AgroR (version 1.3.6)

DIC: Analysis: Completely randomized design

Description

Statistical analysis of experiments conducted in a completely randomized and balanced design with a factor considering the fixed model. The function presents the option to use non-parametric method or transform the dataset.

Usage

DIC(
  trat,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = TRUE,
  mcomp = "tukey",
  grau = 1,
  transf = 1,
  constant = 0,
  test = "parametric",
  mcompNP = "LSD",
  p.adj = "holm",
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  sup = NA,
  CV = TRUE,
  xlab = "",
  fill = "lightblue",
  angle = 0,
  family = "sans",
  textsize = 12,
  labelsize = 4,
  dec = 3,
  width.column = NULL,
  width.bar = 0.3,
  addmean = TRUE,
  errorbar = TRUE,
  posi = "top",
  point = "mean_sd",
  pointsize = 5,
  angle.label = 0,
  ylim = NA
)

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk ("sw"), Lilliefors ("li"), Anderson-Darling ("ad"), Cramer-von Mises ("cvm"), Pearson ("pearson") and Shapiro-Francia ("sf")), the test of homogeneity of variances (Bartlett ("bt") or Levene ("levene")), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey ("tukey"), LSD ("lsd"), Scott-Knott ("sk") or Duncan ("duncan")) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. Non-parametric analysis can be used by the Kruskal-Wallis test. The column, segment or box chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Arguments

trat

Numerical or complex vector with treatments

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)

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 (default is qualitative)

mcomp

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

grau

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

transf

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

constant

Add a constant for transformation (enter value)

test

"parametric" - Parametric test or "noparametric" - non-parametric test

mcompNP

Multiple comparison test (LSD (default) or dunn)

p.adj

Method for adjusting p values for Kruskal-Wallis ("none","holm","hommel", "hochberg", "bonferroni", "BH", "BY", "fdr")

geom

Graph type (columns, boxes or segments)

theme

ggplot2 theme (default is theme_classic())

ylab

Variable response name (Accepts the expression() function)

sup

Number of units above the standard deviation or average bar on the graph

CV

Plotting the coefficient of variation and p-value of Anova (default is TRUE)

xlab

Treatments name (Accepts the expression() function)

fill

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

angle

x-axis scale text rotation

family

Font family

textsize

Font size

labelsize

Label size

dec

Number of cells

width.column

Width column if geom="bar"

width.bar

Width errorbar

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

posi

Legend position

point

Defines whether to plot mean ("mean"), mean with standard deviation ("mean_sd" - default) or mean with standard error ("mean_se"). For quali=FALSE or quali=TRUE. For parametric test it is possible to plot the square root of QMres (mean_qmres)

pointsize

Point size

angle.label

label angle

ylim

Define a numerical sequence referring to the y scale. You can use a vector or the `seq` command.

Author

Gabriel Danilo Shimizu,

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.

W.J. Conover, Practical Nonparametrics Statistics. 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.

Hothorn, T. et al. Package ‘lmtest’. Testing linear regression models. https://cran. r-project. org/web/packages/lmtest/lmtest. pdf. Accessed, v. 6, 2015.

See Also

DBC DQL

Examples

library(AgroR)
data(pomegranate)

with(pomegranate, DIC(trat, WL, ylab = "Weight loss (%)")) # tukey
with(pomegranate, DIC(trat, WL, mcomp = "sk", ylab = "Weight loss (%)"))
with(pomegranate, DIC(trat, WL, mcomp = "duncan", ylab = "Weight loss (%)"))

#=============================
# Kruskal-Wallis
#=============================
with(pomegranate, DIC(trat, WL, test = "noparametric", ylab = "Weight loss (%)"))


#=============================
# chart type
#=============================
with(pomegranate, DIC(trat, WL, geom="point", ylab = "Weight loss (%)"))
with(pomegranate, DIC(trat, WL, ylab = "Weight loss (%)", xlab="Treatments"))

#=============================
# quantitative factor
#=============================
data("phao")
with(phao, DIC(dose,comp,quali=FALSE,grau=2,
               xlab = expression("Dose"~(g~vase^-1)),
               ylab="Leaf length (cm)"))

#=============================
# data transformation
#=============================
data("pepper")
with(pepper, DIC(Acesso, VitC, transf = 0,ylab="Vitamin C"))