impKNNa {robCompositions} | R Documentation |
This function offers several k-nearest neighbor methods for the imputation of missing values in compositional data.
impKNNa(x, method = "knn", k = 3, metric = "Aitchison", agg = "median", primitive = FALSE, normknn = TRUE, das = FALSE, adj="median")
x |
data frame or matrix |
method |
method (at the moment, only “knn” can be used) |
k |
number of nearest neighbors chosen for imputation |
metric |
“Aichison” or “Euclidean” |
agg |
“median” or “mean”, for the aggregation of the nearest neighbors |
primitive |
if TRUE, a more enhanced search for the $k$-nearest neighbors is obtained (see details) |
normknn |
An adjustment of the imputed values is performed if TRUE |
das |
depricated. if TRUE, the definition of the Aitchison distance, based on simple logratios of the compositional part, is used (Aitchison, 2000) to calculate distances between observations. if FALSE, a version using the clr transformation is used. |
adj |
either ‘median’ (default) or ‘sum’ can be chosen for the adjustment of the nearest neighbors, see Hron et al., 2010. |
The Aitchison metric
should be chosen when dealing with compositional data, the Euclidean metric
otherwise.
If primitive
== FALSE, a sequential search for the k-nearest neighbors
is applied for every missing value where all information corresponding to the
non-missing cells plus the information in the variable to be imputed plus some
additional information is available. If primitive
== TRUE, a search of the
k-nearest neighbors among observations is applied where in addition to the variable
to be imputed any further cells are non-missing.
If normknn
is TRUE (prefered option) the imputed cells from a nearest neighbor method are adjusted with special adjustment factors (more details can be found online (see the references)).
xOrig |
Original data frame or matrix |
xImp |
Imputed data |
w |
Amount of imputed values |
wind |
Index of the missing values in the data |
metric |
Metric used |
Matthias Templ
Aitchison, J. and Barcelo-Vidal, C. and Martin-Fernandez, J.A. and Pawlowsky-Glahn, V. (2000) Logratio analysis and compositional distance, Mathematical Geology 32(3):271-275.
Hron, K. and Templ, M. and Filzmoser, P. (2010) Imputation of missing values for compositional data using classical and robust methods Computational Statistics and Data Analysis, In Press, Corrected Proof, ISSN: 0167-9473, DOI:10.1016/j.csda.2009.11.023
data(expenditures) x <- expenditures x[1,3] x[1,3] <- NA xi <- impKNNa(x)$xImp xi[1,3]