kp.estimator.RdIf given attribute.names, then this function produces
estimated average network sizes given by the groups that are defined by
all combinations of the attributes; otherwise, it estimates the
average personal network size for the entire frame population.
kp.estimator_( resp.data, known.populations, weights, boot.weights = NULL, ego.id = NULL, return.boot = FALSE, attribute.names = NULL, total.kp.size = NULL, alter.popn.size = NULL, dropmiss = FALSE, verbose = TRUE ) kp.estimator( resp.data, known.populations, weights, attribute.names = NULL, total.kp.size = 1, alter.popn.size = NULL, dropmiss = FALSE )
| resp.data | the dataframe that has the survey responses |
|---|---|
| known.populations | the names of the columns in |
| weights | the name of a column that has sampling weights |
| boot.weights | Optional dataframe with bootstrap resampled weights. See Details for more info. |
| ego.id | If boot.weights are included, then this is the name of the column(s) we need to join the bootstrap weights onto the dataset. This is most often the id of the ego making the reports. |
| return.boot | if TRUE and boot.weights are included, then return the full bootstrapped estiamates and not just the summaries; this option causes this function to return a list instead of a tibble |
| attribute.names | the names of the columns in |
| total.kp.size | the size of the probe alters; i.e., the sum of the known population sizes. if NULL, then this is set to 1 |
| alter.popn.size | the size of the population of alters; this is most often the frame population, which is the default if nothing else is specified; the size of the frame population is taken to be the sum of the weights over all of resp.data |
| dropmiss | if FALSE, then, for each row, use only the reports about connections to known populations that have no missingness. This effectively assumes that missing reports are 0. if TRUE, then only use rows that have no missingness in reported connections to known populations in estimating degree. in this case, the sampling weights are rescaled so that the implied total size of the frame population is not changed. (see the 'dropmiss' argument to the function report.aggregator_) future versions may have other options |
| verbose | if TRUE, print information to screen |
the estimated average degree (dbar.Fcell.F) for respondents in each
of the categories given by attribute.names
The estimated average degree is \((\sum y_{F_\alpha, A} / N_A) \times N_F / N_{F_\alpha}\) here, we estimate \(N_F / N_{F_\alpha}\) by dividing the total of all respondents' weights by the sum of the weights for respondents in each cell \(\alpha\).
make unit tests
think about how to elegantly add options for dbar_(P,Q) vs dbar_(Q,P)
If you want estimated sampling variances, you can pass in a data frame boot.weights.
boot.weights is assumed to have a column that is named whatever the ego.id is,
and then a series of columns named boot_weight_1, ..., boot_weight_M.
The two options for missing values are 'ignore' or 'complete.obs'. 'ignore' adds up each respondent's nonmissing reported connections to the known populations, effectively treating missing reports as 0s. 'complete.obs' only uses responses from respondents who have non-missing values for all of the known population reports.