kp.degree.estimator.Rdsee kp.individual.estimator instead.
kp.degree.estimator( survey.data, known.popns = NULL, total.popn.size = NULL, dropmiss = FALSE, verbose = FALSE )
| survey.data | the dataframe with the survey results |
|---|---|
| known.popns | if not NULL, a vector whose entries are the size of the known populations, and whose names are the variable names in the dataset corresponding to each one. if NULL, then assume that the survey.data dataframe has an attribute called 'known.popns' containing this vector. |
| total.popn.size | the size of the entire population. if NULL, this function returns proportions; if not NULL, it returns absolute numbers (ie, the proportions * total popn size) |
| verbose | if TRUE, print messages to the screen |
| missing | if "ignore", then proceed with the analysis without doing anything about missing values. if "complete.obs" then, for each row, use only the known populations that have no missingness for the computations. care must be taken in using this second option |
a vector with an estimate of the degree for each row in survey.data. if missing=="ignore", then the degree for rows that have missingness in the 'how many X' questions will be set to NA
compute an estimate of the respondents' degrees using
the known population method
note that this function does not take survey weights, since these estimates are not for total degree, but just for the individual degree of each respondent