midilli: Analysis: Midilli

Description

This function performs Midilli regression analysis.

Usage

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"
)

Value

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.

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

Author

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Details

The exponential model is defined by: $$y = \alpha \times e^{-\beta \cdot x^n} + \theta \cdot x$$

References

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

Examples

Run this code

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

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