PSUBDIC: Analysis: DIC experiments in split-plot
In AgronomiaR/AgroR: Experimental Statistics and Graphics for Agricultural Sciences

Description Usage Arguments Value Note Author(s) References Examples

Analysis of an experiment conducted in a completely randomized design in a split-plot scheme using fixed effects analysis of variance.

PSUBDIC(
  f1,
  f2,
  block,
  response,
  norm = "sw",
  homog = "bt",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  mcomp = "tukey",
  grau = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  xlab = "",
  fill = "lightblue",
  angle = 0,
  family = "sans",
  color = "rainbow",
  legend = "Legend",
  errorbar = TRUE,
  addmean = TRUE,
  textsize = 12,
  dec = 3,
  ylim = NA,
  posi = "right",
  point = "mean_se",
  angle.label = 0
)

`f1`	Numeric or complex vector with plot levels
`f2`	Numeric or complex vector with subplot levels
`block`	Numeric or complex vector with blocks
`response`	Numeric vector with responses
`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 (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)
`constant`	Add a constant for transformation (enter value)
`geom`	Graph type (columns or segments (For simple effect only))
`theme`	ggplot2 theme (default is theme_classic())
`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 (default is sans)
`color`	When the columns are different colors (Set fill-in argument as "trat")
`legend`	Legend title name
`errorbar`	Plot the standard deviation bar on the graph (In the case of a segment and column graph) - default is TRUE
`addmean`	Plot the average value on the graph (default is TRUE)
`textsize`	Font size (default is 12)
`dec`	Number of cells (default is 3)
`ylim`	y-axis limit
`posi`	Legend position
`point`	Point type for regression ("mean_se","mean_sd","mean" or "all")
`angle.label`	Label angle

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

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.

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

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.

#===================================
# Example tomate
#===================================
# Obs. Consider that the "tomato" experiment is a completely randomized design.
library(AgroR)
data(tomate)
with(tomate, PSUBDIC(parc, subp, bloco, resp, ylab="Dry mass (g)"))