imputeQs: Quartile Naive Imputation of Missing Values
In mvdalab: Multivariate Data Analysis Laboratory
imputeQs R Documentation
Quartile Naive Imputation of Missing Values
Description
Missing value imputed as 'Missing'.
Usage
imputeQs(data)
Arguments
data
a dataset with missing values
Details
A completed data frame is returned. For continous variables with missing values, missing values are replaced with 'Missing', while the non-missing values are replaced with their corresponding quartile assignment. For categorical variable with missing values, missing values are replaced with 'Missing'. This procedure can greatly increases the dimensionality of the data.
Author(s)
Nelson Lee Afanador (nelson.afanador@mvdalab.com)
Examples
dat <- introNAs(iris, percent = 25)
imputeQs(dat)
mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.
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| imputeQs | R Documentation |
Quartile Naive Imputation of Missing Values
Description
Missing value imputed as 'Missing'.
Usage
imputeQs(data)
Arguments
data |
a dataset with missing values |
Details
A completed data frame is returned. For continous variables with missing values, missing values are replaced with 'Missing', while the non-missing values are replaced with their corresponding quartile assignment. For categorical variable with missing values, missing values are replaced with 'Missing'. This procedure can greatly increases the dimensionality of the data.
Author(s)
Nelson Lee Afanador (nelson.afanador@mvdalab.com)
Examples
dat <- introNAs(iris, percent = 25) imputeQs(dat)
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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