unscaled_envelope: Unscaled envelope test

Description Usage Arguments Details Value References Examples

View source: R/envelopes.r

Description

The unscaled envelope test, which leads to envelopes with constant width over the distances r. It corresponds to the classical maximum deviation test without scaling.

Usage

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unscaled_envelope(curve_set, ...)

Arguments

curve_set

A curve_set (see create_curve_set) or an envelope object. If an envelope object is given, it must contain the summary functions from the simulated patterns which can be achieved by setting savefuns = TRUE when calling envelope.

...

Additional parameters to be passed to global_envelope_test.

Details

This test suffers from unequal variance of T(r) over the distances r and from the asymmetry of distribution of T(r). We recommend to use the rank_envelope (if number of simulations close to 5000 can be afforded) or st_envelope/qdir_envelope (if large number of simulations cannot be afforded) instead.

Value

An object of class "global_envelope", "envelope" and "fv" (see fv.object), which can be printed and plotted directly.

Essentially a data frame containing columns

Additionally, the return value has attributes

and a punch of attributes for the "fv" object type.

References

Ripley, B.D. (1981). Spatial statistics. Wiley, New Jersey.

Examples

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## Testing complete spatial randomness (CSR)
#-------------------------------------------
require(spatstat)
pp <- spruces
## Test for complete spatial randomness (CSR)
# Generate nsim simulations under CSR, calculate L-function for the data and simulations
env <- envelope(pp, fun="Lest", nsim=999, savefuns=TRUE, correction="translate",
                simulate=expression(runifpoint(pp$n, win=pp$window)))
# The studentised envelope test
res <- unscaled_envelope(env)
plot(res)
# or (requires R library ggplot2)
plot(res, plot_style="ggplot2")

## Advanced use:
# Create a curve set, choosing the interval of distances [r_min, r_max]
curve_set <- crop_curves(env, r_min = 1, r_max = 8)
# For better visualisation, take the L(r)-r function
curve_set <- residual(curve_set, use_theo = TRUE)
# The studentised envelope test
res <- unscaled_envelope(curve_set); plot(res, plot_style="ggplot2")

## Random labeling test
#----------------------
# requires library 'marksummary'
mpp <- spruces
# Use the test function T(r) = \hat{L}_m(r), an estimator of the L_m(r) function
curve_set <- random_labelling(mpp, mtf_name = 'm', nsim=2499, r_min=1.5, r_max=9.5)
res <- unscaled_envelope(curve_set)
plot(res, plot_style="ggplot2", ylab=expression(italic(L[m](r)-L(r))))

myllym/GET documentation built on Sept. 30, 2018, 5:49 a.m.