editrules (version 2.0-3)

localizeErrors: Localize errors on records in a data.frame.

Description

Loops over all records in dat and performs error localization with errorLocalizer. For each record it finds the smallest (weighted) number of variables to be imputed or adapted such that all violated edits can be satisfied, without violating new ones. If there are multiple optimal (equally weighted) solutions a random solution is chosen.

Usage

localizeErrors(E, dat, useBlocks = TRUE, verbose = FALSE,
    weight = rep(1, ncol(dat)), maxduration = 600,
    method = c("localizer", "mip"), ...)

Arguments

E

an object of class editmatrix or editarray

dat

a data.frame with variables in E.

useBlocks

process error localization seperatly for independent blocks in E?

verbose

print progress to screen?

weight

Vector of positive weights for every variable in dat, or an array of weights with the same dimensions as dat.

method

should errorlocalizer ("localizer") or mix integer programming ("mip") be used? NOTE: option "mip" is currently experimental.

maxduration

maximum time for $searchBest() to find the best solution for a single record.

...

Further options to be passed to errorLocalizer

Value

• an object of class errorLocation

Details

For performance purposes, the edits are split in independent blocks which are processed separately. The results are summarized in the output object, causing some loss of information. For example, the number of solutions per record (degeneracy) per block is lost. To retain this information do someting like err <- list(); for ( b in blocks(E)) err <- c(err,localizeErrors(b,dat))

By default, all weights are set equal to one (each variable is considered equally reliable). If a vector of weights is passed, the weights are assumed to be in the same order as the columns of dat. By passing an array of weights (same dimension as dat) separate weights can be specified for each record.

Examples

# an editmatrix and some data:
E <- editmatrix(c(
    "x + y == z",
    "x > 0",
    "y > 0",
    "z > 0"))

dat <- data.frame(
    x = c(1,-1,1),
    y = c(-1,1,1),
    z = c(2,0,2))

# localize all errors in the data
err <- localizeErrors(E,dat)

summary(err)

# what has to be adapted:
err$adapt
# weight, number of equivalent solutions, timings,
err$status


## Not run

# Demonstration of verbose processing
# construct 2-block editmatrix
F <- editmatrix(c(
    "x + y == z",
    "x > 0",
    "y > 0",
    "z > 0",
    "w > 10"))
# Using 'dat' as defined above, generate some extra records
dd <- dat
for ( i in 1:5 ) dd <- rbind(dd,dd)
dd$w <- sample(12,nrow(dd),replace=TRUE)

# localize errors verbosely
(err <- localizeErrors(F,dd,verbose=TRUE))

# printing is cut off, use summary for an overview
summary(err)

# or plot (not very informative in this artificial example)
plot(err)

## End(Not run)


# Example with different weights for each record
E <- editmatrix('x + y == z')
dat <- data.frame(
    x = c(1,1),
    y = c(1,1),
    z = c(1,1))

# At equal weights, both records have three solutions (degeneracy): adapt x, y or z:
localizeErrors(E,dat)$status

# Set different weights per record (lower weight means lower reliability):
w <- matrix(c(
    1,2,2,
    2,2,1),nrow=2,byrow=TRUE)

localizeErrors(E,dat,weight=w)


# an example with categorical variables
E <- editarray(c(
    "age \%in\% c('under aged','adult')",
    "maritalStatus \%in\% c('unmarried','married','widowed','divorced')",
    "positionInHousehold \%in\% c('marriage partner', 'child', 'other')",
    "if( age == 'under aged' ) maritalStatus == 'unmarried'",
    "if( maritalStatus \%in\% c('married','widowed','divorced')) !positionInHousehold \%in\% c('marriage partner','child')"
    )
)
E

#
dat <- data.frame(
    age = c('under aged','adult','adult' ),
    maritalStatus=c('married','unmarried','widowed' ), 
    positionInHousehold=c('child','other','marriage partner')
)
dat
localizeErrors(E,dat)
# the last record of dat has 2 degenerate solutions. Running  the last command a few times
# demonstrates that one of those solutions is chosen at random.

# Increasing the weight of  'positionInHousehold' for example, makes the best solution
# unique again
localizeErrors(E,dat,weight=c(1,1,2))