# Delete locations to harmonize the sampling frequency and time duration

### Description

Standardize the sampling frequency and duration across individuals in a LoCoH-xy object by deleting points

### Usage

1 2 3 4 |

### Arguments

`lxy` |
A LoCoH-xy object |

`id` |
The id value(s) to be harmonized |

`trim.ends` |
Truncate points from either end of the timeline to achieve a common time window, T/F |

`dt.start` |
The starting date-time that all individual trajectories will be truncated to. If |

`dt.end` |
The end date-time that all individual trajectories will be truncated to. If |

`byfreq` |
Delete points to achieve a common sampling frequency ( |

`samp.freq` |
The common time step for the output (in seconds). Can also be set to |

`lcm.round` |
When |

`lcm.max.iter` |
The maximum number of iterations to be used in the algorithm that finds the least common multiple of the median time steps |

`status` |
Show messages, T/F |

`dt.int.round.to` |
The proportion of the median sampling frequency that time intervals will be rounded to when computing the frequency table of sampling intervals (no change is made to the time stamps) |

`tau.diff.max` |
The maximum deviation from tau (the median delta.t of the entire dataset), expressed as a proportion of tau, that time difference between two points must fall for the distance between those two points to be included in the calculation of the median step length |

### Value

a LoCoH-xy object

### Note

This function processes a LoCoH-xy object that contains movement data for several individuals, and removes points such that the output contains a fixed start and end date for each individual, as well as an approximately uniform sampling frequency (time step).

Before using this function, you should clean your data of all abnormally short time intervals (e.g., bursts). See `lxy.thin.bursts`

.

If you know the time interval the data was *supposed* to be sampled (e.g., every 20 minutes), you should
pass that value for `samp.freq`

(expressed in seconds). If `samp.freq="lcm"`

, the function will
automatically compute the common time step for the individuals by taking the least common multiple
of the median time steps of each individual. You can deal with noise by rounding the median sampling interval to
the value of `lcm.round`

(expressed in seconds).

The function `lxy.plot.freq`

can help you see the 'actual' sampling intervals in the data (set `by.date=TRUE`

).

Because this function deletes points, the nearest-neighbors lookup table of the LoCoH-xy object (if any) will be deleted.

### See Also

`xyt.lxy`

, `lxy.plot.freq`

, `lxy.thin.bursts`