affine

Any suitable dataset representing a two-dimensional
    object, such as a point pattern (object of class <code>"ppp"</code>),
    a line segment pattern (object of class <code>"psp"</code>),
    a window (object of class <code>"owin"</code>) or a pixel im

Arguments determining the affine transformation.

Applies any affine transformation of the plane (linear transformation
  plus vector shift) to a plane geometrical object,
  such as a point pattern or a window.

spatial

math

A package for analysing spatial data, mainly Spatial Point
        Patterns, including multitype/marked points and spatial
        covariates, in any two-dimensional spatial region. Also
        supports three-dimensional point patterns, and space-time point
        patterns in any number of dimensions.  Contains over 1000
        functions for plotting spatial data, exploratory data analysis,
        model-fitting, simulation, spatial sampling, model diagnostics,
        and formal inference.  Data types include point patterns, line
        segment patterns, spatial windows, pixel images and
        tessellations.  Exploratory methods include K-functions,
        nearest neighbour distance and empty space statistics, Fry
        plots, pair correlation function, kernel smoothed intensity,
        relative risk estimation with cross-validated bandwidth
        selection, mark correlation functions, segregation indices,
        mark dependence diagnostics etc.  Point process models can be
        fitted to point pattern data using functions ppm, kppm, slrm
        similar to glm. Models may include dependence on covariates,
        interpoint interaction, cluster formation and dependence on
        marks. Fitted models can be simulated automatically.  Also
        provides facilities for formal inference (such as chi-squared
        tests) and model diagnostics (including simulation envelopes,
        residuals, residual plots and Q-Q plots).

affine: Apply Affine Transformation

Description

Usage

Arguments

Value

Details

See Also