Analysis of Plant Disease Epidemics
Easily switch between different power law formulations.
Several aggregation indices.
Coerce to a data frame.
The beta-binomial distribution.
C(alpha) test.
Chi-squared test.
Regroup observational data into even clumps of individuals.
Extract Model Coefficients
list("epiphy"): An R pack...
Maximum likelihood fitting of two distributions and goodness-of-fit co...
Retrieve vector or array indices
Construct count, incidence and severity objects.
Test if an object is of class intensity or one of its subclasses.
Some link functions.
Extract log-likelihood
Map Comparison procedure.
Existing variable mappings.
Construct data mappings.
Taylor's and binary power laws.
Spatial Analysis by Distance IndicEs (SADIE).
Simple maximum likelihood estimation
Wrappers using maximum likelihood estimation for some distributions
Spatial hierarchy analysis.
Divide into groups and reassemble.
To go to higher level in the hierarchy.
Calculate Variance-Covariance Matrix for a Fitted Model Object
Z-test.
A toolbox to make it easy to analyze plant disease epidemics. It provides a common framework for plant disease intensity data recorded over time and/or space. Implemented statistical methods are currently mainly focused on spatial pattern analysis (e.g., aggregation indices, Taylor and binary power laws, distribution fitting, SADIE and 'mapcomp' methods). See Laurence V. Madden, Gareth Hughes, Franck van den Bosch (2007) <doi:10.1094/9780890545058> for further information on these methods. Several data sets that were mainly published in plant disease epidemiology literature are also included in this package.
Useful links