makeNaData {MSnbase} | R Documentation

## Create a data with missing values

### Description

These functions take an instance of class `"MSnSet"` and sets randomly selected values to `NA`.

### Usage

    makeNaData(object, nNA, pNA, exclude)

    makeNaData2(object, nRows, nNAs, exclude)

    whichNA(x)

### Arguments

`object` | An instance of class `MSnSet`.
`nNA` | The absolute number of missing values to be assigned.
`pNA` | The proportion of missing values to be assignmed.
`exclude` | A `vector` to be used to subset `object`, defining rows that should not be used to set `NA`s.
`nRows` | The number of rows for each set.
`nNAs` | The number of missing values for each set.
`x` | A `matrix` or an instance of class `MSnSet`.

### Details

`makeNaData` randomly selects a number `nNA` (or a proportion `pNA`) of cells in the expression matrix to be set to `NA`.

`makeNaData2` will select `length(nRows)` sets of rows from `object`, each with `nRows[i]` rows respectively. The first set will be assigned `nNAs[1]` missing values, the second `nNAs[2]`, ... As opposed to `makeNaData`, this permits to control the number of `NAs` per rows.

The `whichNA` can be used to extract the indices of the missing values, as illustrated in the example.

### Value

An instance of class `MSnSet`, as `object`, but with the appropriate number/proportion of missing values. The returned object has an additional feature meta-data columns, `nNA`

### Author(s)

Laurent Gatto

### Examples

    ## Example 1
    library(pRolocdata)
    data(dunkley2006)
    sum(is.na(dunkley2006))
    dunkleyNA <- makeNaData(dunkley2006, nNA = 150)
    processingData(dunkleyNA)
    sum(is.na(dunkleyNA))
    table(fData(dunkleyNA)$nNA)
    naIdx <- whichNA(dunkleyNA)
    head(naIdx)
    ## Example 2
    dunkleyNA <- makeNaData(dunkley2006, nNA = 150, exclude = 1:10)
    processingData(dunkleyNA)
    table(fData(dunkleyNA)$nNA[1:10])
    table(fData(dunkleyNA)$nNA)
    ## Example 3
    nr <- rep(10, 5)
    na <- 1:5
    x <- makeNaData2(dunkley2006[1:100, 1:5],
                     nRows = nr,
                     nNAs = na)
    processingData(x)
    (res <- table(fData(x)$nNA))
    stopifnot(as.numeric(names(res)[-1]) ==  na)
    stopifnot(res[-1] ==  nr)
    ## Example 3
    nr2 <- c(5, 12, 11, 8)
    na2 <- c(3, 8, 1, 4)
    x2 <- makeNaData2(dunkley2006[1:100, 1:10],
                      nRows = nr2,
                      nNAs = na2)
    processingData(x2)
    (res2 <- table(fData(x2)$nNA))
    stopifnot(as.numeric(names(res2)[-1]) ==  sort(na2))
    stopifnot(res2[-1] ==  nr2[order(na2)])
    ## Example 5
    nr3 <- c(5, 12, 11, 8)
    na3 <- c(3, 8, 1, 3)
    x3 <- makeNaData2(dunkley2006[1:100, 1:10],
                      nRows = nr3,
                      nNAs = na3)
    processingData(x3)
    (res3 <- table(fData(x3)$nNA))

[Package MSnbase version 2.4.2 Index]