kp.degree.estimator (DEPRECATED)
kp.degree.estimator.Rdsee kp.individual.estimator instead.
Usage
kp.degree.estimator(
survey.data,
known.popns = NULL,
total.popn.size = NULL,
dropmiss = FALSE,
verbose = FALSE
)Arguments
- 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)
- dropmiss
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
- verbose
if TRUE, print messages to the screen