Aggregate samples by non-age group — aggregate

Consolidates a set of samples over non-age groups using a population array to create weighted-average samples.

Usage

aggregate_samples(
  sample,
  pop,
  margin,
  groups = NULL,
  bind_new = FALSE,
  new_name = NULL
)

Arguments

sample: an array of samples imported with load_samples()
pop: The population array to be used for weighted averages.
margin: For arrays, The margin on which the groups of interest are stratified.
groups: A vector of either indices for each group or a vector of strings for each group name. If set to NULL, will use all groups in the dataset.
bind_new: If set to TRUE, will bind an array to the original sample dataset. Otherwise, will generate a standalone array of samples.
new_name: The name to assign to the age-standardized group.

Value

An array of weighted-average samples.

Details

aggregate_samples() is only meant for non-age group data, such as spatial regions, time periods, or other sociodemographic groups (race, sex, etc.). If you are interested in consolidating samples by age group, use age_standardize() instead. Additionally, if you plan on doing age-standardization along with aggregating by other groups, always aggregate groups first before doing age-standardization to ensure that the samples are properly standardized.

Examples

pop <- miheart$n[1:2, 1:3, 1:3]
time_margin <- 3
# calculate prevalence by aggregating over time periods
samples_3564 <- aggregate_samples(minsample, pop, margin = time_margin)
# calculate prevalence of only the first two time periods
samples_3554 <- aggregate_samples(minsample, pop, time_margin, groups = 1:2)
# bind prevalence samples to original samples
samples_prev <- aggregate_samples(
  minsample,
  pop,
  time_margin,
  bind_new = TRUE,
  new_name = "1979-1981"
)