MM: Analysis: Michaelis-Menten

In AgroReg: Regression Analysis Linear and Nonlinear for Agriculture

Analysis: Michaelis-Menten

Description

This function performs regression analysis using the Michaelis-Menten model.

Usage

MM(
  trat,
  resp,
  npar = "mm2",
  sample.curve = 1000,
  error = "SE",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  point = "all",
  width.bar = NA,
  r2 = "all",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  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.
npar	Number of parameters (mm2 or mm3)
sample.curve	Provide the number of observations to simulate curvature (default is 1000)
error	Error bar (It can be SE - default, SD or FALSE)
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")
point	defines whether you want to plot all points ("all") or only the mean ("mean")
width.bar	Bar width
r2	coefficient of determination of the mean or all values (default is all)
ic	Add interval of confidence
fill.ic	Color interval of confidence
alpha.ic	confidence interval transparency level
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

Details

The two-parameter Michaelis-Menten model is defined by:

y = \frac{Vm \times x}{k + x}

The three-parameter Michaelis-Menten model is defined by:

y = c + \frac{Vm \times x}{k + x}

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.

Author(s)

Gabriel Danilo Shimizu

References

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

Examples

data("granada")
attach(granada)
MM(time,WL)
MM(time,WL,npar="mm3")

AgroReg documentation built on May 29, 2024, 9:13 a.m.