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

Simulates point patterns according to the null hypothesis and returns the envelope of *D* according to the confidence level.

1 2 |

`X` |
A point pattern ( |

`r` |
A vector of distances. If |

`NumberOfSimulations` |
The number of simulations to run, 100 by default. |

`Alpha` |
The risk level, 5% by default. |

`Cases` |
One of the point types |

`Controls` |
One of the point types. |

`Intertype` |
Logical; if |

`Global` |
Logical; if |

The only null hypothesis is random labeling: marks are distributed randomly across points.

This envelope is local by default, that is to say it is computed separately at each distance. See Loosmore and Ford (2006) for a discussion.

The global envelope is calculated by iteration: the simulations reaching one of the upper or lower values at any distance are eliminated at each step. The process is repeated until *Alpha / Number of simulations* simulations are dropped. The remaining upper and lower bounds at all distances constitute the global envelope. Interpolation is used if the exact ratio cannot be reached.

An envelope object (`envelope`

). There are methods for print and plot for this class.

The `fv`

contains the observed value of the function, its average simulated value and the confidence envelope.

Eric Marcon <[email protected]>

Duranton, G. and Overman, H. G. (2005). Testing for Localisation Using Micro-Geographic Data. *Review of Economic Studies* 72(4): 1077-1106.

Kenkel, N. C. (1988). Pattern of Self-Thinning in Jack Pine: Testing the Random Mortality Hypothesis. *Ecology* 69(4): 1017-1024.

Loosmore, N. B. and Ford, E. D. (2006). Statistical inference using the G or K point pattern spatial statistics. *Ecology* 87(8): 1925-1931.

Marcon, E. and F. Puech (2017). A typology of distance-based measures of spatial concentration. *Regional Science and Urban Economics*. 62:56-67.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(paracou16)
# Keep only 20% of points to run this example
X <- as.wmppp(rthin(paracou16, 0.2))
plot(X)
# Calculate confidence envelope (should be 1000 simulations, reduced to 20 to save time)
r <- 0:30
NumberOfSimulations <- 20
Alpha <- .05
# Plot the envelope (after normalization by pi.r^2)
plot(DEnvelope(X, r, NumberOfSimulations, Alpha,
"V. Americana", "Q. Rosea", Intertype = TRUE), ./(pi*r^2) ~ r)
``` |

dbmss documentation built on March 19, 2018, 5:05 p.m.

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