estimate_L_f: Estimate L_f.
In myllym/spptest: Spatial point process testing

Estimate L_f.

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estimate_L_f(pattern, mtf_name = "m", r_max = NULL, r_vec = NULL,
  calc_unmarked = TRUE, edge_correction = "translate",
  use_biased_lambda2 = FALSE)

`pattern`	A `ppp` object as the simple marked point pattern to be analysed. The marks need to be in the form of a numeric vector. The window has to have the type "rectangle".
`mtf_name`	A vector of mark test function names. "1" stands for the unmarked K-function. Accepted values are '1', 'm', 'mm', 'gamma', 'gammaAbs' and 'morAbs'.
`r_max`	A positive scalar value representing the maximum radius that should be considered. r_vec overrides r_max. By default, r_max is NULL and will get a sensible default.
`r_vec`	A monotonically increasing vector of non-negative r-values to act as the endpoints of the bins for the K_f-functions. r_vec overrides r_max. The bins are exclusive on the left and inclusive on the right. If the first vector element has value zero, it will be regarded as the collapsed bin [0, 0], and the next bin will start from and exclude 0.
`calc_unmarked`	Whether to include the unmarked L in the result.
`edge_correction`	The name of the edge correction to be used. Options are 'translate' and 'none'.
`use_biased_lambda2`	A logical scalar on whether to use the biased or the unbiased (in the Poisson case) estimate of the intensity squared.

A list with either two or three components. 'obs' has the summary function that was asked for. 'r' contains the radius values. 'unmarked' contains the unmarked L function, if asked for.

myllym/spptest documentation built on May 23, 2019, noon