Regression Analysis Linear and Nonlinear for Agriculture
Utils: Adjust y and x scale
Utils: Adjust x scale
Utils: Adjust y scale
AgroReg: Regression Analysis Linear and Nonlinear for Agriculture
Analysis: Avhad and Marchetti
Analysis: Asymptotic, exponential or Logarithmic
Analysis: Asymptotic without intercept
Analysis: Asymptotic or Exponential Negative without intercept
Analysis: Asymptotic or Exponential Negative
Analysis: Brain-Cousens
Analysis: Beta
Analysis: Biexponential
Analysis: Cedergreen-Ritz-Streibig
Change the colors of a graph from the plot_arrange function
Analysis: Comparative models
Graph: Plot correlation
Analysis: Extract models
Analysis: Analogous to the Gaussian model/Bragg
Analysis: Gompertz
Analysis: Hill
Analysis: Interval of confidence
Analysis: Linear-Linear
Analysis: Linear-Plateau
Analysis: Log-logistic
Analysis: Linear, quadratic, quadratic inverse, cubic and quartic
Analysis: Linear, quadratic, quadratic inverse, cubic and quartic with...
Analysis: Cubic without beta2
Analysis: Cubic inverse without beta2
Analysis: Cubic without beta1
Analysis: Cubic inverse without beta1
Analysis: Cubic without beta1, with inverse beta3
Analysis: loess regression (degree 0, 1 or 2)
Analysis: Logarithmic
Analysis: Logarithmic quadratic
Analysis: Logistic
Analysis: Lorentz
Analysis: Midilli
Analysis: Modified Midilli
Analysis: Mitscherlich
Analysis: Michaelis-Menten
Analysis: Newton
Analysis: Graph for not significant trend
Analysis: Page
Analysis: Peleg
Analysis: Plateau-Linear
Analysis: Plateau-quadratic
Merge multiple curves into a single graph
Analysis: Potencial
Analysis: Quadratic-plateau
Analysis: Regression linear or nonlinear
Analysis: Steinhart-Hart
Analysis: Other statistical parameters
Analysis: Thompson
Analysis: Valcam
Analysis: Von Bertalanffy
Analysis: Vega-Galvez
Analysis: Weibull
Analysis: Yield-loss
Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.