getOutlier {mcradds} | R Documentation |
Detect Outliers From BAsummary
Object
Description
Detect the potential outliers from the absolute and relative differences in
BAsummary
object with 4E and ESD method.
Usage
getOutlier(object, ...)
## S4 method for signature 'BAsummary'
getOutlier(
object,
method = c("ESD", "4E"),
difference = c("abs", "rel"),
alpha = 0.05,
h = 5
)
Arguments
object |
( |
... |
not used. |
method |
( |
difference |
( |
alpha |
( |
h |
( |
Value
A list contains the statistics results (stat
), outliers' ord id (ord
),
sample id (sid
), matrix with outliers (outmat
) and matrix without outliers (rmmat
).
Note
Bland-Altman analysis is used as the input data regardless of the 4E and ESD method because it's necessary to determine the absolute and relative differences beforehand. For the 4E method, both of the absolute and relative differences are required to be define, and the bias exceeds the 4 fold of the absolute and relative differences. However for the ESD method, only one of them is necessary (the latter is more recommended), and the bias needs to meet the ESD test.
Examples
data("platelet")
# Using `blandAltman` function with default arguments
ba <- blandAltman(x = platelet$Comparative, y = platelet$Candidate)
getOutlier(ba, method = "ESD", difference = "rel")
# Using sample id as input
ba2 <- blandAltman(x = platelet$Comparative, y = platelet$Candidate, sid = platelet$Sample)
getOutlier(ba2, method = "ESD", difference = "rel")
# Using `blandAltman` function when the `tyep2` is 2 with `X vs. (Y-X)/X` difference
ba3 <- blandAltman(x = platelet$Comparative, y = platelet$Candidate, type2 = 4)
getOutlier(ba3, method = "ESD", difference = "rel")
# Using "4E" as the method input
ba4 <- blandAltman(x = platelet$Comparative, y = platelet$Candidate)
getOutlier(ba4, method = "4E")