VB {AgroReg}

Analysis: Von Bertalanffy

Description

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.

Usage

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"
)

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")

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

Details

The model function for the von Bertalanffy model is:

y = L(1-exp(-k(t-t0)))

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

Examples

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)