Function of the AgroR package for analysis of experiments conducted in a balanced qualitative, single-factorial randomized block design with multiple assessments over time, however without considering time as a factor.

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

Arguments

trat

Numerical or complex vector with treatments

block

Numerical or complex vector with blocks

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 Friedman ("fd"))

geom

Graph type (columns - "bar" or segments "point")

theme

ggplot2 theme (default is theme_classic())

fill

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

ylab

Variable response name (Accepts the expression() function)

xlab

Treatments name (Accepts the expression() function)

textsize

Font size of the texts and titles of the axes

labelsize

Font size of the labels

pointsize

Point size

error

Add error bar (SD)

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)

posi

Legend position

legend

Legend title

ylim

y-axis scale

width.bar

width error bar

size.bar

size error bar

dec

Number of cells

xnumeric

Declare x as numeric (default is FALSE)

all.letters

Adds all label letters regardless of whether it is significant or not.

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

Details

The p-value of the analysis of variance, the normality test for Shapiro-Wilk errors, the Bartlett homogeneity test of variances, the independence of Durbin-Watson errors and the multiple comparison test (Tukey, Scott-Knott, LSD or Duncan).

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.

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.

See also

Author

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Gonçalves

Rodrigo Yudi Palhaci Marubayashi

Examples

rm(list=ls())
data(simulate2)
attach(simulate2)
#> The following objects are masked from laranja:
#> 
#>     bloco, resp, trat
#> The following objects are masked from aristolochia:
#> 
#>     resp, trat
#> The following objects are masked from cloro:
#> 
#>     bloco, resp
#> The following objects are masked from passiflora:
#> 
#>     bloco, trat

#===================================
# default
#===================================
DBCT(trat, bloco, tempo, resp)
#> 
#> -----------------------------------------------------------------
#> ANOVA and assumptions
#> -----------------------------------------------------------------
#>   p-value ANOVA Shapiro-Wilk   Bartlett Durbin-Watson    CV (%)
#> 1  3.210683e-04   0.04597748 0.17664020     0.2232452 11.907549
#> 2  5.870699e-06   0.32303793 0.23068072     0.6899538  4.465134
#> 3  4.517922e-04   0.31186247 0.84811700     0.3190431  4.514334
#> 4  8.164832e-03   0.46256418 0.13341771     0.3167184  5.665891
#> 5  8.687842e-04   0.11296195 0.29177701     0.3166757  2.765267
#> 6  2.543755e-04   0.55188443 0.06254508     0.5822472  2.051416

DBCT(trat, bloco, tempo, resp,fill="rainbow")
#> 
#> -----------------------------------------------------------------
#> ANOVA and assumptions
#> -----------------------------------------------------------------
#>   p-value ANOVA Shapiro-Wilk   Bartlett Durbin-Watson    CV (%)
#> 1  3.210683e-04   0.04597748 0.17664020     0.2232452 11.907549
#> 2  5.870699e-06   0.32303793 0.23068072     0.6899538  4.465134
#> 3  4.517922e-04   0.31186247 0.84811700     0.3190431  4.514334
#> 4  8.164832e-03   0.46256418 0.13341771     0.3167184  5.665891
#> 5  8.687842e-04   0.11296195 0.29177701     0.3166757  2.765267
#> 6  2.543755e-04   0.55188443 0.06254508     0.5822472  2.051416


#===================================
# segment chart
#===================================
DBCT(trat, bloco, tempo, resp, geom="point")
#> 
#> -----------------------------------------------------------------
#> ANOVA and assumptions
#> -----------------------------------------------------------------
#>   p-value ANOVA Shapiro-Wilk   Bartlett Durbin-Watson    CV (%)
#> 1  3.210683e-04   0.04597748 0.17664020     0.2232452 11.907549
#> 2  5.870699e-06   0.32303793 0.23068072     0.6899538  4.465134
#> 3  4.517922e-04   0.31186247 0.84811700     0.3190431  4.514334
#> 4  8.164832e-03   0.46256418 0.13341771     0.3167184  5.665891
#> 5  8.687842e-04   0.11296195 0.29177701     0.3166757  2.765267
#> 6  2.543755e-04   0.55188443 0.06254508     0.5822472  2.051416