Description Usage Arguments Details Value References See Also Examples

View source: R/DutilleulPlot.fun.R

This function applies the Diggle's randomization testing procedure extended by Dutilleul(2011) and performs a plot which checks graphically the independence of two point proceses. It is implemented for homogenous and non homogenous processes.

1 | ```
DutilleulPlot.fun(posx, posy, lambday, nsim = 1000, lenve = c(0.025, 0.975), ...)
``` |

`posx` |
Numeric vector. Position of the occurrence points of the first point process. |

`posy` |
Numeric vector. Position of the occurrence points of the second point process. |

`lambday` |
Numeric vector. Intensity values of the second point process |

`nsim` |
Optional. Positive integer. Number of simulations performed to obtain the confidence bands. |

`lenve` |
Optional. Numeric vector. Lower and the upper percentiles which determine the limits of the confidence band. |

`...` |
Further arguments to be passed to |

This is a procedure to check graphically the independence of two point proceses.
It is based on the comparison of the
cumulative relative frequency of the nearest neighbour distances
between the points in the two observed processes, with their counterpart in two independent processes with the same
intensities. The procedure consists on plotting the cumulative relative frequency of the observed processes and a
confidence band calculated from *nsim* simulated independent processes.

A list with the elements used in the plot

`quantobs` |
Vector of observed percentiles of the nearest neighbour distances. |

`enve1` |
Vector of lower limits of the confidence band. |

`enve2` |
Vector of upper limits of the confidence band. |

Dutilleul, P. (2011), *Spatio-temporal heterogeneity: Concepts and analyses*, Cambridge University Press.

`TestIndNH.fun`

, `CondTest.fun`

,`nearestdist.fun`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
#Two independent NHPPs
set.seed(123)
lambdax<-runif(200, 0.01,0.1)
set.seed(124)
lambday<-runif(200, 0.015,0.15)
posx<-simNHPc.fun(lambdax,fixed.seed=123)$posNH
posy<-simNHPc.fun(lambday, fixed.seed=123)$posNH
aux<-DutilleulPlot.fun(posx, posy, lambday, nsim = 100)
#Two dependent NSPs
#set.seed(123)
#lambdaParent<-runif(200)/10
#DepPro<-DepNHNeyScot.fun(lambdaParent=lambdaParent, d=2, lambdaNumP = 3,
# dist = "normal", sigmaC = 3,fixed.seed=123)
#posx<-DepPro$PP1
#posy<-DepPro$PP2
#aux<-DutilleulPlot.fun(posx, posy, lambday, nsim = 100)
``` |

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