midilli function

Analysis: Midilli

Analysis: Midilli

This function performs Midilli regression analysis.

midilli( trat, resp, initial = NA, sample.curve = 1000, ylab = "Dependent", xlab = "Independent", theme = theme_classic(), legend.position = "top", error = "SE", r2 = "all", point = "all", width.bar = NA, scale = "none", textsize = 12, pointsize = 4.5, linesize = 0.8, linetype = 1, pointshape = 21, fillshape = "gray", colorline = "black", round = NA, yname.formula = "y", xname.formula = "x", comment = NA, fontfamily = "sans" )

Arguments

  • trat: Numeric vector with dependent variable.
  • resp: Numeric vector with independent variable.
  • initial: List starting estimates
  • sample.curve: Provide the number of observations to simulate curvature (default is 1000)
  • ylab: Variable response name (Accepts the expression() function)
  • xlab: treatments name (Accepts the expression() function)
  • theme: ggplot2 theme (default is theme_bw())
  • legend.position: legend position (default is "top")
  • error: Error bar (It can be SE - default, SD or FALSE)
  • r2: coefficient of determination of the mean or all values (default is all)
  • point: defines whether you want to plot all points ("all") or only the mean ("mean")
  • width.bar: Bar width
  • scale: Sets x scale (default is none, can be "log")
  • textsize: Font size
  • pointsize: shape size
  • linesize: line size
  • linetype: line type
  • pointshape: format point (default is 21)
  • fillshape: Fill shape
  • colorline: Color lines
  • round: round equation
  • yname.formula: Name of y in the equation
  • xname.formula: Name of x in the equation
  • comment: Add text after equation
  • fontfamily: Font family

Returns

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Details

The exponential model is defined by:

y=α×eβxn+θx y = \alpha \times e^{-\beta \cdot x^n} + \theta \cdot x

Examples

library(AgroReg) data("granada") attach(granada) midilli(time,100-WL)

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

  • Maintainer: Gabriel Danilo Shimizu
  • License: GPL (>= 2)
  • Last published: 2024-01-16