Description Usage Arguments Value Technical note TODO
If 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.
1 2 3 4 5 | kp.estimator_(resp.data, known.populations, attribute.names, weights,
total.kp.size = NULL, alter.popn.size = NULL)
kp.estimator(resp.data, known.populations, attribute.names, weights,
total.kp.size = 1, alter.popn.size = NULL)
|
resp.data |
the dataframe that has the survey responses |
known.populations |
the names of the columns in |
attribute.names |
the names of the columns in |
weights |
weights to use in computing the estimate |
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 |
the estimated average degree for respondents in each
of the categories given by attribute.names
The estimated average degree is (∑ y_{F_α, A} / N_A) \times N_F / N_{F_α} here, we estimate N_F / N_{F_α} by dividing the total of all respondents' weights by the sum of the weights for respondents in each cell α.
handle case where attribute.names is NULL (should compute overall average)
handle missing values
integrate the individual-level estimator above, kp.degree.estimator
finish documentation for NSE version
make unit tests
think about how to elegantly add options for dbar_(P,Q) vs dbar_(Q,P)
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