Description Usage Arguments Format Details Value Note References See Also Examples

Function to fill in missing time series data.

1 2 |

`dataset` |
is a data frame in the format of the data frame returned by importDVs, with missing values indicated by NA. |

`block` |
is the size of the largest block of missing data that the function will fill-in. |

`pmiss` |
is the maximum amount of the missing data that can be missing in the dataset for fill-in procedure to be performed. |

`model` |
is the type of structural time series model, see StructTS. The default value is trend. If level is used, the results of fillMiss, which by default applies a fixed-interval smoothing to the time series, tsSmooth, will be very close to linear interpolation. |

`smooth` |
a logical that indicates whether or not to apply tsSmooth to the structured time series. |

`...` |
further arguments to be passed to plotting method (see par). |

The returned data frame has the following columns:

Name | Type | Description |

staid | factor | USGS station identification number |

val | numeric | The value of the hydrologic variable |

dates | Date | Date of daily value |

qualcode | factor | Qualification code |

This function will check the percent of missing values and the size of the largest missing block of data. By default, if less than 40 percent of the data are missing and the largest block is less than 30 days, the data will be filled-in by using a structural time series, StructTS from the base stats package in R (R Development Core Team, 2012). The fitted structural time series is then smoothed via a state-space model, tsSmooth from the base stats package in R.

a data frame with NAs in the "val" column replaced by estimated values and a plot showing observed and estimated data. If there are too many missing values, based on default or user defined limits, the unaltered dataset is returned as well as a message, such as "Error in fillMiss(misQ05054000) : Too much missing data. Cannot fill in missing values."

Many methods have been suggested for estimating missing hydrological data. However, experiments showed that the functions in the base stats package worked very well if the blocks of missing data were not long. Users with larger blocks of missing data may want to explore other methods including using nearby gages to estimate missing values at a streamgage. Additional methods for filling in missing hydrological data are summarized in Beauchamp and others (1989) and Elshorbagy and others (2000).

To indicate which values have been replaced, the qualcode field is populated with 'fM' for those values that were estimated using the fillMiss function.

Beauchamp, J.J., 1989, Comparison of regression and time-series methods for synthesizing missing streamflow records: Water Resources Bulletin, v. 25, no. 5, p. 961–975.

Elshorbagy, A.A., Panu, U.S., Simonovic, S.P., 2000, Group-based estimation of missing hydrological data—I. Approach and general methodology: Hydrological Sciences Journal, v. 45, no. 6, p. 849–866.

R Development Core Team, 2012, R—A language and environment for statistical computing: Vienna, Austria, R Foundation for Statistical Computing, [ISBN 3-900051-07-0]. (Also available at https://www.r-project.org.)

StructTS, tsSmooth, cleanUp

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(exampleWaterData)
my.newdata <- fillMiss(misQ05054000, block=30, pmiss=50, log="y")
summary(misQ05054000)
summary(my.newdata)
# ph example
## Not run:
pH05082500<-importDVs("05082500", code="00400", stat="00008",
sdate="2000-01-01", edate="2011-12-31")
plotParam(pH05082500)
pHfilled<-fillMiss(pH05082500, block=45, ylim=c(7.5,9), yaxs="i")
## End(Not run)
``` |

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