How to store mean vectors and covariance matrices in cells of a data table?

user_16515478 0
Viewed 70

Consider a data table with two numeric and one categorical feature. I would like to convert this data table to a new data table. Each row of this data table should correspond to one value of the categorical feature. Furthermore, it should contain a column with the mean vectors that result from the numeric features for each categorical value plus a column with the covariance matrices. Additionally, one can only use an object that contains the names of the columns that refer to the numeric features.

It seems one has to use lists for this, see for example R - store a matrix into a single dataframe cell. However, this information does not help me enough.

Here is an example:

library(data.table)

set.seed(42)
a <- sample(1:3, 10, TRUE)
b <- rnorm(10)
d <- rpois(10, 3)
data <- data.table(a, b, d)
bd <- c("b", "d")

dat <- data[, 
            .(mu = mean(get(bd)), 
              sigma = get(cov(bd))), 
            by = a]

What I want is that dat has three rows, each corresponding to one value in a. This data table should also contain a column with three vectors of length 2 and a column with three 2x2 matrices.

data.table r
1 Answers

We can use mget instead of get as get is for returning a single object value and mget for one or more

data[, lapply(mget(bd), function(x) mean(x)), by = a]

If we need a list column

data[, .(mu = .(as.list(lapply(mget(bd), function(x) mean(x))))), by = a]

IF we want both columns i.e. cov as well

data[, .(mu = .(sapply(mget(bd), function(x) mean(x))), 
       sigma = .(cov(do.call(cbind, mget(bd)))[2])), by = a]
   a                  mu     sigma
1: 1 0.2353046,2.2000000 -2.131663
2: 2 0.1876238,3.3333333  2.062627
3: 3 0.9299794,1.5000000 0.1445644
user_37322710
Related