PAGE {AgroReg} R Documentation

## Analysis: Page

### Description

This function performs exponential page regression analysis.

### Usage

PAGE(
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 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

### Details

The exponential model is defined by:

y = e^{-k \cdot x^n}

### 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

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

library(AgroReg)