Description Usage Arguments Details Value Author(s) Examples

‘rowimpPrep’ identifies all missingness patterns
within an incomplete data set. Running `rowimpPrep`

is a
prerequisite for `BBPMM.row`

.

1 | ```
rowimpPrep(data, ID=NULL, verbose=TRUE)
``` |

`data` |
Either a data frame or matrix with missing values. |

`ID` |
A numeric or character string vector indicating the column
positions or names of the ID variable (if two data sets were stacked
that have a joint subset of variables). The first element refers to
the 'donor ID', the second element refers to the 'recipient
ID'. This distinction is only of relevance, if the data set is 'L-shaped',
i.e. if the data contains only one missing-data pattern (where
incomplete cases are 'recipients'). If |

`verbose` |
Prints information on identified missing-data patterns. Default=TRUE. |

`rowimpPrep`

identifies
all patterns, and allows to decide, whether
to impute all missing-data patterns with `BBPMM.row`

or
just some of them. This comes in handy if variables that were assumed to be
completely observed have missing values. These variables are then
likely to define an unexpected 'block' of their own. Of course,
`BBPMM.row`

can be used to impute missing data that are
not missing-by-design as well, but `BBPMM`

would
probably be the better option. Note that all variables listed in
`compNames`

are used for the imputation model in
`BBPMM.row`

, i.e. completely observed variables (ID variables
aside) which are not to be used in the imputation model, have to be
removed from the data set beforehand.

`data` |
The original data set minus the ID variable(s). |

`key` |
The ID variable(s) from the original data set. |

`blocks` |
A list containing the column positions of all identified missing-data patterns. |

`blockNames` |
A list containing the variable names corresponding
to object |

`compNames` |
A character vector containing the variable names of the (completely observed) imputation model variables. |

`ignore` |
Contains positions of ignored variables. |

`ignored_data` |
Contains ignored variables. |

`indMatrix` |
A matrix with the same dimensions as the incomplete data containing flags for missing values. |

Florian Meinfelder, Thorsten Schnapp [ctb]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
### sample data set with non-normal variables and a single
### missingness pattern
set.seed(1000)
n <- 50
x1 <- round(runif(n,0.5,3.5))
x2 <- as.factor(c(rep(1,10),rep(2,25),rep(3,15)))
x3 <- round(rnorm(n,0,3))
y1 <- round(x1-0.25*(x2==2)+0.5*x3+rnorm(n,0,1))
y1 <- ifelse(y1<1,1,y1)
y1 <- ifelse(y1>4,5,y1)
y2 <- y1+rnorm(n,0,0.5)
y3 <- round(x3+rnorm(n,0,2))
data1 <- as.data.frame(cbind(x1,x2,x3,y1,y2,y3))
misrow1 <- sample(n,20)
is.na(data1[misrow1, c(4:6)]) <- TRUE
### preparation step
impblock <- rowimpPrep(data1)
impblock$blockNames
``` |

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