PLSroundingFits: Small count rounding with post-processing to expected...

In statisticsnorway/SmallCountRounding: Small Count Rounding of Tabular Data

Small count rounding with post-processing to expected frequencies

Description

The counts rounded by PLSrounding Thereafter, based on the publishable rounded data, expected inner cell frequencies are generated by iterative proportional fitting using Mipf. To ensure that empty cells missing in input data are included in the fitting process, the data is first extended using Extend0.

Usage

PLSroundingFits(
  data,
  freqVar = NULL,
  roundBase = 3,
  hierarchies = NULL,
  formula = NULL,
  dimVar = NULL,
  preAggregate = is.null(freqVar),
  xReturn = FALSE,
  extend0 = TRUE,
  limit = 1e-10,
  viaQR = FALSE,
  iter = 1000,
  eps = 0.01,
  tol = 1e-13,
  reduceBy0 = TRUE,
  reduceByColSums = TRUE,
  reduceByLeverage = FALSE,
  ...
)

Arguments

data	data frame (inner cells)
freqVar	Variable holding counts
roundBase	Rounding base
hierarchies	List of hierarchies
formula	Model formula
dimVar	Dimensional variables
preAggregate	Aggregation
xReturn	Dummy matrix in output when TRUE. To return crossTable as well, use xReturn = 2.
extend0	Data is automatically extended by Extend0 when TRUE. Can also be specified as a list meaning parameter varGroups to Extend0.
limit	LSfitNonNeg parameter
viaQR	LSfitNonNeg parameter
iter	Mipf parameter
eps	Mipf parameter
tol	Mipf parameter
reduceBy0	Mipf parameter
reduceByColSums	Mipf parameter
reduceByLeverage	Mipf parameter
...	Further parameters to PLSrounding.

Details

The seven first parameters is documented in more detail in PLSrounding. If iterative proportional fitting succeeds, the maximum difference between rounded counts and ipFit is less than input parameter eps.

Value

Output from PLSrounding (class attribute "PLSrounded") with modified versions of inner and publish:

inner	Extended with more input data variables and with expected frequencies (ipFit).
publish	Extended with aggregated expected frequencies (ipFit).

Examples

z <- data.frame(geo  = c("Iceland", "Portugal", "Spain"), 
                eu = c("nonEU", "EU", "EU"),
                year = rep(c("2018","2019"), each = 3),
                freq = c(2,3,7,1,5,6), stringsAsFactors = FALSE)
z4 <- z[-c(1:2), ]

PLSroundingFits(z4, "freq", formula = ~eu * year + geo, extend0 = FALSE)[c("inner", "publish")]
PLSroundingFits(z4, "freq", formula = ~eu * year + geo)[c("inner", "publish")]

my_km2 <- SSBtools::SSBtoolsData("my_km2")

# Default automatic extension (extend0 = TRUE)
PLSroundingFits(my_km2, "freq", 
       formula = ~(Sex + Age) * Municipality * Square1000m + Square250m)[c("inner", "publish")]

# Manual specification to avoid Nittedal combined with another_km
PLSroundingFits(my_km2, "freq", formula = ~(Sex + Age) * Municipality * Square1000m + Square250m, 
       extend0 = list(c("Sex", "Age"), 
       c("Municipality", "Square1000m", "Square250m")))[c("inner", "publish")]

statisticsnorway/SmallCountRounding documentation built on July 8, 2023, 7:24 p.m.