yieldloss {AgroReg} R Documentation

## Analysis: Yield-loss

### Description

This function performs regression analysis using the Yield loss model.

### Usage

yieldloss(
trat,
resp,
sample.curve = 1000,
error = "SE",
ylab = "Dependent",
xlab = "Independent",
theme = theme_classic(),
legend.position = "top",
point = "all",
width.bar = NA,
r2 = "all",
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,
scale = "none",
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) 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) 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 scale Sets x scale (default is none, can be "log") fontfamily Font family

### Details

The Yield Loss model is defined by:

y = \frac{i \times x}{1+\frac{i}{A} \times 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)

Model imported from the aomisc package (Onofri, 2020)

Gabriel Danilo Shimizu

### References

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

Onofri A. (2020) The broken bridge between biologists and statisticians: a blog and R package, Statforbiology, IT, web: https://www.statforbiology.com

### Examples

data("granada")