prepare a dataset from nrsimulatr for sibling analysis

Usage

prep_nrsim_sib_histories(
  df,
  varmap,
  keep_missing = FALSE,
  keep_varmap_only = FALSE,
  weight.scale = 1,
  death.exposure = c("dhs", "mics"),
  verbose = TRUE
)

Arguments

df: the raw dataset (each row is a survey response)
varmap: see Details; defaults to NULL
keep_missing: should we keep reported sibs that are missing sex or survival status?
keep_varmap_only: should we only keep ego variables mentioned in the varmap? (Default: FALSE)
weight.scale: divide the weight by this number. Defaults to 1, since non-DHS weights are typically already normalized; see Details
death.exposure: whether a sibling who died contributes the month of death as exposure; "dhs" (default) counts it, "mics" stops the month before. See get_sib_df
verbose: report detailed summaries?

Value

a list; see Details

Details

This function is similar to prep_dhs_sib_histories, but it is not customized to work with DHS survey data.

In particular, weight.scale defaults to 1 here, because weights outside the DHS are typically already normalized to average 1. Pass weight.scale = 1e6 if you are preparing data that follows the DHS convention of publishing weights multiplied by 1,000,000.

Note that if the dataframe does not have a column called 'sex', then one will be added, and we will assume respondents are all female (sex='f'). If you want to avoid this, only pass in a dataframe after adding the 'sex' column.

varmap should be a dataframe with columns

orig.varname (the raw variable name)
new.varname (the new variable name)
sibvar (a 0/1 column, with 1 meaning this is a sibling variable and 0 meaning an ego variable)

Each row of varmap describes a variable to rename from the original dataset. Note that you MUST include varmap; at a minimum, it is needed to show which columns are siblings...

For respondents, you should be sure to include

survey (the survey id, usually a country code plus one digit)
caseid (the respondent id)
wwgt (the sampling weight for women)
psu (the primary sampling unit)
doi (the date of the interview)

For siblings, you should be sure to include

sib.death.date (the date of the sibling's death)
sib.alive (whether or not the sib is alive)
sib.sex (the sex of the sibling, coded 'f' or 'm').

Returns a list whose entries include

ego.dat - dataset with information about the survey respondents
sib.dat - dataset with information about the reported siblings
summ - a one-row tibble with a summary of the data

Examples

  # TODO - write example code