yieldloss {AgroReg} | R Documentation |
This function performs regression analysis using the Yield loss model.
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"
)
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 |
The Yield Loss model is defined by:
y = \frac{i \times x}{1+\frac{i}{A} \times x}
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.
Model imported from the aomisc package (Onofri, 2020)
Gabriel Danilo Shimizu
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
data("granada")
attach(granada)
yieldloss(time,WL)