correct_typos: Correct typos in restricted numeric data
In deductive: Data Correction and Imputation Using Deductive Methods

Description Usage Arguments Value Details References Examples

Attempt to fix violations of linear (in)equality restrictions imposed on a record by replacing values with values that differ from the original values by typographical errors.

correct_typos(dat, x, ...)

## S4 method for signature 'data.frame,validator'
correct_typos(dat, x, fixate = NULL, eps = 1e-08, maxdist = 1, ...)

`dat`	An R object holding numeric (integer) data.
`x`	An R object holding linear data validation rules
`...`	Options to be passed to `stringdist` which is used to determine the typographic distance between the original value and candidate solutions. By default, the optimal string alignment distance is used, with all weights equal to one.
`fixate`	`[character]` vector of variable names that may not be changed
`eps`	`[numeric]` maximum roundoff error
`maxdist`	`[numeric]` maximum allowd typographical distance

dat, with values corrected.

The algorithm works by proposing candidate replacement values and checking whether they are likely to be the result of a typographical error. A value is accepted as a solution when it resolves at least one equality violation. An equality restriction a.x=b is considered satisfied when abs(a.x-b)<eps. Setting eps to one or two units of measurement allows for robust typographical error detection in the presence of roundoff-errors.

The algorithm is meant to be used on numeric data representing integers.

The first version of the algorithm was described by S. Scholtus (2009). Automatic correction of simple typing errors in numerical data with balance edits. Statistics Netherlands, Discussion Paper 09046
The generalized version of this algorithm that is implemented for this package is described in M. van der Loo, E. de Jonge and S. Scholtus (2011). Correction of rounding, typing and sign errors with the deducorrect package. Statistics Netherlands, Discussion Paper 2011019

library(validate)

# example from section 4 in Scholtus (2009)

v <-validate::validator( 
   x1 + x2 == x3
 , x2 == x4
 , x5 + x6 + x7 == x8
 , x3 + x8 == x9
 , x9 - x10 == x11
 )
 

dat <- read.csv(textConnection(
"x1, x2 , x3  , x4 , x5 , x6, x7, x8 , x9   , x10 , x11
1452, 116, 1568, 116, 323, 76, 12, 411,  1979, 1842, 137
1452, 116, 1568, 161, 323, 76, 12, 411,  1979, 1842, 137
1452, 116, 1568, 161, 323, 76, 12, 411, 19979, 1842, 137
1452, 116, 1568, 161,   0,  0,  0, 411, 19979, 1842, 137
1452, 116, 1568, 161, 323, 76, 12,   0, 19979, 1842, 137"
))
cor <- correct_typos(dat,v)
dat - cor