Transforms a table with prevalences by age brackets into a table with (smoothed) prevalences by age

This function transforms a table with prevalences by age brackets and by (optional) sex, year and categories into a table with smoothed prevalences at all ages (for all sex, year and categories).

ApproxPrevalenceTable(
  tab,
  agecuts,
  agemin,
  agemax,
  weights.tab = NULL,
  categories = c("categ"),
  option = ""
)

Arguments

tab	a dataframe containing categorisation variables (year, sex, categ) and prevalences by age bracket
agecuts	a vector with age defining the age brackets (minimum age in each age bracket)
agemin	minimum age in the output table
agemax	maximum age in the output table
weights.tab	a dataframe with weights (ie population) by age and some of the categorisation variables of the 'tab' dataframe
categories	a vector of names of variables of the 'tab' dataframe, representing categories
option	maximum age in the output table

Value

a table containing the same categorisation variables than the input table, plus prevalences by age

Details

Parameters must include the vector of age cuts, which defines the age brackets. For instance, c(60,70,80) defines age brackets [60,70), [70,80) and [80,Inf].

The calculation minimises the sum of squares of second-differences of prevalences by age, under the constraint that average prevalences by age brackets (weighted according to the 'weight' vector, usually the vector of population size at each age) are equal to the 'prevalence' input vector. Alternatively, if 'option' is set to 'polynomial', polynomial approximation is calculated. The degree of the polynomial function can be constrained by setting option to 'polynomialD' (with D = degree, between 1 and 4) ; default value is 4.

In the output table, 'prevalence.raw' corresponds to input values of prevalences (constant by age bracket) while 'prevalence.approx' corresponds to smoothed values (different value at every age).