Description Usage Arguments Details Value Author(s) References Examples
Returns a dataframe with outlier groups removed. Note that each column
(other than the parameter
column) is treated as a separate assay.
Therefore if one 'group' does not meet the criteria for inclusion in 1
assay (column value), but does for all others, only the data for the assay
failing the quality control will be removed.
1 | rm.outliers(data, parameter='FLATCODE', n=3, ...)
|
data |
Dataframe or Matrix of inputs for which outliers are to be removed |
parameter |
The parameter (given by column name) on which defines a subgroup. This is the only non-numeric column allowed. |
n |
the number of Median Absolute Deviations (MAD) from the global median a subgroup is allowed to be before it is considered an outlier. |
... |
Other parameters. |
Data should be presorted according to both identifier and parameter
prior to running command. Order will be retained if this is followed.
data
should be a dataframe with a parameter
serving as a label
and the rest of the values numeric. Note that parameter
should be the
attribute where the most error is expected. A visual inspection using box
and whisker plots may be helpful in determining the best variable to use.
data
is first broken down into groups based on parameter
. Both
the group median and the median of all groups (global median) is calculated.
Groups where the absolute value of the difference between the group median
and global median is greater than n
* MAD(group medians) for a
given attribute (column) have their values removed (ie set to NA). Data for
the group is retained for columns that pass this criteria.
rm.outliers
returns a dataframe containing data with values not
passing the filter converted to NA.
Shannon M. Bell
Bell SM, Burgoon LD, Last RL. MIPHENO: Data normalization for high throughput metabolite analysis. BMC Bioinformatics 2012, 13(10)
1 | #See the sweave document in the corresponding paper for examples
|
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