similar to: computing functions with Euler's number (e^n)

Hi: I've been reading "Computational Methods for Multilevel Modeling" by Pinheiro and Bates, the idea of embedding the technique in my own c-level code. The basic idea is to rewrite the joint density in a form to mimic a single least squares problem conditional upon the variance parameters. The paper is fairly clear except that some important level of detail is missing. For

Hi all, I am trying to concatenate words together to create new column names, using a loop. Please consider the following toy example: x <- matrix(nrow = 1, ncol = 3) colnames(x) <- c("a", "b", "c") x[1,1] <- "1" x[1,2] <- "2" x[1,3] <- "3" I would like to create a new matrix with column names based on the column names

Hi, I don't know what I am doing wrong to the toupper does not seem working in sub + regex. The following returns 's' not the upper class 'S' as I expect: sub("q_([a-z])[a-zA-Z]*",toupper('\\1'),"q_sviRaw") Can someone tell me where I did wrong? Thanks, Richard [[alternative HTML version deleted]]

Hi all, I wanto to run a plot about the levels of a variable parting on an ols regression. The regression in done on the rate of return of the variable. Imagine R_{t}=a+b*R_{t-1} So If P, the "estimated" price would be P_{t}=P_{t-1}*R_{t} Imagine that I obtain lm fitted values and the original R_{t}, R_{t-1} are [1000,1] dimmension... How it is posible to obtain also 1000,1 fitted

i have loop index I. I want to increment it by 1. How to do this in registerinfo.td. I m trying following but getting error. let SubRegIndices = [sub_32bit, sub_32bit_hi], CoveredBySubRegs = 1 in { foreach I = 0-255 in { m=add(I,1); def R_#J#_REG64b_#I : R_P<"R_"#J#"_R64b_"#I, !shl(I,2), [!cast<R_P>("R_"#J#"_REG32b_"#I),

Sipura has implemented auto-answer in version 0.9.5 of the SPA-841 firmware. However, it is implemented via the Call-Info header, which Asterisk stable doesn't currently support. The attached patch implments a quick hack to support the Call-Info header from the Dial() application by way of setting the CALL_INFO variable. For example, the following macro can be used to dial up a single

In base R, there are lots of environment variables with either prefix 'R_' or '_R_', e.g. R_ENABLE_JIT and _R_RNG_VERSION_. I always considered R_* variables to be "public" and _R_*_ ones being "internal" but realized I don't have a reference for this. Is this true, or is there another reason? Is the difference between the two kinds documented anywhere?

I have a long equation that I need to break in the R documentation of a package or it trails off the right hand side of the page. Here's the formula: \deqn{Cov(r_{ist}, r_{iuv})= [.5\rho_{ist}\rho_{iuv}(\rho_{isu}^2 + \rho_{isv}^2 + \rho_{itu}^2 + \rho_{itv}^2) + \rho_{isu}\rho_{itv}+ \rho_{isv}\rho_{itu}-(\rho_{ist}\rho_{isu}\rho_{isv} + \rho_{its}\rho_{itu}\rho_{itv}) +

I noticed that in https://svn.r-project.org/R/trunk/src/library/stats/R/loess.R that we are now calling R_loess_raw, R_lowesw, R_lowesp, etc. I'm interested to know what is the benefit/reason for the 'R_', as I am unfamiliar with this prefix and do not see it mentioned in 'Writing R Extensions.' Thanks, Ben

On Sun, Jul 31, 2011 at 3:04 PM, Jianzhou Zhao <jianzhou at seas.upenn.edu> wrote: > The current memory model section ends with the following discussions: > > "Note that in cases where none of the atomic intrinsics are used, this > model places only one restriction on IR transformations on top of what > is required for single-threaded execution: introducing a store to a

The current memory model section ends with the following discussions: "Note that in cases where none of the atomic intrinsics are used, this model places only one restriction on IR transformations on top of what is required for single-threaded execution: introducing a store to a byte which might not otherwise be stored to can introduce undefined behavior.... " Why is introducing

On Sun, Jul 31, 2011 at 7:11 PM, Eli Friedman <eli.friedman at gmail.com> wrote: > On Sun, Jul 31, 2011 at 3:04 PM, Jianzhou Zhao <jianzhou at seas.upenn.edu> wrote: >> The current memory model section ends with the following discussions: >> >> "Note that in cases where none of the atomic intrinsics are used, this >> model places only one restriction on

Hello, I'm trying to fit a special kind of proportional odds model from: Whitehead et al. (2001). Meta-analysis of ordinal outcome using individual patient data. Statistics in medicine 20: 2243-2260. (model 2) The data are as follows: library(nlme) library(geepack) library(Design) library(MASS) options(contrasts=c("contr.SAS","contr.poly")) counts <-

[mod: Forwarded by Jeff Uphoff. I tried to mangle the headers that it appears as the original post: with an invalid return address. -- REW] A few months back, a very alpha version of bliss got posted. That shouldn''t have happened, but, it was pretty much ignored so I didn''t worry about it. But now it seems there''s a bit of a fuss about this. I''ll post the

Hi: I've been reading "Computational Methods for Multilevel Modeling" by Pinheiro and Bates, the idea of embedding the technique in my own c-level code. The basic idea is to rewrite the joint density in a form to mimic a single least squares problem conditional upon the variance parameters. The paper is fairly clear except that some important level of detail is missing. For

This came up on r-help but indicates a bug. dnbinom(x,n,p) calls dbinom_raw(n-1,...) which returns 0 for n<1. -thomas ---------- Forwarded message ---------- Date: Thu, 08 Feb 2001 17:10:23 +0000 From: Yudi Pawitan <yudi@stat.ucc.ie> To: Mark Myatt <mark@myatt.demon.co.uk> Cc: R-Help <r-help@stat.math.ethz.ch> Subject: Re: [R] Goodness of fit to Poisson / NegBinomial

for a silly question, wondering how to test fit with the one sample as follow. I have read _fitting distributions with R_, but that doesn't answer my specific question. inclined to use Kolmogorov-Smirnov D, and its associative p value. much appreciation! X20.001 232 93 84 185 336 417 228 199 2110 1411 612 1913 1314 3015

Dear all, I have a somewhat unusual linear mixed model that I can't seem to code in lme. It's only unusual in that one random effect is applied only to some of the observations (I have an indicator variable that specifies which observations have this random effect). The model is: X_hijk = alpha_h + h * b_i + r_(ij) + e_hijk , where h = 0 or 1 (indicator) i = 1, ..., N j = 1,

I use Samba 3.0.23d at FreeBSD 4.11-RELEASE-p25 as PDC and sometimes Microsoft Workgroup Add-On for MS-DOS and Mail as client. At FreeBSD box I have directory /common/install/postgreSQL and symlink to it: /usr/export/XP/i386/\$oem\$/install/postgreSQL -> /common/install/postgreSQL The path /usr/export/XP shared for MS Network as XP and mount via net use X: \\my_server\XP. So files and

Do your 32768 registers also have sub registers? I can't tell you exactly what to change. I'm not familiar with the code. I would just be running grep or something. ~Craig On Fri, Jul 14, 2017 at 10:23 AM, hameeza ahmed <hahmed2305 at gmail.com> wrote: > Thank you so much. I think there is no issue with my definitions since i > have to use larger registers i.e 65536 bit