errorLocation: The errorLocation object

Description Usage Arguments Details See Also Examples

Description

Object storing information on error locations in a dataset.

summary

Usage

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## 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.

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

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# 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 July 2, 2018, 1 a.m.