Bioinformatics Toolbox™

knnimpute - Impute missing data using nearest-neighbor method

Syntax

knnimpute(Data) knnimpute(Data, k) knnimpute(..., 'Distance', DistanceValue, ...) knnimpute(..., 'DistArgs', DistArgsValue, ...) knnimpute(..., 'Weights', WeightsValues, ...) knnimpute(..., 'Median', MedianValue, ...)

Arguments

`Data`
`k`

Description

knnimpute(Data) replaces NaNs in Data with the corresponding value from the nearest-neighbor column. The nearest-neighbor column is the closest column in Euclidean distance. If the corresponding value from the nearest-neighbor column is also NaN, the next nearest column is used.

knnimpute(Data, k) replaces NaNs in Data with a weighted mean of the k nearest-neighbor columns. The weights are inversely proportional to the distances from the neighboring columns.

knnimpute(..., 'PropertyName', PropertyValue, ...) calls knnimpute with optional properties that use property name/property value pairs. You can specify one or more properties in any order. Each PropertyName must be enclosed in single quotation marks and is case insensitive. These property name/property value pairs are as follows:

knnimpute(..., 'Distance', DistanceValue, ...) computes nearest-neighbor columns using the distance metric distfun. The choices for DistanceValue are:

`'euclidean'`	Euclidean distance (default).
`'seuclidean'`	Standardized Euclidean distance — each coordinate in the sum of squares is inversely weighted by the sample variance of that coordinate.
`'cityblock'`	City block distance.
`'mahalanobis'`	Mahalanobis distance.
`'minkowski'`	Minkowski distance with exponent 2.
`'cosine'`	One minus the cosine of the included angle.
`'correlation'`	One minus the sample correlation between observations, treated as sequences of values.
`'hamming'`	Hamming distance — the percentage of coordinates that differ.
`'jaccard'`	One minus the Jaccard coefficient — the percentage of nonzero coordinates that differ.
`'chebychev'`	Chebychev distance (maximum coordinate difference).
function handle	A handle to a distance function, specified using `@`, for example, `@distfun`.

See pdist for more details.

knnimpute(..., 'DistArgs', DistArgsValue, ...) passes arguments (DistArgsValue) to the function distfun. DistArgsValue can be a single value or a cell array of values.

knnimpute(..., 'Weights', WeightsValues, ...) lets you specify the weights used in the weighted mean calculation. w should be a vector of length k.

knnimpute(..., 'Median', MedianValue, ...) when MedianValue is true, uses the median of the k nearest neighbors instead of the weighted mean.

Example 1

A = [1 2 5;4 5 7;NaN -1 8;7 6 0]

A =

     1     2     5
     4     5     7
   NaN    -1     8
     7     6     0

Note that A(3,1) = NaN. Because column 2 is the closest column to column 1 in Euclidean distance, knnimpute imputes the (3,1) entry of column 1 to be the corresponding entry of column 2, which is -1.

knnimpute(A)

ans =

     1     2     5
     4     5     7
    -1    -1     8
     7     6     0

Example 2

The following example loads the data set yeastdata and imputes missing values in the array yeastvalues:

load yeastdata
% Remove data for empty spots
emptySpots = strcmp('EMPTY',genes);
yeastvalues(emptySpots,:) = [];
genes(emptySpots) = [];
% Impute missing values
imputedValues = knnimpute(yeastvalues);

References

[1] Speed, T. (2003). Statistical Analysis of Gene Expression Microarray Data (Chapman & Hall/CRC).

[2] Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P., and Botstein, D. (1999). "Imputing missing data for gene expression arrays", Technical Report, Division of Biostatistics, Stanford University.

[3] Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., and Altman, R. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525.