polynomial: Analysis: Linear regression graph

Description

Linear regression analysis of an experiment with a quantitative factor or isolated effect of a quantitative factor

Usage

polynomial(
  trat,
  resp,
  ylab = "Response",
  xlab = "Independent",
  yname.poly = "y",
  xname.poly = "x",
  grau = NA,
  theme = theme_classic(),
  point = "mean_sd",
  color = "gray80",
  posi = "top",
  textsize = 12,
  se = FALSE,
  ylim = NA,
  family = "sans",
  pointsize = 4.5,
  linesize = 0.8,
  width.bar = NA,
  n = NA,
  SSq = NA,
  DFres = NA
)

Value

Returns linear, quadratic or cubic regression analysis.

Arguments

trat: Numerical vector with treatments (Declare as numeric)
resp: Numerical vector containing the response of the experiment.
ylab: Dependent variable name (Accepts the expression() function)
xlab: Independent variable name (Accepts the expression() function)
yname.poly: Y name in equation
xname.poly: X name in equation
grau: Degree of the polynomial (1, 2 or 3)
theme: ggplot2 theme (default is theme_classic())
point: Defines whether to plot mean ("mean"), all repetitions ("all"),mean with standard deviation ("mean_sd") or mean with standard error (default - "mean_se").
color: Graph color (default is gray80)
posi: Legend position
textsize: Font size
se: Adds confidence interval (default is FALSE)
ylim: y-axis scale
family: Font family
pointsize: Point size
linesize: line size (Trendline and Error Bar)
width.bar: width of the error bars of a regression graph.
n: Number of decimal places for regression equations
SSq: Sum of squares of the residue
DFres: Residue freedom degrees

Author

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Examples

Run this code

data("phao")
with(phao, polynomial(dose,comp, grau = 2))