## Analysis: DBC experiment in double factorial design with an additional treatment

### Description

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

### Usage

FAT2DBC.ad(
f1,
f2,
block,
response,
norm = "sw",
homog = "bt",
alpha.f = 0.05,
alpha.t = 0.05,
quali = c(TRUE, TRUE),
mcomp = "tukey",
grau = c(NA, NA),
grau12 = NA,
grau21 = NA,
transf = 1,
constant = 0,
geom = "bar",
theme = theme_classic(),
ylab = "Response",
xlab = "",
xlab.factor = c("F1", "F2"),
legend = "Legend",
color = "rainbow",
fill = "lightblue",
textsize = 12,
labelsize = 4,
errorbar = TRUE,
CV = TRUE,
dec = 3,
angle = 0,
posi = "right",
family = "sans",
point = "mean_sd",
sup = NA,
ylim = NA,
angle.label = 0
)


### Arguments

 f1 Numeric or complex vector with factor 1 levels f2 Numeric or complex vector with factor 2 levels block Numeric or complex vector with repetitions response Numerical vector containing the response of the experiment. responseAd Numerical vector with additional treatment responses 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 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. 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. transf Applies data transformation (default is 1; for log consider 0; 'angular' for angular transformation) 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) xlab.factor Provide a vector with two observations referring to the x-axis name of factors 1 and 2, respectively, when there is an isolated effect of the factors. This argument uses 'parse'. legend Legend title name ad.label Aditional label color Column chart color (default is "rainbow") fill Defines chart color (to generate different colors for different treatments, define fill = "trat") textsize Font size labelsize Label Size 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) dec Number of cells angle x-axis scale text rotation posi legend position family Font family point if quali=F, defines whether to plot all points ("all"), mean ("mean"), standard deviation ("mean_sd") or mean with standard error (default - "mean_se"). sup Number of units above the standard deviation or average bar on the graph ylim y-axis scale angle.label label angle

### Value

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.

### 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 quantitative factors.

The assumptions of variance analysis disregard additional treatment

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.

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

### Examples

library(AgroR)
data(cloro)
with(cloro, FAT2DBC.ad(f1, f2, bloco, resp, respAd, ylab="Number of nodules", legend = "Stages"))


[Package AgroR version 1.2.9 Index]