similar to: Summary grouped by factor

How to I "recode" a factor into a binary data frame according to the factor levels: ### example:start set.seed(20) l <- sample(rep.int(c("locA", "locB", "locC", "locD"), 100), 10, replace=T) # [1] "locD" "locD" "locD" "locD" "locB" "locA" "locA" "locA"

Hello, how can I return the name of a variable, say "a$b", from a function? fun <- function(x){ return(substitute(x)); } a <- data.frame(b=1:10); fun(a$b) ... returns a$b, but this is a type language, thus I can't use it as a character string, can I? How? Thanks for help, S?ren

Hi, y is nominal (3 categories), x1 to 3 is scale. What I want is a regression, showing the probability to fall in one of the three categories of y according to the x. How can I perform such a regression in R? Thanks for your help S?ren

Hello, after the creation of a data.frame I like to add NAs as follows: n <- 743; x <- runif(n, 1, 7); Y <- runif(n, 1, 7); Ag6 <- runif(n, 1, 7); df <- data.frame(x, Y, Ag6); # a list with positions: v <- apply(df, 2, function(x) sample(n, sample(1:ceiling(5*n/100), 1), repl=F)); # a loop too much? for (i in 1:length(df)){ df[unlist(v[i]), i] <- NA; } summary(df); This

I have created a function to do something: i <- factor(sample(c("A", "B", "C", NA), 793, rep=T, prob=c(8, 7, 5, 1))) k <- factor(sample(c("X", "Y", "Z", NA), 793, rep=T, prob=c(12, 7, 9, 1))) mytable <- function(x){ xtb <- x btx <- x # do more with x, not relevant here cat("The table has been created,

A Likert scale may have produced counts of answers per category. According to theory I may expect equality over the categories. A statistical test shall reveal the actual equality in my sample. When applying a chi square test with increasing number of repetitions (simulate.p.value) over a fixed sample, the p-value decreases dramatically (looks as if converge to zero). (1) Why? (2) (If

r11 -- r16 are variables showing a reason for usage of a product in 6 different situations. Each variable is a factor with 4 levels imported from a SPSS sav file with labels ranging from "not important" to "very important", and NA's for a sample of N = 276. (1) I need a chi square test of independence showing that the reason does not differ depending on the

Hi, I going through the alias analysis documentation (http://llvm.org/docs/AliasAnalysis.html) and noticed the following in the definition of PartialAlias: " The PartialAlias response is used when the two memory objects are known to be overlapping in some way, but *do not start at the same address*. " Is it really required that the objects do no start at the same address? if

A list contains several matrices. Over all matrices (list elements) I'd like to access one matrix cell: m <- matrix(1:9, nrow=3, dimnames=list(LETTERS[1:3], letters[1:3])) l <- list(m1=m, m2=m*2, m3=m*3) l[[3]] # works l[[3]][1:2, ] # works l[[1:3]][1, 1] # does not work How can I slice all C-c combinations in the list? S?ren -- S?ren Vogel, Dipl.-Psych. (Univ.), PhD-Student, Eawag,

On 07/14/2017 03:00 PM, Davide Italiano via llvm-dev wrote: > On Fri, Jul 14, 2017 at 12:50 PM, Nuno Lopes via llvm-dev > <llvm-dev at lists.llvm.org> wrote: >> Hi, >> >> I going through the alias analysis documentation >> (http://llvm.org/docs/AliasAnalysis.html) and noticed the following in the >> definition of PartialAlias: >> " >> The

Hello, I read the help as well as the examples, but I can not figure out why the following code does not produce the *given* row names, "x" and "y": x <- 1:20 y <- 21:40 rbind( x=cbind(N=length(x), M=mean(x), SD=sd(x)), y=cbind(N=length(y), M=mean(y), SD=sd(y)) ) Could you please help? Thank you S?ren

I have multiple Asterisk instances set up in different locations and would like to modify the callerID of inbound calls to identify which instance the call is coming from.? I knew how to do that with the old sip format, but can't seem to figure it out with PJSip. For example: Currently Location A, extension 10 calls Location B, extension 20.? CallerID on Extension 20 displays

A dataframe holds 3 vars, each checked true or false (1, 0). Another var holds the grouping, r and s: ### start:example set.seed(20) d <- data.frame(sample(c(0, 1), 20, replace=T), sample(c(0, 1), 20, replace=T), sample(c(0, 1), 20, replace=T)) names(d) <- c("A", "B", "C") e <- rep(c("r", "s"), 10) ### end:example How do I get the

On Sun, Jul 16, 2017, 12:45 PM Nuno Lopes <nunoplopes at sapo.pt> wrote: > >On 07/15/2017 04:51 AM, Nuno Lopes wrote: > >>> On 07/14/2017 04:37 PM, Nuno Lopes wrote: > >>>> Thank you all for your replies. > >>>> So here seems to be an agreement that the documentation for > >>>> PartialAlias is incorrect. > >>>>

Hello, r-help at r-project.orgbarplot(twcons.area, beside=T, col=c("green4", "blue", "red3", "gray"), xlab="estate", ylab="number of persons", ylim=c(0, 110), legend.text=c("treated", "mix", "untreated", "NA")) produces a barplot very fine. In addition, I'd like to get the

Thank you all for your replies. So here seems to be an agreement that the documentation for PartialAlias is incorrect. Daniel: now you got me wondering about MustAlias. This is what the docs say: "The MustAlias response may only be returned if the two memory objects are *guaranteed to always start at exactly the same location*" This statement is regardless of the access sizes. For

On Sat, Jul 15, 2017 at 5:35 AM, Hal Finkel <hfinkel at anl.gov> wrote: > > On 07/15/2017 04:51 AM, Nuno Lopes wrote: > >> On 07/14/2017 04:37 PM, Nuno Lopes wrote: >>> >>>> Thank you all for your replies. >>>> So here seems to be an agreement that the documentation for >>>> PartialAlias is incorrect. >>>>

> On 07/14/2017 04:37 PM, Nuno Lopes wrote: >> Thank you all for your replies. >> So here seems to be an agreement that the documentation for PartialAlias >> is incorrect. >> >> Daniel: now you got me wondering about MustAlias. This is what the docs >> say: >> "The MustAlias response may only be returned if the two memory objects >> are

Hello LLVMdev, I’m using LLVM to do static analysis exclusively (without any code generation). To implement this analysis, I’m using multiple address spaces to disambiguate the purpose of the pointed memory. Since address spaces never alias in my model, I set on to implement an alias analysis pass that would exactly provide this information, as I’m seeing a couple of otherwise dead store that

On Sun, Jul 16, 2017 at 2:34 PM, Nuno Lopes <nunoplopes at sapo.pt> wrote: > On Sun, Jul 16, 2017, 12:45 PM Nuno Lopes wrote: >>> >>>> On 07/15/2017 04:51 AM, Nuno Lopes wrote: >>>> >>>>> On 07/14/2017 04:37 PM, Nuno Lopes wrote: >>>>>> >>>>>>> Thank you all for your replies.