DIC {AgroR}R Documentation

Analysis: Completely randomized design

Description

Statistical analysis of experiments conducted in a completely randomized and balanced design with a factor considering the fixed model.

Usage

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

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)

mcomp

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

quali

Defines whether the factor is quantitative or qualitative (default is qualitative)

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)

grau

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

transf

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

test

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

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())

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)

ylab

Variable response name (Accepts the expression() function)

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

dec

Number of cells

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 (default - "mean_se"). Only for quali=F.

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. 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.

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.

Post hoc test in nonparametric is using the criterium Fisher's least significant difference (p-adj="holm").

CV and p-value of the graph indicate coefficient of variation and p-value of the F test of the analysis of variance.

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 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.

See Also

DBC DQL

Examples

library(AgroR)
data(pomegranate)
attach(pomegranate)

DIC(trat, WL) # tukey
DIC(trat, WL, mcomp = "sk")
DIC(trat, WL, mcomp = "duncan")

#=============================
# Kruskal-Wallis
#=============================
DIC(trat, WL, test = "noparametric")

#=============================
# data transformation
#=============================
DIC(trat, WL, transf = 0)

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

#=============================
# quantitative factor
#=============================
data("phao")
attach(phao)
DIC(dose,comp,quali=FALSE,grau=2)

[Package AgroR version 1.2.2 Index]