DICT: Analysis: Completely randomized design evaluated over time

Description

Function of the AgroR package for the analysis of experiments conducted in a completely randomized, qualitative, uniform qualitative design with multiple assessments over time, however without considering time as a factor.

Usage

DICT(
  trat,
  time,
  response,
  alpha.f = 0.05,
  alpha.t = 0.05,
  mcomp = "tukey",
  theme = theme_classic(),
  geom = "bar",
  xlab = "Independent",
  ylab = "Response",
  p.adj = "holm",
  dec = 3,
  fill = "gray",
  error = TRUE,
  textsize = 12,
  labelsize = 5,
  pointsize = 4.5,
  family = "sans",
  sup = 0,
  addmean = FALSE,
  legend = "Legend",
  ylim = NA,
  width.bar = 0.2,
  size.bar = 0.8,
  posi = c(0.1, 0.8),
  xnumeric = FALSE,
  all.letters = FALSE
)

Value

The function returns the p-value of Anova, the assumptions of normality of errors, homogeneity of variances and independence of errors, multiple comparison test, as well as a line graph

Arguments

trat: Numerical or complex vector with treatments
time: Numerical or complex vector with times
response: Numerical vector containing the response of the experiment.
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)
mcomp: Multiple comparison test (Tukey (default), LSD ("lsd"), Scott-Knott ("sk"), Duncan ("duncan") and Kruskal-Wallis ("kw"))
theme: ggplot2 theme (default is theme_classic())
geom: Graph type (columns - "bar" or segments "point")
xlab: treatments name (Accepts the expression() function)
ylab: Variable response name (Accepts the expression() function)
p.adj: Method for adjusting p values for Kruskal-Wallis ("none","holm","hommel", "hochberg", "bonferroni", "BH", "BY", "fdr")
dec: Number of cells
fill: Defines chart color (to generate different colors for different treatments, define fill = "trat")
error: Add error bar
textsize: Font size of the texts and titles of the axes
labelsize: Font size of the labels
pointsize: Point size
family: Font family
sup: Number of units above the standard deviation or average bar on the graph
addmean: Plot the average value on the graph (default is TRUE)
legend: Legend title
ylim: Define a numerical sequence referring to the y scale. You can use a vector or the `seq` command.
width.bar: width error bar
size.bar: size error bar
posi: Legend position
xnumeric: Declare x as numeric (default is FALSE)
all.letters: Adds all label letters regardless of whether it is significant or not.

Author

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.

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.

Examples

Run this code

rm(list=ls())
data(simulate1)
attach(simulate1)
with(simulate1, DICT(trat, tempo, resp))
with(simulate1, DICT(trat, tempo, resp, fill="rainbow",family="serif"))
with(simulate1, DICT(trat, tempo, resp,geom="bar",sup=40))
with(simulate1, DICT(trat, tempo, resp,geom="point",sup=40))

Run the code above in your browser using DataLab