Decompose a contact matrix into mean contacts, normalisation and assortativity

Splits the contact matrix into the mean number of contacts across the whole population (mean.contacts), a normalisation constant (normalisation), age-specific contact rates (contacts), and an assortativity matrix (replacing $matrix). For details, see the "Getting Started" vignette.

Usage

split_matrix(x, survey_pop)

Arguments

x

a list as returned by compute_matrix(), with elements matrix and participants

survey_pop

a data frame; see Population data below

Value

x with $matrix replaced by the assortativity matrix, plus additional elements $mean.contacts, $normalisation, and $contacts

Details

split_matrix() supports single-grouping (rank-2) matrices only.

Population data

survey_pop is a data frame with one column per grouping, named after the grouping (e.g. age, gender) and holding that grouping's levels as they appear in the matrix, plus a population column with the size of each combination. One row per combination of levels is required, and levels are matched to the matrix exactly, without interpolation.

Use align_ages() to build this from a raw population table: it aggregates each grouping to the matrix's levels (interpolating the age grouping where needed) and labels the columns to match.

Examples

data(polymod)
result <- polymod |>
  (\(s) s[country == "United Kingdom"])() |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix()
uk_pop <- data.frame(
  age = limits_to_agegroups(0:80, notation = "brackets"),
  population = rep(1e5, 81)
)
result |> split_matrix(survey_pop = align_ages(uk_pop, result))
#> 
#> ── Contact matrix (3 age groups) ──
#> 
#> Ages: "[0,5)", "[5,15)", and "[15,Inf)"
#> Participants: 1011
#> Mean contacts: 11.55
#> 
#>           contact.age.group
#> age.group      [0,5)    [5,15)  [15,Inf)
#>   [0,5)    3.4975089 1.3067616 0.7643158
#>   [5,15)   0.5837838 4.3810811 0.5192465
#>   [15,Inf) 0.5609865 0.9272422 1.0442825