search for: grand_row

...the power and speed of subsetting to the fullest extent, in most cases only making a single copy of the data. * cast is replaced by two functions depending on the output type: `dcast` produces data frames, and `acast` produces matrices/arrays. * multidimensional margins are now possible: `grand_row` and `grand_col` have been dropped: now the name of the margin refers to the variable that has its value set to (all). * some features have been removed such as the `|` cast operator, and the ability to return multiple values from an aggregation function. I'm reasonably sure both...

...the power and speed of subsetting to the fullest extent, in most cases only making a single copy of the data. * cast is replaced by two functions depending on the output type: `dcast` produces data frames, and `acast` produces matrices/arrays. * multidimensional margins are now possible: `grand_row` and `grand_col` have been dropped: now the name of the margin refers to the variable that has its value set to (all). * some features have been removed such as the `|` cast operator, and the ability to return multiple values from an aggregation function. I'm reasonably sure both...

...the power and speed of subsetting to the fullest extent, in most cases only making a single copy of the data. * cast is replaced by two functions depending on the output type: `dcast` produces data frames, and `acast` produces matrices/arrays. * multidimensional margins are now possible: `grand_row` and `grand_col` have been dropped: now the name of the margin refers to the variable that has its value set to (all). * some features have been removed such as the `|` cast operator, and the ability to return multiple values from an aggregation function. I'm reasonably sure both...

...the power and speed of subsetting to the fullest extent, in most cases only making a single copy of the data. * cast is replaced by two functions depending on the output type: `dcast` produces data frames, and `acast` produces matrices/arrays. * multidimensional margins are now possible: `grand_row` and `grand_col` have been dropped: now the name of the margin refers to the variable that has its value set to (all). * some features have been removed such as the `|` cast operator, and the ability to return multiple values from an aggregation function. I'm reasonably sure both...

according to the documentation of the cast function in the reshape function, I would expect this bit of code from the examples to calculate marginal means over only the 'diet' variable. #Chick weight example names(ChickWeight) <- tolower(names(ChickWeight)) chick_m <- melt(ChickWeight, id=2:4, na.rm=TRUE) cast(chick_m, diet + chick ~ time, mean, margins="diet") But,

Hi, I'd like to get mean values for the margins of my casted data.frame. For the casting I am using dcast() from reshape2. However, when I set the margins parameter (margins=c("grand\_row")) I get following error concerning an unrecognized escape character '\_'. So what is the correct command to get the outermost margins only in reshape2? /johannes [[alternative HTML

Suppose I have data like this: A <- sample(letters[1:3], 1000, replace=TRUE) B <- sample(LETTERS[1:2], 1000, replace=TRUE) x <- rnorm(1000) I can get a table of means via tapply(x, list(A, B), mean) and I can add the marginal means to this using cbind/rbind: main <- tapply(x, list(A,B), mean) Amargin <- tapply(x, list(A), mean) Bmargin <- tapply(x, list(B), mean)

Dear list. New to R, I'm looking for a way of using crosstab to output low-dimensional (higher than 2) contingency tables (frequencies, per-cents by rows, % by columns, mean, quantiles....) I'm looking for something of the following sort dataframe: singers, categorical variates: voice category (soprano,mezzo-soprano, ...) , voice type( drammatic, spinto, lirico-spinto, lirico,