Summarize a Multidesign Object by Design Variables

Computes summaries of the data matrix grouped by combinations of design variables.

Usage

# S3 method for class 'multidesign'
summarize_by(x, ..., sfun = colMeans, extract_data = FALSE, aggregate = NULL)

Arguments

x

A multidesign object

...

Unquoted names of variables to group by

sfun

Summary function to apply (default is colMeans)

extract_data

Logical; whether to extract raw data instead of computing summary

aggregate

Optional explicit cellwise aggregation rule. Required when `x` has a cell mask; accepts `"mean"` or a scalar-returning function.

Value

A new multidesign object containing:

x

Matrix of summary statistics

design

Design information for each summary

column_design

Original column metadata

See also

split.multidesign

Other multidesign functions: multidesign(), reduce.multidesign(), split.multidesign(), split_indices.multidesign(), subset.multidesign()

Examples

X <- matrix(rnorm(100*20), 100, 20)
Y <- tibble::tibble(
  condition = rep(c("A", "B"), each=50),
  block = rep(1:2, times=50)
)
mds <- multidesign(X, Y)

# Get means by condition
means_by_cond <- summarize_by(mds, condition)

# Get means by condition and block
means_by_both <- summarize_by(mds, condition, block)

# Masked summaries require an explicit coordinate-wise rule
mask <- matrix(TRUE, nrow(X), ncol(X))
mask[1:10, 1] <- FALSE
masked <- multidesign(X, Y, cells = mask)
masked_means <- summarize_by(masked, condition, aggregate = "mean")
cell_mask(masked_means)
#>         [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> group 1 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE  TRUE  TRUE  TRUE  TRUE
#> group 2 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE  TRUE  TRUE  TRUE  TRUE
#>         [,14] [,15] [,16] [,17] [,18] [,19] [,20]
#> group 1  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#> group 2  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE