similar to: For loop to cycle through datasets of differing lengths

Dear R users, I am working on allocating the rows within a dataframe into some factor levels.Consider the following dataframe: Start.action Start.time 1 Start.setting 2010-12-30 17:58:00 2 Start.setting 2010-12-30 18:40:00 3 Start.setting 2010-12-31 22:39:00 4 Start.setting 2010-12-31 23:24:00 5

Hi, This question should be simple to answer. I am a new R user. I have a data.frame called appended. I would like to break it into 7 smaller datasets based on the value of a categorical variable dp (which has values 1:7). I would like to name the smaller datasets set1, set2, set3,....,set7. I don't know how to refer to the variable in the for loop, when naming the output datasets. In STATA

In a code segment of my pass plugin, I try to gather AliasSets for all StoreInst, LoadInst and CallInst instructions in a function. Some behaviors of the pass puzzled me. Below is the *.ll of the test program which I run the pass on, it was get with "llvm-gcc -Wl,--disable-opt" from a rather simple *.c program. ---------------------------------- ; ModuleID = 'ptralias.bc'

Good evening I often have as output from simulations a list of various values, vectors and matrices. Supposing that I then run said simulation several times, I often want to extract a particular result from each simulation for plotting and, ideally, put it in a matrix. A simple example v1 <- 1:5 v2 <- 6:10 other1 <- "stuff" other2 <- "stuff" set1 <-

Hi Dovid Yes was one of the options but as the required list is dynamic becomes very messy - and all combinations problem - where as "call all workers job xxx" is what is needed so the ability to call 20+ numbers is what is needed - agi does a database search for all jobx workers and constructs a dialstring with SIP, DAHDI and Local devices. Can someone tell me where to find maximum

Dear all, I have two sets of data (say set1 and set2) as follow: set1 x1 x2 x3 0.30 0.43 3.88 0.38 0.59 3.53 0.30 0.42 2.12 0.33 0.53 2.12 0.30 0.47 3.76 set2 y1 y2 y3 0.32 0.47 5.18 0.23 0.26 1.06 0.42 0.65 3.88 0.28 0.38 3.76 0.35 0.47 1.41 The "pairs" function (such as pairs(~x1+x2+x3 data=set1, main="Simple Scatterplot Matrix") ) is

Hello! I need to compare 2 datasets whether they come from the same distribution. I use function ks.boot{Matching}. And what is the confidence level of the p-value, returned by ks.boot function? The code is: set=read.table("http://stella.sai.msu.ru:8080/~gala/data/testsets.csv", header=T,sep=',') set1=set[!is.na(set$set1),'set1']

Colleagues, (R: 2.13.0; OS X) I often receive sequential datasets in which there are new rows interposed between existing rows. For example: SET1 <- data.frame(list(LETTERS=LETTERS[c(1:4, 6:10)], NUMBERS=c(1:4, 6:10))) SET2 <- data.frame(list(LETTERS=LETTERS[1:10], NUMBERS=1:10)) > SET1 LETTERS NUMBERS 1 A 1 2 B 2 3 C 3 4 D 4 5

Oh, I appologize that I should not have asked about __main() ---- it appears in FAQ. But the question remains that why call to __main() can alias stack location? I think the memory location pointed by data_X pointers are not visible to __main(). In comparison, calls to printf() do not have similar effect. On 5/14/06, Nai Xia <nelson.xia at gmail.com> wrote: > > In a code segment of

Dear friends, I have a data set with 23 columns and 38000 rows. It is a panel running from the years 1991 through 2005. I want to aggregate the data and get the medians of each of the 23 columns for each of the years. In other words my output should be like this Year Median 1991 123 1992 145 1993 132 etc. The sample lines of code to do this operation is set1 <-

In http://lists.llvm.org/pipermail/cfe-commits/Week-of-Mon-20120827/063200.html, consecutive bitfields are wrapped as a group and represented as a large integer and emits loads stores and bit operations appropriate for extracting bits from within it. It fixes the problem of violating C++11 memory model that original widen load/store of bitfield was facing. It also brings more coalescing

On 03/09/2017 12:28 PM, Krzysztof Parzyszek via llvm-dev wrote: > We could add intrinsics to extract/insert a bitfield, which would > simplify a lot of that bitwise logic. But then you need to teach a bunch of places about how to simply them, fold using bitwise logic and other things that reduce demanded bits into them, etc. This seems like a difficult tradeoff. -Hal > >

On Thu, Mar 9, 2017 at 10:54 AM, Hal Finkel <hfinkel at anl.gov> wrote: > On 03/09/2017 12:14 PM, Wei Mi via llvm-dev wrote: >> >> In >> http://lists.llvm.org/pipermail/cfe-commits/Week-of-Mon-20120827/063200.html, >> consecutive bitfields are wrapped as a group and represented as a >> large integer and emits loads stores and bit operations appropriate

Hi all, Suppose that I've two data frames, a and b say, both containing a column 'id'. While data frame 'a' contains multiple rows sharing the same id, data frame 'b' contains just one entry per id (i.e. a 1 to n relationship). For the ease of modeling I now want to generate a new data frame c, which is basically a copy of data frame 'a' augmented by the values

Hello, I've been clustering my data using hclust and cutting the resulting tree with cutree. Separately, I visualize the clusterings with heatmap. Is it possible to have the dendrogram on the heatmap reflect the cutree results? That is, instead of having one large dendrogram, it would have 4 or 25 in the example below. Any guidance on if that's possible or not, and what kinds of

Hi Chris, I took a haste look at the "Points-to Analysis in Almost Linear Time" by Steens , your PHD thesis and SteensGaard.cpp in LLVM this afternoon. So I think: 1. Actually the basic algorithm described originally by SteensGaard does not provide MOD/REF information for functions. 2. The context insensitive part of Data Structure Analysis (LocalAnalysis) can be deemed as an

Dear List I am trying to fit a multinomial model using the mlogit package. Attempting to load the data into mlogit presents the following error. MLOG<-mlogit.data(Mult3,shape="long",choice="CHOICE",alt.var="mode.ids",indivs = "set3",chid.var = "obs") Error in `row.names<-.data.frame`(`*tmp*`, value = c("1.1", "1.2",

On Tuesday 16 May 2006 03:19, Chris Lattner wrote: > On Mon, 15 May 2006, Nai Xia wrote: > > > In other words, if I only use -steens-aa and the data_XXXs are all > > external global variables( and so inComplete ), > > Sounds right! > > > the call to printf will > > make the same effect, which I have tested it. > > > > Am I right ? :) >

On Wed, 17 May 2006, Nai Xia wrote: > Unfortunately, I did not locate the lines in steens-aa for "printf" special case. > In ds-aa, I found the lines below: Right, steens-aa and ds-aa share code for "local analysis", they just stitch it together into an interprocedural analysis in different ways. The code below is used for steens-aa. >

On Mon, 15 May 2006, Nai Xia wrote: > In other words, if I only use -steens-aa and the data_XXXs are all > external global variables( and so inComplete ), Sounds right! > the call to printf will > make the same effect, which I have tested it. > > Am I right ? :) If you've tested it then, yes you're right :). I haven't played with this stuff for a long time,