bits.test | R Documentation |

Performs a Balanced Independent Two-Stage Monte Carlo test of goodness-of-fit for spatial pattern.

bits.test(X, ..., exponent = 2, nsim=19, alternative=c("two.sided", "less", "greater"), leaveout=1, interpolate = FALSE, savefuns=FALSE, savepatterns=FALSE, verbose = TRUE)

`X` |
Either a point pattern dataset (object of class |

`...` |
Arguments passed to |

`exponent` |
Exponent used in the test statistic. Use |

`nsim` |
Number of replicates in each stage of the test.
A total of |

`alternative` |
Character string specifying the alternative hypothesis.
The default ( |

`leaveout` |
Optional integer 0, 1 or 2 indicating how to calculate the deviation between the observed summary function and the nominal reference value, when the reference value must be estimated by simulation. See Details. |

`interpolate` |
Logical value indicating whether to interpolate the distribution of the test statistic by kernel smoothing, as described in Dao and Genton (2014, Section 5). |

`savefuns` |
Logical flag indicating whether to save the simulated function values (from the first stage). |

`savepatterns` |
Logical flag indicating whether to save the simulated point patterns (from the first stage). |

`verbose` |
Logical value indicating whether to print progress reports. |

Performs the Balanced Independent Two-Stage Monte Carlo test proposed by Baddeley et al (2017), an improvement of the Dao-Genton (2014) test.

If `X`

is a point pattern, the null hypothesis is CSR.

If `X`

is a fitted model, the null hypothesis is that model.

The argument `use.theory`

passed to `envelope`

determines whether to compare the summary function for the data
to its theoretical value for CSR (`use.theory=TRUE`

)
or to the sample mean of simulations from CSR
(`use.theory=FALSE`

).

The argument `leaveout`

specifies how to calculate the
discrepancy between the summary function for the data and the
nominal reference value, when the reference value must be estimated
by simulation. The values `leaveout=0`

and
`leaveout=1`

are both algebraically equivalent (Baddeley et al, 2014,
Appendix) to computing the difference `observed - reference`

where the `reference`

is the mean of simulated values.
The value `leaveout=2`

gives the leave-two-out discrepancy
proposed by Dao and Genton (2014).

A hypothesis test (object of class `"htest"`

which can be printed to show the outcome of the test.

Adrian Baddeley, Andrew Hardegen, Tom Lawrence, Robin Milne, Gopalan Nair and Suman Rakshit. Implemented by \spatstatAuthors.

Dao, N.A. and Genton, M. (2014)
A Monte Carlo adjusted goodness-of-fit test for
parametric models describing spatial point patterns.
*Journal of Graphical and Computational Statistics*
**23**, 497–517.

Baddeley, A., Diggle, P.J., Hardegen, A., Lawrence, T., Milne,
R.K. and Nair, G. (2014) On tests of spatial pattern based on
simulation envelopes. *Ecological Monographs* **84** (3) 477–489.

Baddeley, A., Hardegen, A., Lawrence, L.,
Milne, R.K., Nair, G.M. and Rakshit, S. (2017)
On two-stage Monte Carlo tests of composite hypotheses.
*Computational Statistics and Data Analysis*, in press.

Simulation envelopes: `bits.envelope`

.

Other tests:
`dg.test`

,
`dclf.test`

,
`mad.test`

.

ns <- if(interactive()) 19 else 4 bits.test(cells, nsim=ns) bits.test(cells, alternative="less", nsim=ns) bits.test(cells, nsim=ns, interpolate=TRUE)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.