conjdbc function

Analysis: Joint analysis of experiments in randomized block design

Function of the AgroR package for joint analysis of experiments conducted in a randomized qualitative or quantitative single-block design with balanced data.

conjdbc(
  trat,
  block,
  local,
  response,
  transf = 1,
  constant = 0,
  norm = "sw",
  homog = "bt",
  homog.value = 7,
  theme = theme_classic(),
  mcomp = "tukey",
  quali = TRUE,
  alpha.f = 0.05,
  alpha.t = 0.05,
  grau = NA,
  ylab = "response",
  title = "",
  xlab = "",
  fill = "lightblue",
  angulo = 0,
  textsize = 12,
  dec = 3,
  family = "sans",
  errorbar = TRUE
)

Arguments

• trat: Numerical or complex vector with treatments
• block: Numerical or complex vector with blocks
• local: Numeric or complex vector with locations or times
• response: Numerical vector containing the response of the experiment.
• transf: Applies data transformation (default is 1; for log consider 0)
• constant: Add a constant for transformation (enter value)
• norm: Error normality test (default is Shapiro-Wilk)
• homog: Homogeneity test of variances (default is Bartlett)
• homog.value: Reference value for homogeneity of experiments. By default, this ratio should not be greater than 7
• theme: ggplot2 theme (default is theme_classic())
• 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)
• ylab: Variable response name (Accepts the expression() function)
• title: Graph title
• xlab: Treatments name (Accepts the expression() function)
• fill: Defines chart color (to generate different colors for different treatments, define fill = "trat")
• angulo: x-axis scale text rotation
• textsize: Font size
• dec: Number of cells
• family: Font family
• errorbar: Plot the standard deviation bar on the graph (In the case of a segment and column graph) - default is TRUE

Returns

Returns the assumptions of the analysis of variance, the assumption of the joint analysis by means of a QMres ratio matrix, the analysis of variance, the multiple comparison test or regression.

Note

In this function there are three possible outcomes. When the ratio between the experiments is greater than 7, the separate analyzes are returned, without however using the square of the joint residue. When the ratio is less than 7, but with significant interaction, the effects are tested using the square of the joint residual. When there is no significant interaction and the ratio is less than 7, the joint analysis between the experiments is returned.

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.

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.

Examples

library(AgroR)
data(mirtilo)

#===================================
# No significant interaction
#===================================
with(mirtilo, conjdbc(trat, bloco, exp, resp))

#===================================
# Significant interaction
#===================================
data(eucalyptus)
with(eucalyptus, conjdbc(trati, bloc, exp, resp))

References

Ferreira, P. V. Estatistica experimental aplicada a agronomia. Edufal, 2018.

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.

Author(s)

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi