Description Usage Arguments Details Value Note Author(s) References See Also Examples
Clean data by means of winsorization, i.e., by shrinking outlying observations to the border of the main part of the data.
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  winsorize(x, ...)
## Default S3 method:
winsorize(
x,
standardized = FALSE,
centerFun = median,
scaleFun = mad,
const = 2,
return = c("data", "weights"),
...
)
## S3 method for class 'matrix'
winsorize(
x,
standardized = FALSE,
centerFun = median,
scaleFun = mad,
const = 2,
prob = 0.95,
tol = .Machine$double.eps^0.5,
return = c("data", "weights"),
...
)
## S3 method for class 'data.frame'
winsorize(x, ...)

x 
a numeric vector, matrix or data frame to be cleaned. 
... 
for the generic function, additional arguments to be passed
down to methods. For the 
standardized 
a logical indicating whether the data are already robustly standardized. 
centerFun 
a function to compute a robust estimate for the center to
be used for robust standardization (defaults to

scaleFun 
a function to compute a robust estimate for the scale to
be used for robust standardization (defaults to 
const 
numeric; tuning constant to be used in univariate winsorization (defaults to 2). 
return 
character string; if 
prob 
numeric; probability for the quantile of the chisquared distribution to be used in multivariate winsorization (defaults to 0.95). 
tol 
a small positive numeric value used to determine singularity
issues in the computation of correlation estimates based on bivariate
winsorization (see 
The borders of the main part of the data are defined on the scale of the
robustly standardized data. In the univariate case, the borders are given
by +/const
, thus a symmetric distribution is assumed. In the
multivariate case, a normal distribution is assumed and the data are
shrunken towards the boundary of a tolerance ellipse with coverage
probability prob
. The boundary of this ellipse is thereby given by
all points that have a squared Mahalanobis distance equal to the quantile of
the chisquared distribution given by prob
.
If standardize
is TRUE
and return
is "weights"
,
a set of data cleaning weights. Multiplying each observation of the
standardized data by the corresponding weight yields the cleaned
standardized data.
Otherwise an object of the same type as the original data x
containing the cleaned data is returned.
Data cleaning weights are only meaningful for standardized data. In the general case, the data need to be standardized first, then the data cleaning weights can be computed and applied to the standardized data, after which the cleaned standardized data need to be backtransformed to the original scale.
Andreas Alfons, based on code by Jafar A. Khan, Stefan Van Aelst and Ruben H. Zamar
Khan, J.A., Van Aelst, S. and Zamar, R.H. (2007) Robust linear model selection based on least angle regression. Journal of the American Statistical Association, 102(480), 1289–1299.
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