FAT2DIC {AgroR}R Documentation

Analysis: DIC experiments in double factorial

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(
  f1,
  f2,
  response,
  norm = "sw",
  homog = "bt",
  mcomp = "tukey",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  grau = NA,
  transf = 1,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  xlab = "",
  legend = "Legend",
  color = "rainbow",
  fill = "lightblue",
  textsize = 12,
  addmean = TRUE,
  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

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)

mcomp

Multiple comparison test (Tukey (default), LSD, Scott-Knott and Duncan)

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)

grau

Degree of polynomial in case of quantitative factor (default is 1)

transf

Applies data transformation (default is 1; for log consider 0)

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)

legend

Legend title name

color

Column chart color (default is "rainbow")

fill

Defines chart color (to generate different colors for different treatments, define fill = "trat")

textsize

Font 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 ordering of the graph is according to the sequence in which the factor levels are arranged in the data sheet. 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.

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 & 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.art, FAT2DIC.ad

Examples

library(AgroR)
data(cloro)
attach(cloro)
FAT2DIC(f1, f2, resp, ylab="Number of nodules", legend = "Stages")

[Package AgroR version 1.2.2 Index]