multi.impute | R Documentation |

`multi.impute`

performs multiple imputation on a
given quantitative proteomics dataset.

```
multi.impute(data, conditions, nb.imp = NULL, method, parallel = FALSE)
```

`data` |
A quantitative matrix to be imputed, with proteins/peptides in rows and samples in columns. |

`conditions` |
A vector of length the number of samples where each element corresponds to the condition the sample belongs to. |

`nb.imp` |
The number of imputation to perform. |

`method` |
A single character string describing the imputation method to be used. See details. |

`parallel` |
Logical, whether or not use parallel computing
(with |

Multiple imputation consists in imputing several times a given
dataset using a given method. Here, imputation methods can be chosen either
from `mice`

, `imp4p-package`

or
`impute.knn`

:

"pmm", "midastouch", "sample", "cart", "rf","mean", "norm", "norm.nob", "norm.boot", "norm.predict": imputation methods as described in

`mice`

."RF" imputes missing values using random forests algorithm as described in

`impute.RF`

."MLE" imputes missing values using maximum likelihood estimation as described in

`impute.mle`

."PCA" imputes missing values using principal component analysis as described in

`impute.PCA`

."SLSA" imputes missing values using structured least squares algorithm as described in

`impute.slsa`

."kNN" imputes missing values using k nearest neighbors as described in

`impute.knn`

.

A numeric array of dimension c(dim(data),nb.imp).

M. Chion, Ch. Carapito and F. Bertrand (2021). *Accounting for multiple
imputation-induced variability for differential analysis in mass
spectrometry-based label-free quantitative proteomics*. arxiv:2108.07086.
https://arxiv.org/abs/2108.07086.

```
library(mi4p)
data(datasim)
multi.impute(data = datasim[,-1], conditions = attr(datasim,"metadata")$Condition, method = "MLE")
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.