FAT2DBC: Analysis: DBC experiments in double factorial
In AgronomiaR/AgroR: Experimental Statistics and Graphics for Agricultural Sciences

Description Usage Arguments Value Note Author(s) References See Also Examples

Analysis of an experiment conducted in a randomized block design in a double factorial scheme using analysis of variance of fixed effects.

FAT2DBC(
  f1,
  f2,
  block,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  mcomp = "tukey",
  grau = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  xlab = "",
  legend = "Legend",
  fill = "lightblue",
  angle = 0,
  textsize = 12,
  dec = 3,
  family = "sans",
  point = "mean_sd",
  addmean = TRUE,
  errorbar = TRUE,
  CV = TRUE,
  sup = NA,
  color = "rainbow",
  posi = "right",
  ylim = NA,
  angle.label = 0
)

`f1`	Numeric or complex vector with factor 1 levels
`f2`	Numeric or complex vector with factor 2 levels
`block`	Numerical or complex vector with blocks
`response`	Numerical vector containing the response of the experiment.
`norm`	Error normality test (default is Shapiro-Wilk)
`homog`	Homogeneity test of variances (default is Bartlett)
`alpha.f`	Level of significance of the F test (default is 0.05)
`alpha.t`	Significance level of the multiple comparison test (default is 0.05)
`quali`	Defines whether the factor is quantitative or qualitative (qualitative)
`mcomp`	Multiple comparison test (Tukey (default), LSD, Scott-Knott and Duncan)
`grau`	Degree of polynomial in case of quantitative factor (default is 1)
`transf`	Applies data transformation (default is 1; for log consider 0)
`constant`	Add a constant for transformation (enter value)
`geom`	Graph type (columns or segments (For simple effect only))
`theme`	ggplot2 theme (default is theme_classic())
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	Treatments name (Accepts the expression() function)
`legend`	Legend title name
`fill`	Defines chart color (to generate different colors for different treatments, define fill = "trat")
`angle`	x-axis scale text rotation
`textsize`	font size
`dec`	number of cells
`family`	font family
`point`	if quali=FALSE, defines whether to plot all points ("all"), mean ("mean"), standard deviation ("mean_sd" - default) or mean with standard error (default - "mean_se").
`addmean`	Plot the average value on the graph (default is TRUE)
`errorbar`	Plot the standard deviation bar on the graph (In the case of a segment and column graph) - default is TRUE
`CV`	Plotting the coefficient of variation and p-value of Anova (default is TRUE)
`sup`	Number of units above the standard deviation or average bar on the graph
`color`	Column chart color (default is "rainbow")
`posi`	Legend position
`ylim`	y-axis scale
`angle.label`	label angle

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk, Lilliefors, Anderson-Darling, Cramer-von Mises, Pearson and Shapiro-Francia), the test of homogeneity of variances (Bartlett or Levene), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned.

The ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two quantitative factors.

In the final output when transformation (transf argument) is different from 1, the columns resp and respo in the mean test are returned, indicating transformed and non-transformed mean, respectively.

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997

Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.

Practical Nonparametrics Statistics. W.J. Conover, 1999

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

FAT2DBC.ad

#================================================
# Example cloro
#================================================
library(AgroR)
data(cloro)
attach(cloro)
FAT2DBC(f1, f2, bloco, resp, ylab="Number of nodules", legend = "Stages")
FAT2DBC(f1, f2, bloco, resp, mcomp="sk", ylab="Number of nodules", legend = "Stages")
#================================================
# Example covercrops
#================================================
library(AgroR)
data(covercrops)
attach(covercrops)
FAT2DBC(A, B, Bloco, Resp, ylab=expression("Yield"~(Kg~"100 m"^2)),
legend = "Cover crops")
FAT2DBC(A, B, Bloco, Resp, mcomp="sk", ylab=expression("Yield"~(Kg~"100 m"^2)),
legend = "Cover crops")