Description Usage Arguments Details Value References See Also
Identify anomalous coefficients using depth
1 2 | depth_Outliers(coeff, d.method = "L2", c.method = "depth", alpha = 0.05,
B = 1000)
|
coeff |
A dataframe of coefficients of interest. The first column is |
d.method |
A character string determining the depth function to use: "LP", "Projection",
"Mahalanobis", or "Euclidean". It is suggested to not use "Tukey" due to singularity in
coefficient matrix. For details see |
c.method |
A character string determining the method to estimate the cutoff value. This can be "depth" or "alpha". |
alpha |
A value determining the percentage of rows to remove from |
B |
A value determining how many bootstrap datasets should be made to estimate the cutoff value with a suggested rate of 1000. |
The function uses a bootstrap method to estimate a cutoff depth value. Depths below this cutoff depth value are flagged as anomalous.
The "alpha" c.method
removes the alpha percent least deep coefficients. The rest of the
coefficients are bootstrapped and new depths are computed for each new bootstrapped set. The
1
1
The "depth" c.method
bootstraps the coefficients with probability related to the
original depth values. New depths are computed for each new bootstrapped set. The
1 percent empirical percentile of the depths is saved. The cuttoff value is the median of these
1 percent empirical percentile of the depths.
A list with $outliers
is a tibble containing the ID, outlier identification, depth value.
True identifies outliers. $Cb
is the cutoff value
febrero2008unequalgroupoutlier
depth
, bootstrap_C.alpha
, and
bootstrap_C.depth
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