Description Usage Arguments Value References Examples

Impute the missing values in an FIA experiment using a Weighted K-Nearest Neighbours on Truncated Distribution described by Jasmit S. Shah et al.

1 2 3 | ```
## S4 method for signature 'proFIAset'
impute.KNN_TN(object, k = 0.6, classes = c("split",
"unique"))
``` |

`object` |
A proFIAset object. |

`k` |
The number of neighbors considered, can be a fraction then in this case the k will be taken for each class as the frac of the effective of the class. 3 at minima because comparison is based on correlation. |

`classes` |
how to handle imputation for different classes, if 'split', the classes are taken separately, if 'unique', the imputation is done on the full data matrix. |

A proFIAset object with the missing values imputated.

Distribution based nearest neighbor imputation for truncated high dimensional data with applications to pre-clinical and clinical metabolomics studies, J.S Shah 2017, BMC Bioinformatics.

1 2 3 4 5 6 7 | ```
if(require(plasFIA)){
data(plasSet)
###Reinitializing the data matrix
plasSet<-makeDataMatrix(plasSet,maxo=FALSE)
plasSet<-impute.KNN_TN(plasSet,2)
}
``` |

adelabriere/proFIA documentation built on July 12, 2019, 5:46 a.m.

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