View source: R/deviation_test.r

deviation_test | R Documentation |

Crop the curve set to the interval of distances [r_min, r_max],
calculate residuals, scale the residuals and perform a deviation test
with a chosen deviation measure.
The deviation tests are well known in spatial statistics; in GET they are
provided for comparative purposes. Some (maximum type) of the deviation test
have their corresponding envelope tests available, see Myllymäki et al., 2017
(and 'unscaled', 'st' and 'qdir' in `global_envelope_test`

).

deviation_test( curve_set, r_min = NULL, r_max = NULL, use_theo = TRUE, scaling = "qdir", measure = "max", savedevs = FALSE )

`curve_set` |
A residual curve_set object. Can be obtained by using residual(). |

`r_min` |
The minimum radius to include. |

`r_max` |
The maximum radius to include. |

`use_theo` |
Whether to use the theoretical summary function or the mean of the functions in the curve_set. |

`scaling` |
The name of the scaling to use. Options include 'none', 'q', 'qdir' and 'st'. 'qdir' is default. |

`measure` |
The deviation measure to use. Default is 'max'. Must be one of the following: 'max', 'int' or 'int2'. |

`savedevs` |
Logical. Should the global rank values k_i, i=1,...,nsim+1 be returned? Default: FALSE. |

The deviation test is based on a test function *T(r)* and it works as follows:

1) The test function estimated for the data, *T_1(r)*, and for nsim simulations
from the null model, *T_2(r), ...., T_{nsim+1}(r)*, must be saved in 'curve_set'
and given to the deviation_test function.

2) The deviation_test function then

Crops the functions to the chosen range of distances

*[r_min, r_max]*.If the curve_set does not consist of residuals (see

`residual`

), then the residuals*d_i(r) = T_i(r) - T_0(r)*are calculated, where*T_0(r)*is the expectation of*T(r)*under the null hypothesis. If use_theo = TRUE, the theoretical value given in the curve_set$theo is used for as*T_0(r)*, if it is given. Otherwise,*T_0(r)*is estimated by the mean of*T_j(r)*,*j=2,...,nsim+1*.Scales the residuals. Options are

'none' No scaling. Nothing done.

'q' Quantile scaling.

'qdir' Directional quantile scaling.

'st' Studentised scaling.

See for details Myllymäki et al. (2013).

Calculates the global deviation measure

*u_i*,*i=1,...,nsim+1*, see options for 'measure'.'max' is the maximum deviation measure

*u_i = max_(r in [r_min, r_max]) | w(r)(T_i(r) - T_0(r)) |*'int2' is the integral deviation measure

*u_i = int_([r_min, r_max]) ( w(r)(T_i(r) - T_0(r)) )^2 dr*'int' is the 'absolute' integral deviation measure

*u_i = int_([r_min, r_max]) | w(r)(T_i(r) - T_0(r)) | dr*

Calculates the p-value.

Currently, there is no special way to take care of the same values of *T_i(r)*
occuring possibly for small distances. Thus, it is preferable to exclude from
the test the very small distances r for which ties occur.

If 'savedevs=FALSE' (default), the p-value is returned.
If 'savedevs=TRUE', then a list containing the p-value and calculated deviation measures
*u_i*, *i=1,...,nsim+1* (where *u_1* corresponds to the data pattern) is returned.

Myllymäki, M., Grabarnik, P., Seijo, H. and Stoyan. D. (2015). Deviation test construction and power comparison for marked spatial point patterns. Spatial Statistics 11: 19-34. doi: 10.1016/j.spasta.2014.11.004

Myllymäki, M., Mrkvička, T., Grabarnik, P., Seijo, H. and Hahn, U. (2017). Global envelope tests for spatial point patterns. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79: 381–404. doi: 10.1111/rssb.12172

## Testing complete spatial randomness (CSR) #------------------------------------------- if(require("spatstat.explore", quietly=TRUE)) { pp <- unmark(spruces) nsim <- 999 # Generate nsim simulations under CSR, calculate L-function for the data and simulations env <- envelope(pp, fun="Lest", nsim=nsim, savefuns=TRUE, correction="translate") # The deviation test using the integral deviation measure res <- deviation_test(env, measure='int') res # or res <- deviation_test(env, r_min=0, r_max=7, measure='int2') }

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