DBC: Analysis: Randomized block design

In AgroR: Experimental Statistics and Graphics for Agricultural Sciences

Analysis: Randomized block design

Description

This is a function of the AgroR package for statistical analysis of experiments conducted in a randomized block 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

DBC(
  trat,
  block,
  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",
  geom = "bar",
  theme = theme_classic(),
  sup = NA,
  CV = TRUE,
  ylab = "response",
  xlab = "",
  textsize = 12,
  labelsize = 4,
  fill = "lightblue",
  angle = 0,
  family = "sans",
  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
)

Arguments

trat	Numerical or complex vector with treatments
block	Numerical or complex vector with blocks
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
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)
textsize	Font size
labelsize	Label size
fill	Defines chart color (to generate different colors for different treatments, define fill = "trat")
angle	x-axis scale text rotation
family	Font family
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 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.

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 Friedman test. The column, segment or box chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Note

Enable ggplot2 package to change theme argument.

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.

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, [e-mail]

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.

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., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

See Also

DIC, DQL

Examples

library(AgroR)

#=============================
# Example laranja
#=============================
data(laranja)
attach(laranja)
DBC(trat, bloco, resp, mcomp = "sk", angle=45, ylab = "Number of fruits/plants")

#=============================
# Friedman test
#=============================
DBC(trat, bloco, resp, test="noparametric", ylab = "Number of fruits/plants")

#=============================
# Example soybean
#=============================
data(soybean)
with(soybean, DBC(cult, bloc, prod,
                  ylab=expression("Grain yield"~(kg~ha^-1))))

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