GoFtest {dbmss} | R Documentation |

## Goodness of Fit test between a distance based measure of spatial structure and simulations of its null hypothesis

### Description

Calculates the risk to reject the null hypothesis erroneously, based on the distribution of the simulations.

### Usage

```
GoFtest(Envelope)
```

### Arguments

`Envelope` |
An envelope object ( |

### Details

This test was introduced by Diggle(1983) and extensively developped by Loosmore and Ford (2006) for *K*, and applied to *M* by Marcon et al. (2012).

### Value

A p-value.

### Note

No support exists in the literature to apply the GoF test to non-cumulative functions (*g*, *Kd*...).

`Ktest`

is a much better test (it does not rely on simulations) but it is limited to the *K* function against complete spatial randomness (CSR) in a rectangle window.

### References

Diggle, P. J. (1983). *Statistical analysis of spatial point patterns*. Academic Press, London. 148 p.

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., F. Puech and S. Traissac (2012). Characterizing the relative spatial structure of point patterns. International *Journal of Ecology* 2012(Article ID 619281): 11.

### See Also

### Examples

```
# Simulate a Matern (Neyman Scott) point pattern
nclust <- function(x0, y0, radius, n) {
return(runifdisc(n, radius, centre=c(x0, y0)))
}
X <- rNeymanScott(20, 0.2, nclust, radius=0.3, n=10)
autoplot(as.wmppp(X))
# Calculate confidence envelope (should be 1000 simulations, reduced to 50 to save time)
r <- seq(0, 0.3, 0.01)
NumberOfSimulations <- 50
Alpha <- .10
Envelope <- KEnvelope(as.wmppp(X), r, NumberOfSimulations, Alpha)
autoplot(Envelope, ./(pi*r^2) ~ r)
# GoF test. Power is correct if enough simulations are run (say >1000).
paste("p-value =", GoFtest(Envelope))
```

*dbmss*version 2.9-0 Index]