prevalenceApprox.RdGiven a vector of prevalences by age brackets and the vector of age cuts (which defines the age brackets), the function returns a vector of prevalences at all ages. The calculation minimises the sum of squares of second-differences of prevalences by age, under the constraint that average prevalences by age brackets (weight according to the 'weight' vector, usually the vector of population size at each age) are equal to the 'prevalence' input vector.
prevalenceApprox( prevalence, agecuts, agemin, agemax, weight = rep(1, (agemax - agemin + 1)) )
| prevalence | a vector with observed 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 vector |
| agemax | maximum age in the output vector |
| weight | a vector of weights for each age |
a vector with prevalences according to polynomial approximation
Note : Second-differences rather than first-differences are used in the minimisation function, since prevalences according to age are usually parabolic.
prevalenceApprox(prevalence = (FRDreesAPA2017 %>% filter(sex=="female",typepresta=="APA Ã domicile"))$prevalence, agecuts=c(seq(60,95,5)), agemin=60, agemax=100, weight=(FRInseePopulation %>% filter(sex=="female",year==2018,age0101>=60) %>% arrange(age0101))$popx)#> Error in FRInseePopulation %>% filter(sex == "female", year == 2018, age0101 >= 60) %>% arrange(age0101): impossible de trouver la fonction "%>%"prevalenceApprox(prevalence = (FRDreesAPA %>% filter(year==2018,sex=="male",typepresta=="APA Ã domicile"))$prevalence, agecuts=c(seq(60,90,5)), agemin=60, agemax=100, weight=(FRInseePopulation %>% filter(sex=="male",year==2019,age0101>=60) %>% arrange(age0101))$popx)#> Error in FRInseePopulation %>% filter(sex == "male", year == 2019, age0101 >= 60) %>% arrange(age0101): impossible de trouver la fonction "%>%"