Analysis: Randomized block design evaluated over time
DBCT.RdFunction of the AgroR package for analysis of experiments conducted in a balanced qualitative, single-factorial randomized block design with multiple assessments over time, however without considering time as a factor.
DBCT(
trat,
block,
time,
response,
alpha.f = 0.05,
alpha.t = 0.05,
mcomp = "tukey",
geom = "bar",
theme = theme_classic(),
fill = "gray",
ylab = "Response",
xlab = "Independent",
textsize = 12,
labelsize = 5,
pointsize = 4.5,
error = TRUE,
family = "sans",
sup = 0,
addmean = FALSE,
posi = c(0.1, 0.8),
legend = "Legend",
ylim = NA,
width.bar = 0.2,
size.bar = 0.8,
dec = 3,
xnumeric = FALSE,
all.letters = FALSE
)Arguments
- trat
Numerical or complex vector with treatments
- block
Numerical or complex vector with blocks
- time
Numerical or complex vector with times
- response
Numerical vector containing the response of the experiment.
- 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)
- mcomp
Multiple comparison test (Tukey (default), LSD ("lsd"), Scott-Knott ("sk"), Duncan ("duncan") and Friedman ("fd"))
- geom
Graph type (columns - "bar" or segments "point")
- theme
ggplot2 theme (default is theme_classic())
- fill
Defines chart color (to generate different colors for different treatments, define fill = "trat")
- ylab
Variable response name (Accepts the expression() function)
- xlab
Treatments name (Accepts the expression() function)
- textsize
Font size of the texts and titles of the axes
- labelsize
Font size of the labels
- pointsize
Point size
- error
Add error bar (SD)
- family
Font family
- sup
Number of units above the standard deviation or average bar on the graph
- addmean
Plot the average value on the graph (default is TRUE)
- posi
Legend position
- legend
Legend title
- ylim
Define a numerical sequence referring to the y scale. You can use a vector or the `seq` command.
- width.bar
width error bar
- size.bar
size error bar
- dec
Number of cells
- xnumeric
Declare x as numeric (default is FALSE)
- all.letters
Adds all label letters regardless of whether it is significant or not.
Value
The function returns the p-value of Anova, the assumptions of normality of errors, homogeneity of variances and independence of errors, multiple comparison test, as well as a line graph
Details
The p-value of the analysis of variance, the normality test for Shapiro-Wilk errors, the Bartlett homogeneity test of variances, the independence of Durbin-Watson errors and the multiple comparison test (Tukey, Scott-Knott, LSD or Duncan).
Note
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.
References
Principles and procedures of statistics a biometrical approach Steel & Torry & 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
rm(list=ls())
data(simulate2)
attach(simulate2)
#> The following objects are masked from laranja:
#>
#> bloco, resp, trat
#> The following objects are masked from aristolochia:
#>
#> resp, trat
#> The following objects are masked from cloro:
#>
#> bloco, resp
#> The following objects are masked from passiflora:
#>
#> bloco, trat
#===================================
# default
#===================================
DBCT(trat, bloco, tempo, resp)
#>
#> -----------------------------------------------------------------
#> ANOVA and assumptions
#> -----------------------------------------------------------------
#> p-value ANOVA Shapiro-Wilk Bartlett Durbin-Watson CV (%)
#> 1 3.210683e-04 0.04597748 0.17664020 0.2232452 11.907549
#> 2 5.870699e-06 0.32303793 0.23068072 0.6899538 4.465134
#> 3 4.517922e-04 0.31186247 0.84811700 0.3190431 4.514334
#> 4 8.164832e-03 0.46256418 0.13341771 0.3167184 5.665891
#> 5 8.687842e-04 0.11296195 0.29177701 0.3166757 2.765267
#> 6 2.543755e-04 0.55188443 0.06254508 0.5822472 2.051416
DBCT(trat, bloco, tempo, resp,fill="rainbow")
#>
#> -----------------------------------------------------------------
#> ANOVA and assumptions
#> -----------------------------------------------------------------
#> p-value ANOVA Shapiro-Wilk Bartlett Durbin-Watson CV (%)
#> 1 3.210683e-04 0.04597748 0.17664020 0.2232452 11.907549
#> 2 5.870699e-06 0.32303793 0.23068072 0.6899538 4.465134
#> 3 4.517922e-04 0.31186247 0.84811700 0.3190431 4.514334
#> 4 8.164832e-03 0.46256418 0.13341771 0.3167184 5.665891
#> 5 8.687842e-04 0.11296195 0.29177701 0.3166757 2.765267
#> 6 2.543755e-04 0.55188443 0.06254508 0.5822472 2.051416
#===================================
# segment chart
#===================================
DBCT(trat, bloco, tempo, resp, geom="point")
#>
#> -----------------------------------------------------------------
#> ANOVA and assumptions
#> -----------------------------------------------------------------
#> p-value ANOVA Shapiro-Wilk Bartlett Durbin-Watson CV (%)
#> 1 3.210683e-04 0.04597748 0.17664020 0.2232452 11.907549
#> 2 5.870699e-06 0.32303793 0.23068072 0.6899538 4.465134
#> 3 4.517922e-04 0.31186247 0.84811700 0.3190431 4.514334
#> 4 8.164832e-03 0.46256418 0.13341771 0.3167184 5.665891
#> 5 8.687842e-04 0.11296195 0.29177701 0.3166757 2.765267
#> 6 2.543755e-04 0.55188443 0.06254508 0.5822472 2.051416
