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

Generally, this function performs data standardization by an ID. It is useful for data pre-processing, by removing daily, monthly, or other periodic means which are not of interest. Two quick methods are available: removing the mean and standardization. Note, this is not the optimal method in the statistical literature for de-seasonalization.

1 | ```
deSeasonalize(dates, X, type = "daily", method = "deMean")
``` |

`dates` |
vector of dates or IDs for the data, |

`X` |
vector or matrix of all data to be de-seasonalized. Missing values are not allowed and the data should be numeric. The length, or number of rows, of |

`type` |
how often to de-seasonalize. Allowed values are |

`method` |
how de-seasonalization is implemented. Allowed values are |

For a supplied matrix, `X`

, the same `dates`

– ID criteria – is used for de-seasonalization in each column. That is, each column of `X`

is de-seasonalized by `dates`

.

This is not an optimal de-seasonalization methodology. For users interested in a more robust method, please see the '`deseasonalize`

' R package.

a vector or matrix of the same dimension as `X`

which has been de-seasonalized appropriately.

Megan Heyman

R package 'deseasonalize', http://cran.r-project.org/package=deseasonalize

1 2 3 4 5 6 7 | ```
ID <- as.Date(c("2014-05-14", "2013-06-20", "2013-05-14", "2012-06-20",
"1999-09-09", "1998-09-08", "1998-09-09", "1982-05-14",
"2000-09-08"))
someData <- seq(1, 9)
deSeasonalize(dates=ID, X=someData, type="daily", method="deMean")
deSeasonalize(dates=ID, X=someData, type="monthly", method="standardize")
``` |

```
[1] -3.0 -1.0 -1.0 1.0 -1.0 -1.5 1.0 4.0 1.5
[1] -0.8320503 -0.7071068 -0.2773501 0.7071068 -1.0246951 -0.4391550 0.1463850
[8] 1.1094004 1.3174651
```

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