PageFlip" "1" #Option
2017 Dec 20
2
outlining (highlighting) pixels in ggplot2
Using the small reproducible example below, I'd like to know if one can somehow use the matrix "sig" (defined below) to add a black outline (with lwd=2) to all pixels with a corresponding value of 1 in the matrix 'sig'? So for example, in the ggplot2 plot below, the pixel located at [1,3] would be outlined by a black square since the value at sig[1,3] == 1. This is my first
2019 Mar 05
3
Development version of R fails tests and is not installed
G'day all, I have daily scripts running to install the patched version of the current R version and the development version of R on my linux box (Ubuntu 18.04.2 LTS). The last development version that was successfully compiled and installed was "R Under development (unstable) (2019-02-25 r76159)" on 26 February. Since then the script always fails as a regression test seems to
2017 Oct 19
2
Select part of character row name in a data frame
Dear R contributors, I have a problem in selecting in an efficient way, rows of a data frame according to a condition, which is a part of a row name of the table. The data frame is made of 64 rows and 2 columns, but the row names are very long but I need to select them according to a small part of it and perform calculations on the subsets. This is the example: X Y "Unique to
2017 Dec 20
0
outlining (highlighting) pixels in ggplot2
Hi Eric, you can use an annotate-layer, eg ind<-which(sig>0,arr.ind = T) ggplot(m1.melted, aes(x = Month, y = Site, fill = Concentration), autoscale = FALSE, zmin = -1 * zmax1, zmax = zmax1) + geom_tile() + coord_equal() + scale_fill_gradient2(low = "darkred", mid = "white", high = "darkblue",
2020 Aug 13
0
Accumulating CPU load from Xorg process with DRI3
I'm aware of this issue, and am experiencing it myself. The issue is that drmmode_event_handler takes up more and more CPU time. It seems like some events are being "left behind". I haven't had time to debug it further yet though. I also have DRI3 enabled, but only very rarely do I make use of my secondary GPUs, and I'm pretty sure I've seen the problem happen without
2005 Oct 10
1
using innov in arima.sim
Hello, I have used the arima.sim function to generate a lot of time series, but to day I got som results that I didn't quite understand. Generating two time series z0 and z1 as eps <- rnorm(n, sd=0.03) z0 <- arima.sim(list(ar=c(0.9)), n=n, innov=eps) and z1 <- arima.sim(list(ar=c(0.9)), n=n, sd=0.03), I would expect z0 and z1 to be qualitatively similar. However, with n=10 the
2009 Feb 08
0
Initial values of the parameters of a garch-Model
Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model
2008 Apr 06
1
lme cant get parameter estimated correctly
I am caught in a mental trap. Why isn't the between groups variance estimated (0.0038) to be around the value with which I generated the data (0.0002)? Thanks Toby set.seed(76589437887) fph = 0.4 Sigh = sqrt(0.0002) Sigi = sqrt(0.04) ci = 1 fpi = matrix(,7200,3) for (i in 1:90) { fph = rnorm(1, fph, Sigh) for (k in 1:80) { fpi[ci,1:3] = matrix(c(i, k, rnorm(1, fph, Sigi)),1) ci
2007 May 15
1
read.table() can't read in this table (But Splus can) (PR#9687)
On Mon, 2007-05-14 at 23:41 +0200, vax9000 at gmail.com wrote: > Full_Name: vax, 9000 > Version: 2.4.0, 2.2.1 > OS: 2.4.0: Mac OS X; 2.2.1: Linux > Submission from: (NULL) (192.35.79.70) > > > To reproduce this bug, first go to the website "http://llmpp.nih.gov/DLBCL/" and > download the 14.8M data set "Web Figure 1 Data file". The direct link is >
2008 Feb 03
0
[LLVMdev] 2.2 Prerelease available for testing
Target: FreeBSD 6.2-STABLE on i386 autoconf says: configure:2122: checking build system type configure:2140: result: i386-unknown-freebsd6.2 [...] configure:2721: gcc -v >&5 Using built-in specs. Configured with: FreeBSD/i386 system compiler Thread model: posix gcc version 3.4.6 [FreeBSD] 20060305 [...] objdir != srcdir, for both llvm and gcc. Release build. llvm-gcc 4.2 from source.
2001 Jun 07
3
Diag "Hat" matrix
Hi R users: What is the difference between in the computation of the diag of the "hat" matrix in: "lm.influence" and the matrix operations with "solve()" and "t()"? I mean, this is my X matrix x1 x2 x3 x4 x5 [1,] 0.297 0.310 0.290 0.220 0.1560 [2,] 0.360 0.390 0.369 0.297 0.2050 [3,] 0.075 0.058 0.047 0.034 0.0230 [4,] 0.114 0.100
2010 Jun 18
1
12th Root of a Square (Transition) Matrix
Dear R-tisans, I am trying to calculate the 12th root of a transition (square) matrix, but can't seem to obtain an accurate result. I realize that this post is laced with intimations of quantitative finance, but the question is both R-related and broadly mathematical. That said, I'm happy to post this to R-SIG-Finance if I've erred in posting this to the general list. I've
2008 Jan 24
6
[LLVMdev] 2.2 Prerelease available for testing
LLVMers, The 2.2 prerelease is now available for testing: http://llvm.org/prereleases/2.2/ If anyone can help test this release, I ask that you do the following: 1) Build llvm and llvm-gcc (or use a binary). You may build release (default) or debug. You may pick llvm-gcc-4.0, llvm-gcc-4.2, or both. 2) Run 'make check'. 3) In llvm-test, run 'make TEST=nightly report'. 4) When
2008 Jan 28
0
[LLVMdev] 2.2 Prerelease available for testing
Target: FreeBSD 7.0-RC1 on amd64. autoconf says: configure:2122: checking build system type configure:2140: result: x86_64-unknown-freebsd7.0 [...] configure:2721: gcc -v >&5 Using built-in specs. Target: amd64-undermydesk-freebsd Configured with: FreeBSD/amd64 system compiler Thread model: posix gcc version 4.2.1 20070719 [FreeBSD] [...] objdir != srcdir, for both llvm and gcc. Release
2006 Jul 17
1
sem: negative parameter variances
Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per
2003 Nov 24
0
link between arima and arma fit
Hi dear sirs, I am wondering why the fit of the time serie x with an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078
2011 Jan 06
0
Set axis limits in mixtools plot
Hello, Can the x and y axis limits be specified in a density plot with the mixtools package for a finite mixture model? Uncommenting the xlim2/ ylim2 lines in the plot command below generates 'not a graphical parameter' warnings (and does not change the axis settings), and uncommenting the xlim/ylim lines generates a 'formal argument "ylim" matched by multiple actual
2011 Jul 10
1
Package "survival" --- Difference of coxph strata with subset?
[code]>require("survival") > coxph(Surv(futime,fustat)~age + strata(rx),ovarian) Call: coxph(formula = Surv(futime, fustat) ~ age + strata(rx), data = ovarian) coef exp(coef) se(coef) z p age 0.137 1.15 0.0474 2.9 0.0038 Likelihood ratio test=12.7 on 1 df, p=0.000368 n= 26, number of events= 12 > coxph(Surv(futime,fustat)~age, ovarian, subset=rx==1)
2011 Sep 02
1
Parameters in Gamma Frailty model
Dear all, I'm new to frailty model. I have a question on the output from 'survival' pack. Below is the output. What does gamma1,2,3 refer to? How do I calculate joint hazard function or marginal hazard function using info below? Many thanks! Call: coxph(formula = surv ~ as.factor(tibia) + frailty(as.factor(bdcat)), data = try) n=877 (1 observation deleted due to missingness)