errorLocation: The errorLocation object

In editrules: Parsing, Applying, and Manipulating Data Cleaning Rules

Description

Object storing information on error locations in a dataset.

summary

Usage

## S3 method for class 'errorLocation'
plot(x, topn = min(10, ncol(x$adapt)), ...)

## S3 method for class 'errorLocation'
summary(object, ...)

Arguments

x	errorLocation object
topn	Number of variables to show in 'errors per variable plot'. Only the top-n are are shown. By default the top-20 variables with the most errors are shown.
...	other arguments that will be transferred to barplot
object	an R object

Details

The errorlocation objects consists of the following slots wich can be accessed with the dollar operator, just like with lists. Right now the only functions creating such objects are localizeErrors and checkDatamodel.

• adapt a logical array where each row/column shows which record/variable should be adapted.

• status A data.frame with the same number of rows as adapt. It contains the following columns

  • weight weight of the found solution

  • degeneracy number of equivalent solutions found

  • user user time used to generate solution (as in sys.time)

  • system system time used to generate solution (as in sys.time)

  • elapsed elapsed time used to generate solution (as in sys.time)

  • maxDurationExceeded Was the maximum search time reached?

  • memfail Indicates whether a branch was broken off due to memory allocation failure (branch and bound only)

• method The error localization method used, can be "mip", "localizer" or "checkDatamodel".

• call The R calls to the function generating the object.

• user character user who generated the object.

• timestamp character timestamp.

It is possible to plot objects of class errorLocation. An overview containing three or four graphs will be plotted in a new window. Axes in scatterplots are set to logarithmic if their scales maxima exceed 50.

See Also

localizeErrors, checkDatamodel

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)

for ( d in dir("../pkg/R",full.names=TRUE)) dmp <- source(d)
# 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(expression(
    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))


# an example with mixed data:

E <- editset(expression(
    x + y == z,
    2*u  + 0.5*v == 3*w,
    w >= 0,
    if ( x > 0 ) y > 0,
    x >= 0,
    y >= 0,
    z >= 0,
    A %in% letters[1:4],
    B %in% letters[1:4],
    C %in% c(TRUE,FALSE),
    D %in% letters[5:8],
    if ( A %in% c('a','b') ) y > 0,
    if ( A == 'c' ) B %in% letters[1:3],
    if ( !C == TRUE) D %in% c('e','f')
))

set.seed(1)
dat <- data.frame(
    x = sample(-1:8),
    y = sample(-1:8),
    z = sample(10),
    u = sample(-1:8),
    v = sample(-1:8),
    w = sample(10),
    A = sample(letters[1:4],10,replace=TRUE),
    B = sample(letters[1:4],10,replace=TRUE),
    C = sample(c(TRUE,FALSE),10,replace=TRUE),
    D = sample(letters[5:9],10,replace=TRUE),
    stringsAsFactors=FALSE
)

(el <-localizeErrors(E,dat,verbose=TRUE))

editrules documentation built on May 1, 2019, 6:32 p.m.