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 with columns lower.age.limit and population (e.g. from wpp_age())

...

passed to pop_age() for interpolation

Value

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

Examples

data(polymod)
pop <- data.frame(
  lower.age.limit = c(0, 5, 15),
  population = c(3500000, 6000000, 50000000)
)
polymod |>
  (\(s) s[country == "United Kingdom"])() |>
  assign_age_groups(age_limits = c(0, 5, 15)) |>
  compute_matrix() |>
  split_matrix(survey_pop = pop)
#> 
#> ── Contact matrix (3 age groups) ──
#> 
#> Ages: "[0,5)", "[5,15)", and "[15,Inf)"
#> Participants: 1011
#> Mean contacts: 11.48
#> 
#>           contact.age.group
#> age.group      [0,5)   [5,15)  [15,Inf)
#>   [0,5)    3.6702254 1.599842 0.7411032
#>   [5,15)   0.6126126 5.363669 0.5034768
#>   [15,Inf) 0.5886896 1.135204 1.0125673