| Name : R-spatstat
| |
| Version : 3.2.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.1
| Date : 2024-09-23 12:59:16
|
| Group : Development/Libraries/Other
| Source RPM : R-spatstat-3.2.1-lp153.1.1.src.rpm
|
| Size : 5.00 MB
| |
| Packager : (none)
| |
| Summary : Spatial Point Pattern analysis, model-fitting, simulation, tests
|
Description :
Comprehensive open-source toolbox for analysing Spatial Point Patterns.
Focused mainly on two-dimensional point patterns, including
multitype/marked points, in any spatial region. Also supports
three-dimensional point patterns, space-time point patterns in any
number of dimensions, point patterns on a linear network, and patterns
of other geometrical objects. Supports spatial covariate data such as
pixel images. Contains over 3000 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,
tessellations, and linear networks. Exploratory methods include quadrat
counts, K-functions and their simulation envelopes, 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, and kernel estimates
of covariate effects. Formal hypothesis tests of random pattern
(chi-squared, Kolmogorov-Smirnov, Monte Carlo,
Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and
tests for covariate effects (Cox-Berman-Waller-Lawson,
Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be
fitted to point pattern data using the functions ppm(), kppm(), slrm(),
dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox
point processes, Neyman-Scott cluster processes, and determinantal
point processes. Models may involve dependence on covariates,
inter-point interaction, cluster formation and dependence on marks.
Models are fitted by maximum likelihood, logistic regression, minimum
contrast, and composite likelihood methods. A model can be fitted to a
list of point patterns (replicated point pattern data) using the
function mppm(). The model can include random effects and fixed effects
depending on the experimental design, in addition to all the features
listed above. Fitted point process models can be simulated,
automatically. Formal hypothesis tests of a fitted model are supported
(likelihood ratio test, analysis of deviance, Monte Carlo tests) along
with basic tools for model selection (stepwise(), AIC()) and variable
selection (sdr). Tools for validating the fitted model include
simulation envelopes, residuals, residual plots and Q-Q plots, leverage
and influence diagnostics, partial residuals, and added variable plots.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
