MM {AgroReg}

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")