VB {AgroReg} | R Documentation |
The Von Bertalanffy model. It's a kind of growth curve for a time series and takes its name from its creator, Ludwig von Bertalanffy. It is a special case of the generalized logistic function. The growth curve (biology) is used to model the average length from age in animals.
VB(
trat,
resp,
initial = NA,
sample.curve = 1000,
ylab = "Dependent",
xlab = "Independent",
theme = theme_classic(),
legend.position = "top",
r2 = "all",
error = "SE",
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"
)
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") |
r2 |
Coefficient of determination of the mean or all values (default is all) |
error |
Error bar (It can be SE - default, SD or FALSE) |
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 |
The model function for the von Bertalanffy model is:
y = L(1-exp(-k(t-t0)))
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.
Gabriel Danilo Shimizu
Leandro Simoes Azeredo Goncalves
library(AgroReg)
x=seq(1,20)
y=c(0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.91,
0.92, 0.94, 0.96, 0.98, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00)
VB(x,y)