Analysis of an experiment conducted in a completely randomized design in a double factorial scheme using analysis of variance of fixed effects.
FAT2DIC(
f1,
f2,
response,
norm = "sw",
homog = "bt",
alpha.f = 0.05,
alpha.t = 0.05,
quali = c(TRUE, TRUE),
names.fat = c("F1", "F2"),
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,
addmean = TRUE,
errorbar = TRUE,
CV = TRUE,
dec = 3,
width.column = 0.9,
width.bar = 0.3,
angle = 0,
posi = "right",
family = "sans",
point = "mean_sd",
sup = NA,
ylim = NA,
angle.label = 0
)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.
Numeric or complex vector with factor 1 levels
Numeric or complex vector with factor 2 levels
Numerical vector containing the response of the experiment.
Error normality test (default is Shapiro-Wilk)
Homogeneity test of variances (default is Bartlett)
Level of significance of the F test (default is 0.05)
Significance level of the multiple comparison test (default is 0.05)
Defines whether the factor is quantitative or qualitative (qualitative)
Name of factors
Multiple comparison test (Tukey (default), LSD, Scott-Knott and Duncan)
Polynomial degree in case of quantitative factor (default is 1). Provide a vector with two elements.
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.
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.
Applies data transformation (default is 1; for log consider 0; `angular` for angular transformation)
Add a constant for transformation (enter value)
Graph type (columns or segments (For simple effect only))
ggplot2 theme (default is theme_classic())
Variable response name (Accepts the expression() function)
Treatments name (Accepts the expression() function)
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 title name
Column chart color (default is "rainbow")
Defines chart color (to generate different colors for different treatments, define fill = "trat")
Font size
Label Size
Plot the average value on the graph (default is TRUE)
Plot the standard deviation bar on the graph (In the case of a segment and column graph) - default is TRUE
Plotting the coefficient of variation and p-value of Anova (default is TRUE)
Number of cells
Width column if geom="bar"
Width errorbar
x-axis scale text rotation
Legend position
Font family
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.
Number of units above the standard deviation or average bar on the graph
y-axis scale
Label angle
Gabriel Danilo Shimizu, shimizu@uel.br
Leandro Simoes Azeredo Goncalves
Rodrigo Yudi Palhaci Marubayashi
Principles and procedures of statistics a biometrical approach Steel & Torry & 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., & de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.
FAT2DIC.ad
#====================================
# Example cloro
#====================================
library(AgroR)
data(cloro)
with(cloro, FAT2DIC(f1, f2, resp, ylab="Number of nodules", legend = "Stages"))
#====================================
# Example corn
#====================================
library(AgroR)
data(corn)
with(corn, FAT2DIC(A, B, Resp, quali=c(TRUE, TRUE),ylab="Heigth (cm)"))
with(corn, FAT2DIC(A, B, Resp, mcomp="sk", quali=c(TRUE, TRUE),ylab="Heigth (cm)"))
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