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

Error properties of estimates derived from imputation differ from those of regression-based estimates because the two methods include a different mix of error components. This function computes a partitioning of error statistics as proposed by Stage and Crookston (2007).

1 | ```
errorStats(mahal,...,scale=FALSE,pzero=0.1,plg=0.5,seeMethod="lm")
``` |

`mahal` |
An object of class |

`...` |
Other objects of class |

`scale` |
When |

`pzero` |
The lower tail p-value used to pick |

`plg` |
The upper tail p-value used to pick |

`seeMethod` |
Method used to compute |

See http://www.treesearch.fs.fed.us/pubs/28385

A list that contains several data frames. The column names of each are a combination of the name of the object used to compute the statistics and the name of the statistic. The rownames correspond the the Y-variables from the first argument. The data frame names are as follows:

`common` |
statistics used to compute other statistics. |

`name of first argument` |
error statistics for the first |

`names of ... arguments` |
error statistics for each of the remaining |

`see` |
standard error of estimate for individual regressions fit for corresponding Y-variables. |

`rmmsd0` |
root mean square difference for imputations based on |

`mlf` |
square root of the model lack of fit: |

`rmsd` |
root mean square error. |

`rmsdlg` |
root mean square error of the observations with larger distances. |

`sei` |
standard error of imputation |

`dstc` |
distance component: |

Note that unlike Stage and Crookston (2007), all statistics reported here are in the natural units, not squared units.

Nicholas L. Crookston [email protected]

Albert R. Stage [email protected]

Stage, A.R.; Crookston, N.L. (2007). Partitioning error components
for accuracy-assessment of near neighbor methods of imputation.
*For. Sci.* 53(1):62-72.
http://www.treesearch.fs.fed.us/pubs/28385

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
require (yaImpute)
data(TallyLake)
diag(cov(TallyLake[,1:8])) # see col A in Table 3 in Stage and Crookston
mal=yai(x=TallyLake[,9:29],y=TallyLake[,1:8],
noTrgs=TRUE,method="mahalanobis")
msn=yai(x=TallyLake[,9:29],y=TallyLake[,1:8],
noTrgs=TRUE,method="msn")
# variable "see" for "mal" matches col B (when squared and scaled)
# other columns don't match exactly as Stage and Crookston used different
# software to compute values
errorStats(mal,msn)
``` |

yaImpute documentation built on Dec. 27, 2018, 3 a.m.

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