seg()
is that here we adjust census 1 part way through processing, based on some calculations similar to GGB.ggbseg(X, minA = 15, maxA = 75, minAges = 8, exact.ages = NULL,
eOpen = NULL, deaths.summed = FALSE)
data.frame
with columns, $pop1
, $pop2
, $deaths
, $date1
, $date2
, $age
, $sex
, and $cod
(if there are more than 1 region/sex/intercensal period).TRUE
). By default we assume FALSE
, i.e. that the average annual was given.data.frame
with columns for the coverage coefficient $coverage
, and the minimum $lower
and maximum $upper
of the age range on which it is based. Rows indicate data partitions, as indicated by the optional $cod
variable.$date1
and $date2
(or an unambiguous character string of the date, like, "1981-05-13"
) or 2) by giving column names "day1","month1","year1","day2","month2","year2"
containing integers. If only year1
and year2
columns are given, then we assume January 1 dates. If year and month are given, then we assume dates on the first of the month. If you want coverage estimates for a variety of intercensal periods/regions/by sex, then stack them, and use a variable called $cod
with a unique values for each data chunk. Different values of $cod
could indicate sexes, regions, intercensal periods, etc. The $deaths
column should refer to the average annual deaths in each age class in the intercensal period. Sometimes one uses the arithmetic average of recorded deaths in each age, or simply the average of the deaths around the time of census 1 and census 2. To identify an age-range in the traditional visual way, see plot.ggb()
, when working with a single year/sex/region of data. The automatic age-range determination feature of this function tries to implement an intuitive way of picking ages that follows the advice typically given for doing so visually. We minimize the square of the average squared residual between the fitted line and right term. Finally, only specify eOpen
when working with a single region/sex/period of data, otherwise the same value will be passed in irrespective of mortality and sex.Hill K, You D, Choi Y. Death distribution methods for estimating adult mortality: sensitivity analysis with simulated data errors. Demographic Research. 2009; 21:235-254.
Preston, S. H., Coale, A. J., Trussel, J. & Maxine, W. Estimating the completeness of reporting of adult deaths in populations that are approximately stable. Population Studies, 1980; v.4: 179-202
# The Mozambique data
res <- ggbseg(Moz)
res
# The Brasil data
BM <- ggbseg(BrasilMales)
BF <- ggbseg(BrasilFemales)
head(BM)
head(BF)
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