coreInfluence {Bchron} | R Documentation |

## Find the influence of dates in a pair of Bchronology runs across the core

### Description

This function takes as input two `Bchronology`

runs and compares the uncertainty intervals. It does this by
computing the mean uncertainty across the core (`type = 'mean'`

) at a specified percentile level (e.g. 95%) and
subsequently reporting the reduction/increase in uncertainty between the two runs. Both cores must
have the same set of depths/positions at regular intervals.

### Usage

```
coreInfluence(
bchrRun1,
bchrRun2,
percentile = 0.95,
type = c("plot", "summary", "max"),
ageTolerance = 500,
...
)
```

### Arguments

`bchrRun1` |
The output of a run of the |

`bchrRun2` |
The output of another run of the |

`percentile` |
The value of the percentile to compare the uncertainties. Default is 95% |

`type` |
if |

`ageTolerance` |
A value in years for which to report the positions at which the reduction in uncertainty exceeds this value. |

`...` |
Additional arguments to plot |

### Details

For example, if the `ageTolerance`

value is 500 years, then `coreInfluence`

will return all of the positions at
which the uncertainty reduction is bigger than 500.

### Value

Depending on type will outputs some text and plots providing the influence values for the cores in question.

### See Also

`Bchronology`

, `choosePositions`

, `dateInfluence`

for finding the influence of removing a single date from a core

### Examples

```
data(Glendalough)
# Start with a run that remove two dates
GlenOut1 <- Bchronology(
ages = Glendalough$ages[-c(3:4)],
ageSds = Glendalough$ageSds[-c(3:4)],
calCurves = Glendalough$calCurves[-c(3:4)],
positions = Glendalough$position[-c(3:4)],
positionThicknesses = Glendalough$thickness[-c(3:4)],
ids = Glendalough$id[-c(3:4)],
predictPositions = seq(0, 1500, by = 10)
)
GlenOut2 <- Bchronology(
ages = Glendalough$ages,
ageSds = Glendalough$ageSds,
calCurves = Glendalough$calCurves,
positions = Glendalough$position,
positionThicknesses = Glendalough$thickness,
ids = Glendalough$id,
predictPositions = seq(0, 1500, by = 10)
)
# Now compare their influence
coreInfluence(GlenOut1,
GlenOut2,
type = c("max", "plot"),
xlab = "Age (cal years BP)",
ylab = "Depth (cm)",
main = "Chronology difference at 95% for
Glendalough removing two dates",
las = 1
)
```

*Bchron*version 4.7.6 Index]