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",
alpha.f = 0.05,
alpha.t = 0.05,
quali = c(TRUE, TRUE),
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,
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)

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

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.

## 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.

## Author

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Rodrigo Yudi Palhaci Marubayashi

## 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)"))
#> boundary (singular) fit: see help('isSingular')
#>
#> -----------------------------------------------------------------
#> Normality of errors
#> -----------------------------------------------------------------
#>                          Method Statistic     p.value
#>  Shapiro-Wilk normality test(W) 0.9636318 0.009177611
#>
#> As the calculated p-value is less than the 5% significance level, H0 is rejected. Therefore, errors do not follow a normal distribution
#>
#>
#> -----------------------------------------------------------------
#> Homogeneity of Variances
#> -----------------------------------------------------------------
#> Plot
#>                               Method Statistic   p.value
#>  Bartlett test(Bartlett's K-squared)  6.761137 0.2390196
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, the variances can be considered homogeneous
#>
#> ----------------------------------------------------
#> Split-plot
#>                               Method Statistic   p.value
#>  Bartlett test(Bartlett's K-squared)  5.451767 0.1415521
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, the variances can be considered homogeneous
#>
#> ----------------------------------------------------
#> Interaction
#>                               Method Statistic   p.value
#>  Bartlett test(Bartlett's K-squared)  34.15961 0.0628852
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, the variances can be considered homogeneous
#>
#> -----------------------------------------------------------------
#> -----------------------------------------------------------------
#>
#> CV1 (%) =  10.67
#> CV2 (%) =  4.55
#> Mean =  0.2433
#> Median =  0.2402
#>
#>
#> -----------------------------------------------------------------
#> Analysis of Variance
#> -----------------------------------------------------------------
#>         Df Sum Sq      Mean Sq      F value   Pr(>F)
#> F1       5 0.012779159 0.0025558319  3.793889 0.016
#> Error A 18 0.012126072 0.0006736707
#> F2       3 0.033333572 0.0111111908 90.570262 p<0.001
#> F1 x F2 15 0.004012849 0.0002675233  2.180653 0.019
#> Error B 54 0.006624738 0.0001226803
#>
#>
#> Your analysis is not valid, suggests using a try to transform the data
#>
#>
#> -----------------------------------------------------------------
#> Significant interaction: analyzing the interaction
#> -----------------------------------------------------------------
#> Analyzing  F1  inside of each level of  F2
#> -----------------------------------------------------------------
#>                      GL         SQ       QM       Fc  p.value
#> F1 : F2 GR      5.00000 0.00285200 0.000570 2.190341 0.074853
#> F1 : F2 IN      5.00000 0.00612400 0.001225 4.702737 0.001855
#> F1 : F2 PE      5.00000 0.00602100 0.001204 4.623975  0.00207
#> F1 : F2 SA      5.00000 0.00179500 0.000359 1.378652 0.253307
#> Combined error 39.14545 0.01017782 0.000260
#>
#> -----------------------------------------------------------------
#> Analyzing  F2  inside of the level of  F1
#> -----------------------------------------------------------------
#>               GL       SQ       QM        Fc  p.value
#> F2 : F1 AZOS   3 0.003100 0.001033  8.422958  0.00011
#> F2 : F1 AbV5   3 0.009387 0.003129 25.506107        0
#> F2 : F1 ENTB   3 0.008261 0.002754 22.446718        0
#> F2 : F1 PSMO   3 0.008936 0.002979 24.280165        0
#> F2 : F1 STPH   3 0.002889 0.000963  7.850077 0.000194
#> F2 : F1 TEST   3 0.004773 0.001591 12.967502    2e-06
#> Error b       54 0.006625 0.000123
#>
#> -----------------------------------------------------------------
#> Final table
#> -----------------------------------------------------------------
#>             GR         IN         PE       SA
#> AZOS 0.225 abB 0.248 abcA  0.257 bcA 0.227 aB
#> AbV5 0.229 abB  0.268 abA  0.286 abA 0.232 aB
#> ENTB 0.215 bBC   0.232 cB 0.267 abcA 0.209 aC
#> PSMO  0.251 aB   0.277 aA   0.294 aA 0.233 aB
#> STPH 0.226 abB 0.249 abcA   0.248 cA 0.219 aB
#> TEST 0.229 abB  0.236 bcB 0.264 abcA 0.217 aB
#>
#>
#> Averages followed by the same lowercase letter in the column and uppercase in the row do not differ by the tukey (p< 0.05 )