R_indicator {sampcompR}

Calculate the R-Indicator

Description

Calculates the R-Indicator of the (weighted) data frame.

Usage

R_indicator(
  dfs,
  response_identificators,
  variables,
  id = NULL,
  weight = NULL,
  strata = NULL,
  get_r2 = FALSE
)

Arguments

dfs

A character vector containing the names of data frames to calculate the R indicator.

response_identificators

A character vector, naming response identificators for every df. Response identificators should indicate if respondents are part of the set of respondents (respondents = 1) or not part of the set of respondents. (non-respondents = 0). If only one character is provided, the same variable is used in every df.

variables

A character vector with the names of variables that should be used in the model to calculate the R indicator.

id

A character vector that determines id variables that are used to weight the dfs with the help of the survey package. They have to be part of the respective data frame. If only one character is provided, the same variable is used to weight every df.

weight

A character vector that determines variables to weight the dfs. They have to be part of the respective data frame. If only one character is provided, the same variable used to weight every df. If a weight variable is provided also an id variable is needed. For weighting, the survey package is used.

strata

A character vector that determines strata variables that are used to weight the dfs with the help of the survey package. They have to be part of the respective data frame. If only one character is provided, the same variable is used to weight every df.

get_r2

If true, Pseudo R-squared of the propensity model will be returned, based on the method of McFadden.

Value

A list containing the R-indicator, and its standard error for every data frame.

Note

The calculated R-indicator is based on Shlomo et al., (2012).

References

• Shlomo, N., Skinner, C., & Schouten, B. (2012). Estimation of an indicator of the representativeness of survey response. Journal of Statistical Planning and Inference, 142(1), 201–211. https://doi.org/10.1016/j.jspi.2011.07.008

Examples

require(wooldridge)
card<-wooldridge::card

# For the purpose of this example, we assume that only respondents living in 
# the south or only white respondents have participated in the survey.

sampcompR::R_indicator(dfs=c("card","card"),
                       response_identificators = c("south","black"),
                       variables = c("age","educ","fatheduc","motheduc","wage","IQ"),
                       weight = c("weight","weight"))