adj_outlier: Corrects data located in outlier.
In hyunyulhenry/HenryQuant: Favorite R Functions for Henry's Quantopia <http://henryquant.blogspot.kr/>
Description
Usage
Arguments
Examples
Description
It is possible to use the
Windsorizing method to correct the data above the reference value and the
Trim(Truncation) method to delete data above the reference value.
Usage
1
adj_outlier(Input, Target = 0.01, Winsorizing = TRUE)
Arguments
Input
Factor Exposure Data
Target
reference value, basically 1 percent
Winsorizing
TRUE for Winsorizing method, FALSE for Trim(Truncation) method
Examples
1
2
3
4
5
## Not run:
Input = sample(-1000:1000, 100, replace=F)
x = adj_outlier(Input, Target = 0.01, Winsorizing = TRUE)
## End(Not run)
hyunyulhenry/HenryQuant documentation built on Nov. 15, 2019, 2:28 a.m.
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Description Usage Arguments Examples
Description
It is possible to use the Windsorizing method to correct the data above the reference value and the Trim(Truncation) method to delete data above the reference value.
Usage
1 | adj_outlier(Input, Target = 0.01, Winsorizing = TRUE)
|
Arguments
Input |
Factor Exposure Data |
Target |
reference value, basically 1 percent |
Winsorizing |
TRUE for Winsorizing method, FALSE for Trim(Truncation) method |
Examples
1 2 3 4 5 | ## Not run:
Input = sample(-1000:1000, 100, replace=F)
x = adj_outlier(Input, Target = 0.01, Winsorizing = TRUE)
## End(Not run)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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