LM_i: Analysis: Linear, quadratic, quadratic inverse, cubic and...

In AgroReg: Regression Analysis Linear and Nonlinear for Agriculture

Analysis: Linear, quadratic, quadratic inverse, cubic and quartic without intercept

Description

Linear, quadratic, quadratic inverse, cubic and quartic regression.

Usage

LM_i(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  error = "SE",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  xlab = "Independent",
  degree = NA,
  theme = theme_classic(),
  legend.position = "top",
  point = "all",
  r2 = "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)
ic	Add interval of confidence
fill.ic	Color interval of confidence
alpha.ic	confidence interval transparency level
xlab	Independent variable name (Accepts the expression() function)
degree	degree of the polynomial (0.5, 1, 2, 3 or 4)
theme	ggplot2 theme (default is theme_classic())
legend.position	legend position (default is "top")
point	defines whether you want to plot all points ("all") or only the mean ("mean")
r2	coefficient of determination of the mean or all values (default is all)
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

The linear model is defined by:

y = \beta_1\cdot x

The quadratic model is defined by:

y = \beta_1\cdot x + \beta_2\cdot x^2

The quadratic inverse model is defined by:

y = \beta_1\cdot x + \beta_2\cdot x^{0.5}

The cubic model is defined by:

y = \beta_1\cdot x + \beta_2\cdot x^2 + \beta_3\cdot x^3

The quartic model is defined by:

y = \beta_1\cdot x + \beta_2\cdot x^2 + \beta_3\cdot x^3+ \beta_4\cdot x^4

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("aristolochia")
attach(aristolochia)
LM_i(trat,resp, degree = 3)

AgroReg documentation built on May 29, 2024, 9:13 a.m.