Description Usage Arguments Details Value Note Author(s) References See Also Examples

write data files and initial .inp files to setup file directory system for running backwards selection using M Plus

1 2 3 4 5 6 | ```
Initialize(InitialData, NumImpute = 0, DataFileName = "NewData",
NameFile = "New", Directry = getwd(),
NADes = c(-99), startSeedImputations = 1000,
InputInitializeMat = "N", WhichCat = rep(1, ncol(InitialData)),
AllMethods = c("logreg", "polr", "pmm"), WhichImpute = rep(1, ncol(InitialData)),
WhichRowsImp = c(1:nrow(InitialData)), PasteIND = 1)
``` |

`InitialData` |
dataframe of initial data |

`NumImpute` |
the number of imputated datasets to create default is 0 for no imputation |

`DataFileName` |
the name of the .dat file to be created from the initial data or imputed datasets default is "NewData" |

`NameFile` |
the name of the folder where the .inp and .out files with the same base name are stored default is "New" |

`Directry` |
the path where the folder containing the .inp .out and .dat files are saved during backward selection |

`NADes` |
a list of length 1 with the value for missing data default is c(-99) |

`startSeedImputations` |
the seed for the first imputed data sets all other data sets seed is plus one from this value default is 1000 |

`InputInitializeMat` |
square dataframe where row name and column names are equal and are the variable names of InitialData with 1 for include path and 0 for exclude created by |

`WhichCat` |
list of length number of variables in InitialData with 1 for categorical and 0 for continuous variables default is all 1s for all categorical data |

`AllMethods` |
default is set at "logreg" for binary imputation "polr" for categorical imputation "pmm" for continuous |

`WhichImpute` |
list of length of names of InitialData specifying '1' for Impute column and '0' for no Imputation |

`WhichRowsImp` |
list of length of number of rows of InitialData specifying '1' for Impute row and '0' for no Imputation |

`PasteIND` |
a value of 1 indicates to use all possible indirect effects in modelling and a value of 0 is input to only use direct effects in modelling |

this function must be run before using any other of the important functions in order to create initial input files

Output is list of length 2:

`AllNames` |
list containing the names of the folders containing each imputed data set path analysis |

`AllDataNames` |
list containing the name of the imputed datasets which is just length one element if there are no imputations. Also files are written and saved. |

the PasteIND=0 option should be used when the set of variables is too large to specify all indirect effects. In selection, `AllBackwardSelect`

, if the m plus first .inp file fails to run due to a singularity issue, consider creating a new InputInitializeMat. See `CreateInitializeMatrix`

for the format of the InputInitializeMat, and consider replacing some 1 values for included paths with 0s to not include the path.
It is also very important that all variables start with a capital letter and contain only numbers and letters (no spaces or special characters) in this version

William Terry

`MplusAutomation`

and `mice`

1 2 3 4 5 6 7 | ```
## Not run:
InitD=Simulate(MissingYN=1)
xxx=Initialize(InitD,NumImpute=3,WhichCat=c(1,1,1,1,0))
## End(Not run)
``` |

PathSelectMP documentation built on May 2, 2019, 3:15 a.m.

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