RDS.I.DS.estimates {RDS} | R Documentation |
This function computes the basic RDS-I/DS estimates for a categorical variable. This is the ``data smoothed'' version of RDS where it is assumed that the observed Markov process is reversible.
RDS.I.DS.estimates(rds.data, group.variable, network.variable)
rds.data |
A data frame. This data frame must identify recruitment patters by a pair of fields named ``recruitment.id'' and ``recruiter.id''. |
group.variable |
A categorical variable to be analyzed. |
network.variable |
A string giving the name of the variable in the rds.data that contains the network sizes of
survey respondents. |
A vector of proportion estimates constructed using the ``smoothed'' transition matrix.
W. Whipple Neely
Gile, K. J., Handcock, M. S., 2009b. Respondent-driven sampling: An assessment of current methodology. Under review, Nuffield College, University of Oxford.
Neely, W. W., 2009. Bayesian methods for data from respondent driven sampling. Dissertation in-progress, Department of Statistics, University of Wisconsin, Madison.
Salganik, M., Heckathorn, D. D., 2004. Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology 34, 193-239.
Volz, E., Heckathorn, D., 2008. Probability based estimation theory for Respondent Driven Sampling. The Journal of Official Statistics 24 (1), 79-97.
RDS.I.estimates
RDS.II.estimates
data(simulated) RDS.I.DS.estimates(rds.data=simulated.data,group.variable='X',network.variable='network.size')