quadratic.plateau {AgroReg}

Analysis: Quadratic-plateau

Description

This function performs the quadratic-plateau regression analysis.

Usage

quadratic.plateau(
  trat,
  resp,
  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.

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

Details

The quadratic-plateau model is defined by:

First curve:

y = \beta_0 + \beta_1 \cdot x + \beta_2 \cdot x^2 (x < breakpoint)

Second curve:

y = \beta_0 + \beta_1 \cdot breakpoint + \beta_2 \cdot breakpoint^2 (x > breakpoint)

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

Leandro Simoes Azeredo Goncalves

References

Chiu, G. S., R. Lockhart, and R. Routledge. 2006. Bent-cable regression theory and applications. Journal of the American Statistical Association 101:542-553.

Toms, J. D., and M. L. Lesperance. 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84:2034-2041.

See Also

linear.linear, linear.plateau

Examples

library(AgroReg)
data("granada")
attach(granada)
quadratic.plateau(time,WL)