computeMSIE: Mean Squared Imputation Error
In wNNSel: Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables
Description
Usage
Arguments
Value
Examples
Description
This function computes the mean squared imputation error for a given complete/true data matrix,
imputed data matrix and the data matrix with missing values.
Usage
1
computeMSIE(x.miss, x.impute, x.true)
Arguments
x.miss
a matrix, having missing values
x.impute
an imputed data matrix. Note that it should not contain any missing values.
x.true
complete/true data matrix. Note that it should not contain any missing values.
Value
value of MSIE
Examples
1
2
3
4
5
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7
set.seed(3)
x.true = matrix(rnorm(100),10,10)
## create 10% missing values in x
x.miss = artifNA(x.true, 0.10)
## impute using wNNSel method
x.impute = wNNSel.impute(x.miss)
computeMSIE(x.miss, x.impute, x.true)
wNNSel documentation built on May 2, 2019, 2:49 p.m.
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Description Usage Arguments Value Examples
Description
This function computes the mean squared imputation error for a given complete/true data matrix, imputed data matrix and the data matrix with missing values.
Usage
1 | computeMSIE(x.miss, x.impute, x.true)
|
Arguments
x.miss |
a |
x.impute |
an imputed data |
x.true |
complete/true data |
Value
value of MSIE
Examples
1 2 3 4 5 6 7 | set.seed(3)
x.true = matrix(rnorm(100),10,10)
## create 10% missing values in x
x.miss = artifNA(x.true, 0.10)
## impute using wNNSel method
x.impute = wNNSel.impute(x.miss)
computeMSIE(x.miss, x.impute, x.true)
|
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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