summary.ipf | R Documentation |
Generates a detailed summary of an Iterative Proportional Fitting (IPF) calibration, providing a complete tool for evaluating the calibration's success and the validity of the resulting weights.
The output is a list of data.tables for a comprehensive evaluation, including:
Calibration Results:
calib_results_conP_*
and calib_results_conH_*
: Key diagnostic tables that compare calibrated margins to population targets and assess the goodness of fit via metrics like maxFac
.
Data and Diagnostics:
weighted data
: An excerpt of the final dataset with the calculated calibration weights.
distribution of the weights
: A statistical overview of the weight distribution (min, max, CV).
Detailed Margin Comparisons:
conP_*
, conH_*
, *_adjusted
, *_original
, *_rel_diff_*
: Tables that compare original sample margins, calibrated margins, and population targets, along with their relative differences.
## S3 method for class 'ipf'
summary(object, ...)
object |
object of class ipf |
... |
additional arguments |
a list of the following outputs
## Not run:
# load data
eusilc <- demo.eusilc(n = 1, prettyNames = TRUE)
# personal constraints
conP1 <- xtabs(pWeight ~ age, data = eusilc)
conP2 <- xtabs(pWeight ~ gender + region, data = eusilc)
conP3 <- xtabs(pWeight*eqIncome ~ gender, data = eusilc)
# household constraints
conH1 <- xtabs(pWeight ~ hsize + region, data = eusilc)
# simple usage ------------------------------------------
calibweights1 <- ipf(
eusilc,
conP = list(conP1, conP2, eqIncome = conP3),
bound = NULL,
verbose = TRUE
)
output <- summary(calibweights1)
# the output can easily be exported to an Excel file, e.g. with
# library(openxlsx)
# write.xlsx(output, "SummaryIPF.xlsx")
## End(Not run)
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