Package 'wNNSel' reference manual

Title:	Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables
Description:	New tools for the imputation of missing values in high-dimensional data are introduced using the non-parametric nearest neighbor methods. It includes weighted nearest neighbor imputation methods that use specific distances for selected variables. It includes an automatic procedure of cross validation and does not require prespecified values of the tuning parameters. It can be used to impute missing values in high-dimensional data when the sample size is smaller than the number of predictors. For more information see Faisal and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.
Authors:	Shahla Faisal
Maintainer:	Shahla Faisal <[email protected]>
License:	GPL-2
Version:	0.1
Built:	2025-02-06 03:24:37 UTC
Source:	https://github.com/cran/wNNSel

Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables

Description

This package introduces new non-parametric tools for the imputation of missing values in high-dimensional data. It includes weighted nearest neighbor imputation methods that use distances for selected covariates. The careful selection of distances that carry information about the missing values yields an imputation tool. It does not require pre-specified $k$ , unlike other kNN methods. It can be used to impute missing values in high-dimensional data when $n<p$ .

Details

Package:	wNNSel
Version:	0.1
Date:	2017-11-08
Depends:	R (>= 2.10)
License:	GPL (>= 2)

The main function of the package is wNNSel for implementing the nonparameteric procedure of nearest neighbors imputaiton. See wNNSel for more details.

Note

*Author's Last name changed to Faisal from Ramzan in 2016.

Author(s)

Shahla Faisal <[email protected]>

References

Tutz, G. and Ramzan,S*. (2015). Improved methods for the imputation of missing data by nearest neighbor methods. Computational Statistics and Data Analysis, Vol. 90, pp. 84-99.

Faisal, S.* and Tutz, G. (2017). Missing value imputation for gene expression data by tailored nearest neighbors. Statistical Application in Genetics and Molecular Biology. Vol. 16(2), pp. 95-106.

Introduce MCAR Missing Values in a matrix

Description

This function artificially introduces missing values in a data matrix under missing completely at random (MCAR) mechanism.

Usage

artifNA(x, miss.prop = 0.1)
artifNA(x, miss.prop = 0.1)

Arguments

`x`	a matrix, in which missing values are to be created.
`miss.prop`	proportion of missing values

Value

a matrix with missing values

Examples

 set.seed(3)
 x = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 artifNA(x, 0.10)
set.seed(3)
 x = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 artifNA(x, 0.10)

Introduce MCAR Missing Values in a matrix for cross validation

Description

This function introduces additional missing values in a missing data matrix artificially. The missing values are introduced under missing completely at random (MCAR) mechanism.

Usage

artifNA.cv(x, testNA.prop = 0.1)
artifNA.cv(x, testNA.prop = 0.1)

Arguments

`x`	a matrix, in which missing values are to be created.
`testNA.prop`	proportion of missing values

Value

a list contatining a matrix with artifical missing values, removed indices and the provided x matrix

Examples

 set.seed(3)
 x = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 x.miss<- artifNA(x, 0.10)
 ## create another 10% missing values in x
 x.miss.cv<- artifNA.cv(x, 0.10)
 summary(x.miss)
 summary(x.miss.cv)
set.seed(3)
 x = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 x.miss<- artifNA(x, 0.10)
 ## create another 10% missing values in x
 x.miss.cv<- artifNA.cv(x, 0.10)
 summary(x.miss)
 summary(x.miss.cv)

Mean Absolute Imputation Error

Description

This function computes the mean absolute imputation error for a given complete/true data matrix, imputed data matrix and the data matrix with missing values.

Usage

computeMAIE(x.miss, x.impute, x.true)
computeMAIE(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

  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)
  computeMAIE(x.miss, x.impute, x.true)
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)
  computeMAIE(x.miss, x.impute, x.true)

Mean Squared Imputation Error

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

computeMSIE(x.miss, x.impute, x.true)
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

 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)
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)

Normalized Root Mean Squared Imputatoin Error

Description

This function computes the nrmalized root mean squared imputation error for a given complete/true data matrix, imputed data matrix and the data matrix with missing values.

Usage

computeNRMSE(x.miss, x.impute, x.true)
computeNRMSE(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

  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)
  computeNRMSE(x.miss, x.impute, x.true)
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)
  computeNRMSE(x.miss, x.impute, x.true)

Cross Validation for wNNSel Imputation

Description

This function aims to search for optimal values of the tuning parameters for the wNNSel imputation.

Usage

cv.wNNSel(x, kernel = "gaussian", x.dist = "euclidean", method = "2",
  m.values = seq(2, 8, by = 2), c.values = seq(0.1, 0.5, by = 0.1),
  lambda.values = seq(0, 0.6, by = 0.01)[-1], times.max = 5,
  testNA.prop = 0.05)
cv.wNNSel(x, kernel = "gaussian", x.dist = "euclidean", method = "2",
  m.values = seq(2, 8, by = 2), c.values = seq(0.1, 0.5, by = 0.1),
  lambda.values = seq(0, 0.6, by = 0.01)[-1], times.max = 5,
  testNA.prop = 0.05)

Arguments

`x`	a `matrix` containing missing values
`kernel`	kernel function to be used in nearest neighbors imputation. Default kernel function is "gaussian".
`x.dist`	distance to compute, The default is `x.dist="euclidean"` to compute Euclidean distance. Set `x.dist` to `NULL` to use Manhattan distance.
`method`	convex function, performs selection of variables. If `method="1"`, linear function is used and when if `method="c"`, power function is used.
`m.values`	a `vector` of integer values, required when mehtod="2".
`c.values`	a `vector` between 0 and less than 1. It is required when mehtod="1".
`lambda.values`	a `vector`, for the tuning parameter $\lambda$
`times.max`	maximum number of repititions for the cross validation procedure.
`testNA.prop`	proportion of values to be deleted artificially for cross validation in the missing matrix `x`. Default method uses 5 percent.

Details

Some values are artificially deleted and wNNSel is run multiple times, varying $\lambda$ and $m$ . For each pair of $\lambda$ and $m$ , compute MSIE on the subset of the data matrix x for which the the values were deleted artificially. (See References for more detail).

Value

a list containing

`lambda.opt`	optimal parameter selected by cross validation
`m.opt`	optimal parameter selected by cross validation
`MSIE.cv`	cross validation error

Author(s)

Shahla Faisal <[email protected]>

References

Tutz, G. and Ramzan,S. (2015). Improved methods for the imputation of missing data by nearest neighbor methods. Computational Statistics and Data Analysis, Vol. 90, pp. 84-99.

Faisal, S. and Tutz, G. (2017). Missing value imputation for gene expression data by tailored nearest neighbors. Statistical Application in Genetics and Molecular Biology. Vol. 16(2), pp. 95-106.

Examples

 set.seed(3)
 x.true = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 x.miss = artifNA(x.true, 0.10)
 ## use cross validation to find optimal values
 result = cv.wNNSel(x.miss)
 ## optimal values are
 result$lambda.opt
 result$m.opt
 ## Now use these values to get final imputation
 x.impute = wNNSel.impute(x.miss, lambda=result$lambda.opt, m=result$m.opt)
 ## and final MSIE
 computeMSIE(x.miss, x.impute, x.true)
set.seed(3)
 x.true = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 x.miss = artifNA(x.true, 0.10)
 ## use cross validation to find optimal values
 result = cv.wNNSel(x.miss)
 ## optimal values are
 result$lambda.opt
 result$m.opt
 ## Now use these values to get final imputation
 x.impute = wNNSel.impute(x.miss, lambda=result$lambda.opt, m=result$m.opt)
 ## and final MSIE
 computeMSIE(x.miss, x.impute, x.true)

Imputatin using wNNSel method.

Description

'wNNSel' is used to impute the missing values particularly in high dimensional data. It uses a cross validation procedure for selecting the best values of the tuning parameters. It also works when the samples are smaller than the covariates.

Usage

wNNSel(x, x.initial = NULL, x.true = NULL, k, useAll = TRUE,
  x.dist = "euclidean", kernel = "gaussian", method = "2", impute.fn,
  convex = TRUE, m.values = seq(2, 8, by = 2), c.values = seq(0.1, 0.5, by
  = 0.1), lambda.values = seq(0, 0.6, by = 0.01)[-1], times.max = 5,
  testNA.prop = 0.05, withinFolds = FALSE, folds, verbose = TRUE)
wNNSel(x, x.initial = NULL, x.true = NULL, k, useAll = TRUE,
  x.dist = "euclidean", kernel = "gaussian", method = "2", impute.fn,
  convex = TRUE, m.values = seq(2, 8, by = 2), c.values = seq(0.1, 0.5, by
  = 0.1), lambda.values = seq(0, 0.6, by = 0.01)[-1], times.max = 5,
  testNA.prop = 0.05, withinFolds = FALSE, folds, verbose = TRUE)

Arguments

`x`	a numeric data `matrix` containing missing values
`x.initial`	an optional. A complete data matrix e.g. using mean imputation of `x`. If provided, it will be used for the computation of correlations.
`x.true`	a matrix of true or complete data. If provided, `MSIE` will be returned in the results list.
`k`	an optional, the number of nearest neighbors to use for imputation.
`useAll`	`logical`. If `TRUE`, all available neighbors are used for the imputation.
`x.dist`	distance to compute. The default is `x.dist="euclidean"`, that uses the Euclidean distance. Set `x.dist` to `NULL` for Manhattan distance.
`kernel`	kernel function to be used in nearest neighbors imputation. Default kernel function is "gaussian".
`method`	convex function, performs selection of variables. If `method="1"`, linear function is used and the power function is used when `method="2"`.
`impute.fn`	the imputation function to run on the length k vector of values for a missing feature. Defaults to a weighted mean of the neighboring values, weighted by the specified `kernel`. If not specified then wNN imputation will be used by default.
`convex`	logical. If `TRUE`, selected variables are used for the computation of distance. The default is `TRUE`.
`m.values`	a `vector` of integer values, required when mehtod="2".
`c.values`	a `vector` between 0 and less than 1. It is required when mehtod="1".
`lambda.values`	a `vector`, for the tuning parameter $\lambda$
`times.max`	maximum number of repititions for the cross validation procedure.
`testNA.prop`	proportion of values to be deleted artificially for cross validation in the missing matrix `x`. Default method uses 5 percent.
`withinFolds`	`logical`. Use only if the neighbors/rows belong to particular folds/groups. Default is set to `FALSE`.
`folds`	a `list` of vectors specifying folds/groups for neighbors. lenght of list is equal to the number of folds/groups. Each element/vector of the list indicates row indices belonging to that particular group/fold.
`verbose`	logical. If `TRUE`, prints status updates

Details

For each sample, identify missinng features. For each missing feature find the nearest neighbors which have that feature. Impute the missing value using the imputation function on the selected vector of values found from the neighbors. By default the wNNSel method automatically searches for optimal values for a given data matrix.

The default method uses x.dist="euclidean" including selected covariates. The specific distancs are computed using important covariates only. If mehtod="1", the linear function in absolute value of $r$ is used, defined by

$\frac{|r|}{1-c} - \frac{c}{1-c},$

for $|r|>c$ , and, 0 , otherwise. By default, the power function $|r|^m$ is used when mehtod="2". For more detailed discussion, see references.

Value

a list containing imputed data matrix, and cross validation results

`x.impute`	imputed data matrix
`MSIE`	True error. Note it is only available when x.true is provided.
`lambda.opt`	optimal parameter selected by cross validation
`m.opt`	optimal parameter selected by cross validation
`MSIE.cv`	cross validation error

References

Tutz, G. and Ramzan,S. (2015). Improved methods for the imputation of missing data by nearest neighbor methods. Computational Statistics and Data Analysis, Vol. 90, pp. 84-99.

Faisal, S. and Tutz, G. (2017). Missing value imputation for gene expression data by tailored nearest neighbors. Statistical Application in Genetics and Molecular Biology. Vol. 16(2), pp. 95-106.

Examples

 set.seed(3)
 x.true = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 x.miss = artifNA(x.true, 0.10)
 ## imputed matrix
 result <- wNNSel(x.miss)
 result$x.impute
 ## cross validation result can be accessed using
 result$cross.val
set.seed(3)
 x.true = matrix(rnorm(100),10,10)
 ## create 10% missing values in x
 x.miss = artifNA(x.true, 0.10)
 ## imputed matrix
 result <- wNNSel(x.miss)
 result$x.impute
 ## cross validation result can be accessed using
 result$cross.val

Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables

Description

This function imputes the missing values using user-spefied values of the tuning parameters. It also works when the samples are smaller than the covariates.

Usage

wNNSel.impute(x, k, useAll = TRUE, x.initial = NULL, x.dist = "euclidean",
  kernel = "gaussian", lambda = 0.3, impute.fn, convex = TRUE,
  method = "2", m = 2, c = 0.3, withinFolds = FALSE, folds,
  verbose = TRUE, verbose2 = FALSE)
wNNSel.impute(x, k, useAll = TRUE, x.initial = NULL, x.dist = "euclidean",
  kernel = "gaussian", lambda = 0.3, impute.fn, convex = TRUE,
  method = "2", m = 2, c = 0.3, withinFolds = FALSE, folds,
  verbose = TRUE, verbose2 = FALSE)

Arguments

`x`	a `matrix` containing missing values
`k`	an optional, the number of nearest neighbors to use for imputation.
`useAll`	`logical`. The default is `useALL=TRUE`, that is, all available neighbors are used for the imputation.
`x.initial`	an optional. A complete data matrix e.g. using mean imputation of `x`. If provided, it will be used for the computation of correlations.
`x.dist`	distance to compute. The default is `x.dist="euclidean"`, that uses the Euclidean distance. Set `x.dist` to `NULL` for Manhattan distance.
`kernel`	kernel function to be used in nearest neighbors imputation. Default kernel function is "gaussian".
`lambda`	`scaler`, a tuning parameter
`impute.fn`	the imputation function to run on the length k vector of values for a missing feature. Defaults to a weighted mean of the neighboring values, weighted by the specified `kernel`. If not specified then wNN imputation will be used by default.
`convex`	logical. If `TRUE`, selected variables are used for the computation of distance. The default is `TRUE`.
`method`	convex function, performs selection of variables. If `method="1"`, linear function is used and the power function is used when `method="2"`.
`m`	`scaler`, a tuning parameter required by the power function.
`c`	`scaler`, a tuning parameter required by the linear function.
`withinFolds`	`logical`. Use only if the neighbors/rows belong to particular folds/groups. Default is set to `FALSE`.
`folds`	a `list` of vectors specifying folds/groups for neighbors. lenght of list is equal to the number of folds/groups. Each element/vector of the list indicates row indices belonging to that particular group/fold.
`verbose`	logical. If `TRUE`, prints status updates
`verbose2`	logical. If `TRUE`, prints status updates with more detail

Details

Value

imputed data matrix

Examples

  set.seed(3)
  x = matrix(rnorm(100),10,10)
  x.miss = x > 1
  x[x.miss] = NA
  wNNSel.impute(x)
  wNNSel.impute(x, lambda=0.5, m=2)
set.seed(3)
  x = matrix(rnorm(100),10,10)
  x.miss = x > 1
  x[x.miss] = NA
  wNNSel.impute(x)
  wNNSel.impute(x, lambda=0.5, m=2)

Package 'wNNSel'

Help Index

Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables

Description

Details

Note

Author(s)

References

Introduce MCAR Missing Values in a matrix

Description

Usage

Arguments

Value

Examples

Introduce MCAR Missing Values in a matrix for cross validation

Description

Usage

Arguments

Value

See Also

Examples

Mean Absolute Imputation Error

Description

Usage

Arguments

Value

Examples

Mean Squared Imputation Error

Description

Usage

Arguments

Value

Examples

Normalized Root Mean Squared Imputatoin Error

Description

Usage

Arguments

Value

Examples

Cross Validation for wNNSel Imputation

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Imputatin using wNNSel method.

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables

Description

Usage

Arguments

Details

Value

See Also

Examples