| LM23i {AgroReg} | R Documentation | 
Analysis: Cubic inverse without beta1
Description
Degree 3 polynomial inverse model without the beta 1 coefficient.
Usage
LM23i(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  error = "SE",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  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 | 
 Dependent variable name (Accepts the expression() function)  | 
error | 
 Error bar (It can be SE - default, SD or FALSE)  | 
xlab | 
 Independent variable name (Accepts the expression() function)  | 
theme | 
 ggplot2 theme (default is theme_classic())  | 
legend.position | 
 legend position (default is "top")  | 
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
Inverse degree 3 polynomial model without the beta 1 coefficient is defined by:
y = \beta_0 + \beta_2\cdot x^{1/2} + \beta_3\cdot x^{1/3}
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)
data("granada")
attach(granada)
LM23i(time, WL)