cv.wNNSel: Cross Validation for wNNSel Imputation
In wNNSel: Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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)

`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 λ
`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.

Some values are artificially deleted and wNNSel is run multiple times, varying λ and m. For each pair of λ 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).

a list containing

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

Shahla Faisal <shahla_ramzan@yahoo.com>

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.

artifNA.cv, wNNSel

 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)