DIC {AgroR} R Documentation

Analysis: Completely randomized design

Description

Statistical analysis of experiments conducted in a completely randomized and balanced design with a factor considering the fixed model. The function presents the option to use non-parametric method or transform the dataset.

Usage

DIC(
trat,
response,
norm = "sw",
homog = "bt",
alpha.f = 0.05,
alpha.t = 0.05,
quali = TRUE,
mcomp = "tukey",
grau = 1,
transf = 1,
constant = 0,
test = "parametric",
mcompNP = "LSD",
geom = "bar",
theme = theme_classic(),
ylab = "Response",
sup = NA,
CV = TRUE,
xlab = "",
fill = "lightblue",
angle = 0,
family = "sans",
textsize = 12,
labelsize = 4,
dec = 3,
width.column = NULL,
width.bar = 0.3,
errorbar = TRUE,
posi = "top",
point = "mean_sd",
angle.label = 0,
ylim = NA
)


Arguments

 trat Numerical or complex vector with treatments response Numerical vector containing the response of the experiment. 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 (default is 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, 'angular' for angular transformation) constant Add a constant for transformation (enter value) test "parametric" - Parametric test or "noparametric" - non-parametric test mcompNP Multiple comparison test (LSD (default) or dunn) p.adj Method for adjusting p values for Kruskal-Wallis ("none","holm","hommel", "hochberg", "bonferroni", "BH", "BY", "fdr") geom Graph type (columns, boxes or segments) theme ggplot2 theme (default is theme_classic()) ylab Variable response name (Accepts the expression() function) sup Number of units above the standard deviation or average bar on the graph CV Plotting the coefficient of variation and p-value of Anova (default is TRUE) 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 textsize Font size labelsize Label size dec Number of cells width.column Width column if geom="bar" width.bar Width errorbar addmean Plot the average value on the graph (default is TRUE) errorbar Plot the standard deviation bar on the graph (In the case of a segment and column graph) - default is TRUE posi Legend position point Defines whether to plot mean ("mean"), mean with standard deviation ("mean_sd" - default) or mean with standard error ("mean_se"). For quali=FALSE or quali=TRUE. For parametric test it is possible to plot the square root of QMres (mean_qmres) angle.label label angle ylim Define a numerical sequence referring to the y scale. You can use a vector or the 'seq' command.

Value

The table of analysis of variance, the test of normality of errors (Shapiro-Wilk ("sw"), Lilliefors ("li"), Anderson-Darling ("ad"), Cramer-von Mises ("cvm"), Pearson ("pearson") and Shapiro-Francia ("sf")), the test of homogeneity of variances (Bartlett ("bt") or Levene ("levene")), the test of independence of Durbin-Watson errors, the test of multiple comparisons (Tukey ("tukey"), LSD ("lsd"), Scott-Knott ("sk") or Duncan ("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 Kruskal-Wallis test. The column, segment or box chart for qualitative treatments is also returned. The function also returns a standardized residual plot.

Note

Enable ggplot2 package to change theme argument.

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.

Post hoc test in nonparametric is using the criterium Fisher's least significant difference (p-adj="holm").

CV and p-value of the graph indicate coefficient of variation and p-value of the F test of the analysis of variance.

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, 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.

W.J. Conover, Practical Nonparametrics Statistics. 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.

Mendiburu, F., and de Mendiburu, M. F. (2019). Package ‘agricolae’. R Package, Version, 1-2.

Hothorn, T. et al. Package ‘lmtest’. Testing linear regression models. https://cran. r-project. org/web/packages/lmtest/lmtest. pdf. Accessed, v. 6, 2015.

Examples

library(AgroR)
data(pomegranate)

with(pomegranate, DIC(trat, WL, ylab = "Weight loss (%)")) # tukey
with(pomegranate, DIC(trat, WL, mcomp = "sk", ylab = "Weight loss (%)"))
with(pomegranate, DIC(trat, WL, mcomp = "duncan", ylab = "Weight loss (%)"))

#=============================
# Kruskal-Wallis
#=============================
with(pomegranate, DIC(trat, WL, test = "noparametric", ylab = "Weight loss (%)"))

#=============================
# chart type
#=============================
with(pomegranate, DIC(trat, WL, geom="point", ylab = "Weight loss (%)"))
with(pomegranate, DIC(trat, WL, ylab = "Weight loss (%)", xlab="Treatments"))

#=============================
# quantitative factor
#=============================
data("phao")
with(phao, DIC(dose,comp,quali=FALSE,grau=2,
xlab = expression("Dose"~(g~vase^-1)),
ylab="Leaf length (cm)"))

#=============================
# data transformation
#=============================
data("pepper")
with(pepper, DIC(Acesso, VitC, transf = 0,ylab="Vitamin C"))


[Package AgroR version 1.3.3 Index]