apply_mov: Apply multivariate outlier detection
In konradsemsch/aider: Perform everyday analytical and data science tasks more efficiently
Description
Usage
Arguments
Examples
Description
This function calculates individual observations outlier score with the Lof algorithm and
returns the original data frame with the Lof outlier score, Lof stats and variables with the
highest impact on the Lof score, as well as a data frame with the most outlying cases (approx. top 5
Usage
1
Arguments
df
A data frame
Examples
1
2
3
4
5
6
7
data <- recipes::credit_data %>%
apply_recipe(Status) %>%
prep(retain = TRUE) %>%
juice() %>%
select(-Status)
out <- apply_mov(data)
konradsemsch/aider documentation built on May 22, 2019, 2:40 p.m.
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Description Usage Arguments Examples
Description
This function calculates individual observations outlier score with the Lof algorithm and returns the original data frame with the Lof outlier score, Lof stats and variables with the highest impact on the Lof score, as well as a data frame with the most outlying cases (approx. top 5
Usage
1 |
Arguments
df |
A data frame |
Examples
1 2 3 4 5 6 7 | data <- recipes::credit_data %>%
apply_recipe(Status) %>%
prep(retain = TRUE) %>%
juice() %>%
select(-Status)
out <- apply_mov(data)
|
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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