PSUBDIC: Analysis: DIC experiments in split-plot

Description

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

Usage

PSUBDIC(
  f1,
  f2,
  block,
  response,
  norm = "sw",
  alpha.f = 0.05,
  alpha.t = 0.05,
  quali = c(TRUE, TRUE),
  names.fat = c("F1", "F2"),
  mcomp = "tukey",
  grau = c(NA, NA),
  grau12 = NA,
  grau21 = NA,
  transf = 1,
  constant = 0,
  geom = "bar",
  theme = theme_classic(),
  ylab = "Response",
  xlab = "",
  xlab.factor = c("F1", "F2"),
  fill = "lightblue",
  angle = 0,
  family = "sans",
  color = "rainbow",
  legend = "Legend",
  errorbar = TRUE,
  addmean = TRUE,
  textsize = 12,
  labelsize = 4,
  dec = 3,
  ylim = NA,
  posi = "right",
  point = "mean_se",
  angle.label = 0
)

Value

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 multiple comparisons (Tukey, LSD, Scott-Knott or Duncan) or adjustment of regression models up to grade 3 polynomial, in the case of quantitative treatments. The column chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Arguments

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)
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)
names.fat: Name of factors
mcomp: Multiple comparison test (Tukey (default), LSD, Scott-Knott and Duncan)
grau: Polynomial degree in case of quantitative factor (default is 1). Provide a vector with three elements.
grau12: Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 2, in the case of interaction f1 x f2 and qualitative factor 2 and quantitative factor 1.
grau21: Polynomial degree in case of quantitative factor (default is 1). Provide a vector with n levels of factor 1, in the case of interaction f1 x f2 and qualitative factor 1 and quantitative factor 2.
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)
xlab.factor: Provide a vector with two observations referring to the x-axis name of factors 1 and 2, respectively, when there is an isolated effect of the factors. This argument uses `parse`.
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)
labelsize: Label size (default is 4)
dec: Number of cells (default is 3)
ylim: y-axis limit
posi: Legend position
point: This function defines whether the point must have all points ("all"), mean ("mean"), standard deviation (default - "mean_sd") or mean with standard error ("mean_se") if quali= FALSE. For quali=TRUE, `mean_sd` and `mean_se` change which information will be displayed in the error bar.
angle.label: Label angle

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


#===================================
# 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)"))

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