This can be used as an external check of population counts
in older ages, assuming the stable population standard is representative enough, or it can be used to redistribute population in ages above a
Redistribute_from. This is handy, for instance, for
ensuring all censuses extend to a specified maximum age (e.g. 100+)
prior to intercensal interpolations. The assumption is that, at least in
Age_fit and higher ages, the population should follow
a stable pattern proportional to a given survival curve subject to
OPAG( Pop, Age_Pop, nLx, Age_nLx, Age_fit = NULL, AgeInt_fit = NULL, Redistribute_from = max(Age_Pop), OAnew = max(Age_nLx), method = "mono" )
numeric vector of population counts
integer vector of the lower bounds of the population age groups
numeric vector of stationary population age structure in arbitrary integer age groups
integer vector of lower bounds of age groups of
integer vector of lower bounds for age groups of
integer vector of widths of age groups of
integer lower age bound that forms the cutoff, above which we redistribute counts using the stable standard.
integer. Desired open age group in the output (must being element of
character, graduation method used for intermediate graduation. Default
It may be helpful to try more than one fitting possibility,
and more than one
Redistribute_from cut point, as results may vary.
Redistribute_from can be lower than your current open age group,
OAnew can be higher, as long as it is within the range of
Age_nLx doesn't go high enough for your needs, you can extrapolate
it ahead of time. For this, you'd want the
nMx the underlie it, and you
lt_abridged(), specifying a higher open age, and then
nLx again from it.
# India Males, 1971 Pop <- smooth_age_5(pop1m_ind, Age = 0:100, method = "Arriaga") Age_Pop <- names2age(Pop) AgeInt_Pop <- age2int(Age_Pop, OAvalue = 1) nLx <- downloadnLx(NULL, "India","male",1971)#> Downloading nLx data for India, years 1971, gender maleAge_nLx <- names2age(nLx) AgeInt_nLx <- age2int(Age_nLx, OAvalue = 1) Pop_fit <- OPAG(Pop, Age_Pop = Age_Pop, nLx = nLx, Age_nLx = Age_nLx, Age_fit = c(60,70), AgeInt_fit = c(10,10), Redistribute_from = 80)#> #> Age_Pop and Age_nLx age intervals are different!