FAT3DIC {AgroR} R Documentation

## Analysis: DIC experiments in triple factorial

### Description

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

### Usage

FAT3DIC(
f1,
f2,
f3,
response,
norm = "sw",
homog = "bt",
alpha.t = 0.05,
alpha.f = 0.05,
quali = c(TRUE, TRUE, TRUE),
mcomp = "tukey",
grau = c(NA, NA, NA),
grau12 = NA,
grau13 = NA,
grau23 = NA,
grau21 = NA,
grau31 = NA,
grau32 = NA,
grau123 = NA,
grau213 = NA,
grau312 = NA,
transf = 1,
constant = 0,
names.fat = c("F1", "F2", "F3"),
ylab = "Response",
xlab = "",
xlab.factor = c("F1", "F2", "F3"),
sup = NA,
fill = "lightblue",
theme = theme_classic(),
angulo = 0,
family = "sans",
errorbar = TRUE,
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 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.t Significance level of the multiple comparison test (default is 0.05) alpha.f Level of significance of the F 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 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. 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 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 family Font family 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 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, FAT3DIC(f1, f2, f3, resp))


[Package AgroR version 1.2.9 Index]