FAT3DIC: Analysis: DIC experiments in triple factorial

In AgroR: Experimental Statistics and Graphics for Agricultural Sciences

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",
  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",
  addmean = TRUE,
  errorbar = TRUE,
  dec = 3,
  geom = "bar",
  textsize = 12,
  labelsize = 4,
  point = "mean_sd",
  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)
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
point	This function defines whether the point must have all points ("all"), mean ("mean"), standard deviation (default - "mean_sd") or mean with standard error ("mean_se") if quali= FALSE. For quali=TRUE, 'mean_sd' and 'mean_se' change which information will be displayed in the error bar.
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, [e-mail]

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))

AgroR documentation built on May 29, 2024, 4:18 a.m.