Description Usage Arguments Value References See Also Examples

Compute a MSE approximation for area predictors. The structure of interest is an isotropic planar random compact set. The sampling device is a uniform random lattice of figures (point patterns, line segments, quadrats...). The approximation depends only on sampling parameters and on the mean perimeter (to be provided) of the structure.

1 | ```
area.mse(x, B = 1, L = 3)
``` |

`x` |
a lattice of figures, object of class |

`B` |
the mean perimeter. Default: 1. |

`L` |
an integer, the criterion for stopping summation of the
Epstein zeta function. Argument of the function |

The MSE approximation as a numeric.

Kieu, K. and Mora, M. (2006). Precision of stereological
planar area predictors. *J. Microsc.*, 222(3), 201-211.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# Sampling by a unit hexagonal point lattice
area.mse(PPHexLat2())
# Sampling by a unit square point lattice
area.mse(PPRectLat2())
# Sampling by a lattice of point patterns
area.mse(PPRectLat2(n=5,hp=0.1))
# Sampling by quadrats (may be slow)
## Not run: area.mse(QRectLat2(hq=0.5,vq=0.7))
# Sampling by a square lattice of segments (may be slow)
## Not run: area.mse(SRectLat2(end=c(0.5,0.1))
# Sampling by an hexagonal lattice of segments (may be slow)
## Not run: area.mse(SHexLat2(end=c(0.2,0.15)))
``` |

pgs documentation built on May 29, 2017, 5:30 p.m.

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