summary.imlist

This function assumes that <code>im</code> objects are each realisations of the same (stochastic) object.
It returns pointwise summaries such as observed sample mean and sample variance.

Functions for estimating the gliding box lacunarity (GBL),
covariance, and pair-correlation of a random closed set (RACS) in 2D
from a binary coverage map (e.g. presence-absence land cover maps).
Contains a number of newly-developed covariance-based estimators of
GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and
balanced estimators, proposed by Picka (2000)
<http://www.jstor.org/stable/1428408>, for covariance, centred
covariance, and pair-correlation. Also contains methods for
estimating contagion-like properties of RACS and simulating 2D Boolean
models. Binary coverage maps are usually represented as raster images
with pixel values of TRUE, FALSE or NA, with NA representing
unobserved pixels. A demo for extracting such a binary map from a
geospatial data format is provided. Binary maps may also be
represented using polygonal sets as the foreground, however for most
computations such maps are converted into raster images. The package
is based on research conducted during the author's PhD studies.

Kassel Hingee

lacunaritycovariance

Gliding Box Lacunarity and Other Metrics for 2D Random Closed
Sets

summary.imlist function

<dl><dt>object</dt>
<dd>A list of <code>im</code> objects</dd>
<dt>...</dt>
<dd>Ignored</dd>
<dt>harmonizeobject</dt>
<dd>If TRUE (default) the pixel dimensions of the images will be harmonized. Otherwise the object will be tested for compatibility.</dd></dl>

Arguments

Author

Pointwise summary of a list of im objects — summary.imlist

<dl>

<dt>object</dt>
<dd>A list of <code>im</code> objects</dd>


<dt>...</dt>
<dd>Ignored</dd>


<dt>harmonizeobject</dt>
<dd>If TRUE (default) the pixel dimensions of the images will be harmonized. Otherwise the object will be tested for compatibility.</dd>

</dl>

summary.imlist: Pointwise summary of a list of `im` objects

Description

Usage

Value

Arguments

Author

Examples