similar to: Help with card-sorting experiment

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']

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

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 <-

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

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 > >

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 <-

Hello, I am plotting data as a venn diagram but would like to be able to control how it is plotted like a lattice object. Right now, it plots right away. I would like to name it and then plot at will. I thought to convert the whole thing to a PostScript file then get it back into R via grImport, but surely (please!) there must be a less roundabout way to do this. Any help is appreciated. Here

I have encountered this problem on several occasions and am not sure how to handle it. I use for-loops to cycle through datasets. When each dataset is of equal length, it works fine as I can combine the datasets and have each loop pick up a different column, but when the datasets are differing lengths, I am struggling. Here is an example: A<-1:10 B<-1:15 C<-1:18

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

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

Hi all, I suspect the answer to this query will be the tongue-in-cheek "use a quantum computer", but I thought my understanding might be sufficiently limited that I'm missing a simpler option. I'm looking for a way to cycle through all possible combinations of 2 groups of data. For 10 data points, that's 2^10 combinations, for 20 data points it's 2^20, etc. combn() from

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

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

Dear Sirs I can't try the package sets because I have a old mac system. Package sets needs R = 2.6. I need to group some variables into a set of new variables defined by a particular modality: Example: my.df = data.frame(var1=c(1,2,2,1,2,2,1,2), var2=c(2,2,1,1,2,2,1,1), var3=c(1,2,2,1,1,1,1,2), var4=c(1,1,1,1,2,1,2,1)) I'm looking for a way to compute set1 and set2? set1 = c(5,4) %

Hello list, I wonder if anyone might be able to help me troubleshoot an attempt at porting some simple Python code to R. The function below is supposed to take a matrix containing item ratings from various users and, given a vector containing at least 1 rating and 1 missing value, employ a 'weighted slope one' algorithm to predict the missing values. The algorithm itself is fairly

I would like to create 15 box plots from two sets of data. Set1 - containts PayGrade, Min_Salary, Max_Salary data for 15 pay grades Set2 - contains PayGrade, Actual_Min, Actual_Max, and Actual_Mean for the 15 pay grades I would like 15 box plots (one for each paygrade) whose whiskers were the Min_Salary and Max_Salary data and whose ''box'' was Actual_Min, Actual_Mean,

Hi, I am trying to generate 100 different random walks each containing 500 variables. The script I am using to develop one time series: > x <- w <- rnorm(500) > for (t in 2:500) x[t] <- x[t - 1] + w[t] >From here I become stuck as I would like to generate a series and name it Set1 for the first set, then Set2 for the second....Set(n) I have tried rep() to generate the 100 time

Hi R-devel, I am facing a situation where the number of options I would like to propose to the user is somewhat big (and could easily increase more and more as I will code up a little more - even coming to a point where an user should be able to implement his own options). What we have to handle options is the couple: options(par=value) and getOption("par") I was aking myselft what

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