FAT2DIC.ad: Analysis: DIC experiment in double factorial design with an...

In AgroR: Experimental Statistics and Graphics for Agricultural Sciences

Analysis: DIC experiment in double factorial design with an additional treatment

Description

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

Usage

FAT2DIC.ad(
  f1,
  f2,
  repe,
  response,
  responseAd,
  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",
  ad.label = "Additional",
  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
)

Arguments

f1	Numeric or complex vector with factor 1 levels
f2	Numeric or complex vector with factor 2 levels
repe	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)
names.fat	Name of factors
mcomp	Multiple comparison test (Tukey (default), LSD and Duncan)
grau	Polynomial degree in case of quantitative factor (default is 1). Provide a vector with two 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
width.column	Width column if geom="bar"
width.bar	Width errorbar
angle	x-axis scale text rotation
posi	legend position
family	Font family
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.
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, [e-mail]

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

References

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.

See Also

FAT2DIC

dunnett

Examples

library(AgroR)
data(cloro)
respAd=c(268, 322, 275, 350, 320)
with(cloro, FAT2DIC.ad(f1, f2, bloco, resp, respAd, ylab="Number of nodules", legend = "Stages"))

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