PLSroundingFits {SmallCountRounding}

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")]