Analysis and Simulation of Plant Disease Progress Curves
Area under disease progress curve
Area under disease progress stairs
Function for Exponential model
Fits epidemic models using data linearization
Estimate model parameters for multiple disease progress curves
Fits epidemic models using nonlinear aproach
Fits epidemic models using nonlinear aproach. This function also estim...
Function for Gompertz model
Function for logistic model
Function for Monomolecular model
Creates a plot panel for the fitted models
Print fit_lin() or fit_nlin() outputs
Print fit_nlin2() outputs
Simulate an epidemic using the Exponential model
Simulate an epidemic using the Gompertz model
Simulate an epidemic using the logistic model
Simulate an epidemic using the Monomolecular model
Analysis and visualization of plant disease progress curve data. Functions for fitting two-parameter population dynamics models (exponential, monomolecular, logistic and Gompertz) to proportion data for single or multiple epidemics using either linear or no-linear regression. Statistical and visual outputs are provided to aid in model selection. Synthetic curves can be simulated for any of the models given the parameters. See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058> for further information on the methods.