beta_reg: Analysis: Beta

View source: R/beta_analysis.R

beta_regR Documentation

Analysis: Beta

Description

This function performs beta regression analysis.

Usage

beta_reg(
  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,
  xname.formula = "x",
  yname.formula = "y",
  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

xname.formula

Name of x in the equation

yname.formula

Name of y in the equation

comment

Add text after equation

fontfamily

Font family

Details

The beta model is defined by:

Y = d \times \{(\frac{X-X_b}{X_o-X_b})(\frac{X_c-X}{X_c-X_o})^{\frac{X_c-X_o}{X_o-X_b}}\}^b

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)

Model imported from the aomisc package (Andrea Onofri)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Onofri, A., 2020. The broken bridge between biologists and statisticians: a blog and R package. Statforbiology. http://www.statforbiology.com/tags/aomisc/

Examples

library(AgroReg)
X <- c(1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50)
Y <- c(0, 0, 0, 7.7, 12.3, 19.7, 22.4, 20.3, 6.6, 0, 0)
beta_reg(X,Y)

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