FAT3DBC {AgroR}R Documentation

Analysis: DBC experiments in triple factorial

Description

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

Usage

FAT3DBC(
  f1,
  f2,
  f3,
  block,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE, TRUE),
  mcomp = "tukey",
  transf = 1,
  constant = 0,
  names.fat = c("F1", "F2", "F3"),
  ylab = "Response",
  xlab = "",
  xlab.factor = c("F1", "F2", "F3"),
  sup = NA,
  grau = c(NA, NA, NA),
  grau12 = NA,
  grau13 = NA,
  grau23 = NA,
  grau21 = NA,
  grau31 = NA,
  grau32 = NA,
  grau123 = NA,
  grau213 = NA,
  grau312 = NA,
  fill = "lightblue",
  theme = theme_classic(),
  angulo = 0,
  errorbar = TRUE,
  addmean = TRUE,
  family = "sans",
  dec = 3,
  geom = "bar",
  textsize = 12,
  labelsize = 4,
  angle.label = 0
)

Arguments

f1

Numeric or complex vector with factor 1 levels

f2

Numeric or complex vector with factor 2 levels

f3

Numeric or complex vector with factor 3 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)

transf

Applies data transformation (default is 1; for log consider 0; 'angular' for angular transformation)

constant

Add a constant for transformation (enter value)

names.fat

Allows labeling the factors 1, 2 and 3.

ylab

Variable response name (Accepts the expression() function)

xlab

Treatments name (Accepts the expression() function)

xlab.factor

Provide a vector with two observations referring to the x-axis name of factors 1, 2 and 3, respectively, when there is an isolated effect of the factors. This argument uses 'parse'.

sup

Number of units above the standard deviation or average bar on the graph

grau

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with three elements.

grau12

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 2, in the case of interaction f1 x f2 and qualitative factor 2 and quantitative factor 1.

grau13

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 3, in the case of interaction f1 x f3 and qualitative factor 3 and quantitative factor 1.

grau23

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 3, in the case of interaction f2 x f3 and qualitative factor 3 and quantitative factor 2.

grau21

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 1, in the case of interaction f1 x f2 and qualitative factor 1 and quantitative factor 2.

grau31

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 1, in the case of interaction f1 x f3 and qualitative factor 1 and quantitative factor 3.

grau32

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 2, in the case of interaction f2 x f3 and qualitative factor 2 and quantitative factor 3.

grau123

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 1, in the case of interaction f1 x f2 x f3 and quantitative factor 1.

grau213

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 2, in the case of interaction f1 x f2 x f3 and quantitative factor 2.

grau312

Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 3, in the case of interaction f1 x f2 x f3 and quantitative factor 3.

fill

Defines chart color (to generate different colors for different treatments, define fill = "trat")

theme

ggplot2 theme (default is theme_classic())

angulo

x-axis scale text rotation

errorbar

Plot the standard deviation bar on the graph (In the case of a segment and column graph) - default is TRUE

addmean

Plot the average value on the graph (default is TRUE)

family

Font family

dec

Number of cells

geom

Graph type (columns or segments)

textsize

Font size

labelsize

Label Size

angle.label

label angle

Value

The analysis of variance table, the Shapiro-Wilk error normality test, the Bartlett homogeneity test of variances, the Durbin-Watson error independence test, multiple comparison test (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.For significant triple interaction only, no graph is returned.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. The bars of the column and segment graphs are standard deviation.

The function does not perform multiple regression in the case of two or more quantitative factors. The bars of the column and segment graphs are standard deviation.

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.

Author(s)

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

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.

Ferreira, E. B., Cavalcanti, P. P., and Nogueira, D. A. (2014). ExpDes: an R package for ANOVA and experimental designs. Applied Mathematics, 5(19), 2952.

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

Examples

library(AgroR)
data(enxofre)
with(enxofre, FAT3DBC(f1, f2, f3, bloco, resp))

[Package AgroR version 1.2.9 Index]