PSUBDIC: Analysis: DIC experiments in split-plot

In AgroR: Experimental Statistics and Graphics for Agricultural Sciences

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
)

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

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.

Note

The order of the chart follows the alphabetical pattern. Please use 'scale_x_discrete' from package ggplot2, 'limits' argument to reorder x-axis. 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.

Author(s)

Gabriel Danilo Shimizu, [e-mail]

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

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

AgroR documentation built on May 29, 2024, 4:18 a.m.