similar to: reshaping some data

Dear R-lister I have a question about how to create multiple error bar plots. I found that I can use an "errbar" function from Hmisc package to create one error bar plot in which there are multiple data points (x, y) with the error bars. Thus, I know that I can get "one" error bar plot which consists of many data points. However, I did not find a way to put

After searching in R- Introduction, FAQ, help... I don't understand this: I write a function in a file (.R): tt <- function(mc) { date() mc<-read.csv2("machines.txt",na.strings="") date() } I source it in R and I type tt(). The answer is > tt() [1] "Mon Aug 14 11:18:25 2000" > The instructions following the first "date()" are ignored. Why?

II am translating some gauss code into R, and gauss has a matrix product function called the horizontal direct product (*~), which is some sort of variant on the Kronecker product. For example if x is 2x2 and y is 2x2 the horizontal direct product, z, of x and y is defined (in the Gauss manual) as: row 1 = x11*y11 x11*y12 x12*y11 x12*y12 row 2 = x21*y21 x21*y22 x22*y21 x22*y22 Or in R

Hi, We got a question about interpretating R-suqared. The actual outputs for a test dataset is X=(x1,x2, ..., xn). model 1 predicted the outputs as Y1=(y11,y12,..., y1n) model n predicted the outputs as Y2=(y21,y22,..., y2n) ... model m predicted the outputs as Ym=(ym1,ym2,..., ymn) Now we have two ways to calculate R squared to evaluate the average performance of committee model. (a)

Example: define i8 @narrow_add(i8 %x, i8 %y) { %x32 = zext i8 %x to i32 %y32 = zext i8 %y to i32 %add = add nsw i32 %x32, %y32 %tr = trunc i32 %add to i8 ret i8 %tr } With no data-layout or with an x86 target where 8-bit integer is in the data-layout, we reduce to: $ ./opt -instcombine narrowadd.ll -S define i8 @narrow_add(i8 %x, i8 %y) { %add = add i8 %x, %y ret i8 %add } But on

Hello! let: test<-1:3 list(test) names(test)<-c("X11","X12","Y23") >test[["Y2"]] 3 I had assumed that the names in a list are like a keys in a hash. Therefore i thought that no value should be returned. The behavior of: >test["Y2"] <NA> NA is as i expected. Should it be as it is? How is the definition of [[]] and []?

Dear R-developers, I would like to suggest a 'method' slot for format.ftable() (see an adjusted 'format.ftable()' below, taken from the source of R-2.15.2). At the moment, format.ftable() contains several empty cells due to the way the row and column labels are printed. This creates problems (= unwanted empty columns/rows) when converting an ftable to a LaTeX table; see an

Thanks for the perf testing. I assume that DAG legalization is equipped to handle these cases fairly well, or someone would've complained by now... FWIW (and at least some of this can be blamed on me), instcombine already does the narrowing transforms without checking shouldChangeType() for binops like and/or/xor/udiv. The justification was that narrower ops are always better for

Hello Thanks for looking into this. I can't be very confident what the knock on result of a change like that would be, especially on architectures that are not Arm. What I can do though, is run some benchmarks and look at that results. Using this patch: --- a/lib/Transforms/InstCombine/InstructionCombining.cpp +++ b/lib/Transforms/InstCombine/InstructionCombining.cpp @@ -150,6 +150,9 @@

Heya folks First of all, sorry if this isn't the correct list, but yet, I think spam is a kind of network attack and should be treated as a security issue.. I run a working mail server using Postfix, MySQL, Courier-IMAP, SpamAssassin and ClamAV (amavisd-new) .. I've checked the configuration file for SpamAssassin, but yet I havn't find any good solution for spam.. Sure, spam will

Hello everyone, I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y. Lets assume a simple data.frame: Imputed = data.frame( X1 = c(1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2), X2 =

This is one of those instances where a less superficial knowledge of R's technical details comes in really handy. What you need to do is convert the data frame to a single (numeric) vector for, e.g. a matrix() call. This can be easily done by noting that a data frame is also a list and using do.call(): ## imp is the data frame: do.call(c,imp) X11 X12 X13 X14 X15 X16 X17 X18 X19

Hello everyone, Thank you for this. Nonetheless it is not exactly want i need. I need mydata[[1]] to provide the values for all 3 variables (Y, X1 and X2) of the first imputation only. As it stands it returns the whole database. Any ideas? Best, ioanna ________________________________ From: Bert Gunter <bgunter.4567 at gmail.com> Sent: 24 May 2018 16:04 To: Ioanna Ioannou Cc:

Do I need to use winbind between pdc and bdc if I'm using LDAP backend? I have a PDC setup with LDAP master on backend and a BDC with slave LDAP in another building.I'm just having a problem with getting users to authenticate and use the BDC for home directories and exactly how to have W2k clients join the domain.. The BDC is in another building (connected by a slow connection) so it needs

Dear R developers, I noticed a bug in the stats4 package, specifically in the confint method applied to ?mle? objects. In particular, when some ?fixed? parameters define the log likelihood, these parameters are stored within the mle object but they are not used by the ?confint" method, which retrieves their value from the global environment (whenever they still exist). Sample code: >

Hi all, at first glance, samba3.20.pre2 seemed to crash at startup, so I posted a bug-report at bugzilla (https://bugzilla.samba.org/show_bug.cgi?id=2916). After I installed this version in a seperate directory, so it wouldn't conflict with any existing *.tdb, but still use the same ldap-directory and the same smb.conf, it now starts. I assume that I have some .tdb-file or something alike,

Dear experts, I have in all 14 matrices which stands for gene expression divergence and 14 matrices which stands for gene sequence divergence. I have tried joining them by using the concatanation function giving SequenceDivergence <- c(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14) ExpressionDivergence <- c(Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8,Y9,Y10,Y11,Y12,Y13,Y14) where X1,X2..X14 are the

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Hi All, I am making a serious effort to try to learn R, but one hurdle I am facing is that I need to "see" the data as I walk through the examples in the packages. For instance, many examples on the web start by a command like data("wines"). How can I actually view what the dataset looks like prior to transformations and analysis? I have tried to use edit() , print, and

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