scaling {networkscaleup}

Scale raw log degree and log prevalence estimates

Description

This function scales estimates from either the overdispersed model or from the correlated models. Several scaling options are available.

Usage

scaling(
  log_degrees,
  log_prevalences,
  scaling = c("all", "overdispersed", "weighted", "weighted_sq"),
  known_sizes = NULL,
  known_ind = NULL,
  Correlation = NULL,
  G1_ind = NULL,
  G2_ind = NULL,
  B2_ind = NULL,
  N = NULL
)

Arguments

log_degrees

The matrix of estimated raw log degrees from either the overdispersed or correlated models.

log_prevalences

The matrix of estimates raw log prevalences from either the overdispersed or correlated models.

scaling

An character vector providing the name of scaling procedure should be performed in order to transform estimates to degrees and subpopulation sizes. Scaling options are 'overdispersed', 'all' (the default), 'weighted', or 'weighted_sq' ('weighted' and 'weighted_sq' are only available if 'Correlation' is provided. Further details are provided in the Details section.

known_sizes

The known subpopulation sizes corresponding to a subset of the columns of ard.

known_ind

The indices that correspond to the columns of ard with known_sizes. By default, the function assumes the first n_known columns, where n_known corresponds to the number of known_sizes.

Correlation

The estimated correlation matrix used to calculate scaling weights. Required if 'scaling = weighted' or 'scaling = weighted_sq'.

G1_ind

If 'scaling = overdispersed', a vector of indices corresponding to the subpopulations that belong to the primary scaling groups, i.e. the collection of rare girls' names in Zheng, Salganik, and Gelman (2006). By default, all known_sizes are used. If G2_ind and B2_ind are not provided, 'C = C_1', so only G1_ind are used. If G1_ind is not provided, no scaling is performed.

G2_ind

If 'scaling = overdispersed', a vector of indices corresponding to the subpopulations that belong to the first secondary scaling groups, i.e. the collection of somewhat popular girls' names.

B2_ind

If 'scaling = overdispersed', a vector of indices corresponding to the subpopulations that belong to the second secondary scaling groups, i.e. the collection of somewhat popular boys' names.

N

The known total population size.

Details

The 'scaling' options are described below:

NULL

No scaling is performed

overdispersed

The scaling procedure outlined in Zheng et al. (2006) is performed. In this case, at least 'Pg1_ind' must be provided. See overdispersedStan for more details.

all

All subpopulations with known sizes are used to scale the parameters, using a modified scaling procedure that standardizes the sizes so each population is weighted equally. Additional details are provided in Laga et al. (2021).

weighted

All subpopulations with known sizes are weighted according their correlation with the unknown subpopulation size. Additional details are provided in Laga et al. (2021)

weighted_sq

Same as 'weighted', except the weights are squared, providing more relative weight to subpopulations with higher correlation.

Value

The named list containing the scaled log degree, degree, log prevalence, and size estimates

References

Zheng, T., Salganik, M. J., and Gelman, A. (2006). How many people do you know in prison, Journal of the American Statistical Association, 101:474, 409–423

Laga, I., Bao, L., and Niu, X (2021). A Correlated Network Scaleup Model: Finding the Connection Between Subpopulations