similar to: Device problems in a loop.

Hello, I want to have a title that will look something like: "Results for \theta=2.1", given that I have a variable theta=2.1, and \theta should show on the screen like the greek letter. I've tried a lot of things: theta <- 2.1 plot(1:10, main=expression(paste("Results for", theta, "=", eval(theta)))) or using bquote plot(1:10, main=paste("Results for

Full_Name: Juan Gea Version: R version 2.6.2 OS: Fedora Core 6 Submission from: (NULL) (79.153.48.49) Abort: objeS <- matrix("AAA",1000000) class(objeS) outTxt <- textConnection("vaClob", open = "w", local = FALSE) dput(objeS,outTxt) close(outTxt) R version 2.6.2 (2008-02-08) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN

If you found my subject heading to be confusing then I'm sure you'll enjoy the example I've included below. I find the apply type functions to be wonderful for avoiding loops but when I use them with existing functions, I end up using loops anyway to extract the vectors I want. I would appreciate it if someone could show me how to avoid these loops. Thanks. EXAMPLE:

Curious to know if this warning is expected behavior, and if so, what is the recommended way instead: > dput(letters, file = stderr()) c("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p",

Hello everyone, I'm trying to estimate the parameters of the returns series attached using the GARCH code below, but I get the following error message: Error in solve.default(Hessian) : system is computationally singular: reciprocal condition number = 0 Error in diag(solve(Hessian)) : error in evaluating the argument 'x' in selecting a method for function 'diag' Can

Hello all, I'm trying to create a heatmap using 2 matrices I have: z and v. Both matrices represent different correlations for the same independent variables. The problem I have is that I wish to have the values from matrix z to be represented by color intensity while having the values from matrix v to be represented by circle size. I currently have the following in front of me and an unsure

I'm running a huge number of regressions in a loop, so I tried lm.fit for a speedup. However, I would like to be able to calculate the t-stats for the coefficients. Does anyone have some functions for calculating the regression summary stats of an lm.fit object? Thanks, Whit

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

I define the following function to convert a t-value with degrees of freedom DF to another t-value with different degrees of freedom fullDF: tConvert <- function(tval, DF, fullDF) ifelse(DF>=1, qt(pt(tval, DF), fullDF), 0) It works as expected with the following case: > tConvert(c(2,3), c(10,12), 12) [1] 1.961905 3.000000 However, it gives me warning for the example below although the

I really can't figure out how to get and set array indices from the c interface. so to get an element I'm calling tindex = *fn\SymbolTable(*index\name) index = LLVMBuildLoad(builder,tindex,"index") arr = *fn\SymbolTable(*array\name) arrptr = LLVMBuildLoad(Builder,arr,"arrayptr") tmp = LLVMBuildGEP(Builder,arrptr,index,0,"ptr") ptr =

This patch introduces a handful of new options, I mentioned earlier in: http://www.mail-archive.com/nut-upsdev at lists.alioth.debian.org/msg02088.html See the large commit message in the follow-up for the details and rationale. I realize it's a bit larger diff - so if it's required I can split it into few smaller ones. Michal Soltys (1): APC smart driver update and new features.

I am attempting to lower the selectCC instruction to the instruction set of the backend I'm working on and I cannot seem to find a way to correctly implement this instruction. I know how this instruction should get implemented; I just have yet to find a way to do it. I want the select_cc instruction to be lowered into a comparison followed by a conditional move. I've attempted to use a

Sorry for a bit longer delay than I anticipated, I was stuffed with the work. This is the next iteration of the patch adding some functionality to apcsmart driver, and relying on 'ignorelb' recently added. Follow up from previous thread: http://www.mail-archive.com/nut-upsdev at lists.alioth.debian.org/msg02331.html Main differences is that V3 is split into many small patches, so the

This is 2nd version of the earlier patch featuring a few new features and fixes to the apcsmart driver, following the remarks in: http://www.mail-archive.com/nut-upsdev at lists.alioth.debian.org/msg02294.html Major changes from v1: - handle battery.charge and battery.runtime checks at main.c level - handle "immutable but writable" conflict gracefully at driver level -

When I do t.test on two distributions (see example below), it outputs numerous data about the t.test. What I'd like to do is individually capture some of this data and assign it to other variables. However, I am unable to find anything in the help section. In the example below, the t value is -4.0441 and the p-value is 0.006771 How can I assign these values to two variables, let's

I'm trying to cross-compile Samba 3.0.21a to run on mipsel (using uClibc). It fails at some point however, and I can't spot the problem: # ./configure --target=mipsel-linux --host=mipsel-linux --build=i386-pc-linux-gnu && make (...) Compiling lib/sendfile.c Compiling lib/time.c lib/time.c: In function `GetTimeOfDay': lib/time.c:66: error: too few arguments to function

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

Hello, I'm attempting to plot error bars side by side rather than stacked on top of each other with ggplot2. Here is the sample code I am using: #Code #Data spd<-c("s","f","f","s","f","s","s","s","f","f","s","f") r<-c(4.9,3.2,2.1,.2,3.8,6.4,7.5,1.7,3.4,4.1,2.2,5)

My apologies if you will receive this twice as I posted it to the RForum forum yesterday. My understanding is that it is a separate forum. Gently disabuse me of this notion if I am mistaken. [paste] Thanks for allowing me to post to your community board. First, let me just say that we are looking to hire someone who is interested in and has been delving into RoR. However, we are not looking

On Thu, Sep 11, 2008 at 6:14 PM, Villmow, Micah <Micah.Villmow at amd.com> wrote: > I am attempting to lower the selectCC instruction to the instruction set of > the backend I'm working on and I cannot seem to find a way to correctly > implement this instruction. I know how this instruction should get > implemented; I just have yet to find a way to do it. I want the select_cc