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#' knn missing value imputation
#'
#' applies a k-nearest neighbour approach to impute missing values
#' @export knn_impute
#' @import pmp
knn_impute<-setClass(
"knn_impute",
contains = c('method'),
slots=c(params.neighbours='entity',
params.row_max='entity',
params.col_max='entity',
outputs.imputed='entity'
),
prototype=list(name = 'kNN missing value imputation',
description = 'k-nearest neighbour missing value imputation.',
type = 'normalisation',
predicted = 'imputed',
params=c('neighbours','row_max','col_max'),
outputs=c('imputed'),
params.neighbours=entity(name = 'Number of neighbours',
description = 'The number of neighbours (k) to use ofr imputation of missing values.',
value = 5,
type='numeric'),
params.neighbours=entity(name = 'Maximum percent per sample',
description = 'The maximum percent missing values per sample',
value = 50,
type='numeric'),
params.neighbours=entity(name = 'Maximum percent per feature',
description = 'The maximum percent missing values per feature',
value = 50,
type='numeric'),
outputs.imputed=entity(name = 'Imputed dataset',
description = 'A dataset object containing the data where missing values have been imputed.',
type='dataset',
value=dataset()
)
)
)
#' @export
setMethod(f="method.apply",
signature=c("knn_impute","dataset"),
definition=function(M,D)
{
opt=param.list(M)
smeta=dataset.sample_meta(D)
x=dataset.data(D)
imputed = mv_imputation(as.matrix(x),method='knn',k = opt$neighbours,rowmax=opt$row_max/100,colmax=opt$col_max/100,maxp = NULL)
dataset.data(D) = as.data.frame(imputed)
output.value(M,'imputed') = D
return(M)
}
)
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