This algorithm tries to detect and repair records that violate linear (in)equality constraints by correcting possible rounding errors as described by Scholtus(2008). Typically data is constrainted by Rx=a and Qx ≥ b.
1 2 3 4 5 6 7 8  correctRounding(E, dat, ...)
## S3 method for class 'editset'
correctRounding(E, dat, ...)
## S3 method for class 'editmatrix'
correctRounding(E, dat, fixate = NULL, delta = 2,
K = 10, round = TRUE, assumeUnimodularity = FALSE, ...)

E 

dat 

... 
arguments to be passed to other methods. 
fixate 

delta 
tolerance on checking for rounding error 
K 
number of trials per record. See details 
round 
should the solution be rounded, default TRUE 
assumeUnimodularity 
If 
The algorithm first finds violated constraints
r'_{i}xa_i > 0 , and selects edits that may be due to a rounding error 0 < r'_{i}xa_i ≤q δ.
The algorithm then makes a correction suggestion where the errors are attributed to randomly selected variables under the lineair equality constraints.
It checks if the suggested correction
does not violate the inequality matrix Q. If it does, it will try to generate a different solution up till K
times.
A deducorrrect
object.
Scholtus S (2008). Algorithms for correcting some obvious inconsistencies and rounding errors in business survey data. Technical Report 08015, Statistics Netherlands.
deducorrectobject
status
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63  E < editmatrix(expression(
x1 + x2 == x3,
x2 == x4,
x5 + x6 + x7 == x8,
x3 + x8 == x9,
x9  x10 == x11
)
)
dat < data.frame( x1=12
, x2=4
, x3=15
, x4=4
, x5=3
, x6=1
, x7=8
, x8=11
, x9=27
, x10=41
, x11=13
)
sol < correctRounding(E, dat)
# example with editset
for ( d in dir("../pkg/R/",full.names=TRUE) ) dmp < source(d)
E < editmatrix(expression(
x + y == z,
x >= 0,
y >= 0,
z >= 0,
if ( x > 0 ) y > 0
))
dat < data.frame(
x = 1,
y = 0,
z = 1)
# solutions causing new violations of conditional rules are rejected
sol < correctRounding(E,dat)
# An example with editset
E < editset(expression(
x + y == z,
x >= 0,
y > 0,
y < 2,
z > 1,
z < 3,
A %in% c('a','b'),
B %in% c('c','d'),
if ( A == 'a' ) B == 'b',
if ( B == 'b' ) x < 1
))
dat < data.frame(
x = 0,
y = 1,
z = 2,
A = 'a',
B = 'b'
)
correctRounding(E,dat)

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