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

PSUBDBC(
  f1,
  f2,
  block,
  response,
  norm = "sw",
  homog = "bt",
  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"),
  color = "rainbow",
  textsize = 12,
  labelsize = 4,
  dec = 3,
  legend = "Legend",
  errorbar = TRUE,
  addmean = TRUE,
  ylim = NA,
  point = "mean_se",
  fill = "lightblue",
  angle = 0,
  family = "sans",
  posi = "right",
  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)

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 (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`.

color

When the columns are different colors (Set fill-in argument as "trat")

textsize

Font size (default is 12)

labelsize

Font size (default is 4)

dec

Number of cells (default is 3)

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)

ylim

y-axis limit

point

Point type for regression ("mean_se","mean_sd","mean" or "all")

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)

posi

Legend position

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

Examples


#==============================
# Example tomate
#==============================
library(AgroR)
data(tomate)
with(tomate, PSUBDBC(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)  0.959285 0.004550077
#> 
#> 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)  7.024669 0.2188121
#> 
#> 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)  6.098779 0.1069021
#> 
#> 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)  35.50227 0.04633559
#> 
#> As the calculated p-value is less than the 5% significance level, H0 is rejected. Therefore, the variances are not homogeneous
#> 
#> -----------------------------------------------------------------
#> Additional Information
#> -----------------------------------------------------------------
#> 
#> CV1 (%) =  9.91
#> 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  4.396538 0.012  
#> Block    3 0.003406148 0.0011353827  1.953084 0.164  
#> Error A 15 0.008719924 0.0005813283                  
#> 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 0.002852000 0.000570 2.403389 0.055831
#> F1 : F2 IN      5.00000 0.006124000 0.001225 5.160159 0.001138
#> F1 : F2 PE      5.00000 0.006021000 0.001204 5.073736 0.001275
#> F1 : F2 SA      5.00000 0.001795000 0.000359  1.51275 0.210258
#> Combined error 35.99749 0.008531405 0.000237                  
#> 
#> -----------------------------------------------------------------
#> 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 )


#==============================
# Example orchard
#==============================
library(AgroR)
data(orchard)
with(orchard, PSUBDBC(A, B, Bloco, Resp, ylab="CBM"))
#> boundary (singular) fit: see help('isSingular')
#> 
#> -----------------------------------------------------------------
#> Normality of errors
#> -----------------------------------------------------------------
#>                          Method Statistic   p.value
#>  Shapiro-Wilk normality test(W) 0.9870216 0.7739824
#> 
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered normal
#> 
#> 
#> -----------------------------------------------------------------
#> Homogeneity of Variances
#> -----------------------------------------------------------------
#> Plot
#>                               Method Statistic   p.value
#>  Bartlett test(Bartlett's K-squared) 0.2401679 0.6240849
#> 
#> 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) 0.7650993 0.9430648
#> 
#> 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)  6.046862 0.7352191
#> 
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, the variances can be considered homogeneous
#> 
#> -----------------------------------------------------------------
#> Additional Information
#> -----------------------------------------------------------------
#> 
#> CV1 (%) =  24.59
#> CV2 (%) =  20.95
#> Mean =  152.2183
#> Median =  149
#> 
#> -----------------------------------------------------------------
#> Analysis of Variance
#> -----------------------------------------------------------------
#>         Df Sum Sq    Mean Sq    F value    Pr(>F) 
#> F1       1  5539.204  5539.2042  3.9547333 0.103  
#> Block    5  2916.463   583.2926  0.4164437 0.821  
#> Error A  5  7003.259  1400.6518                   
#> F2       4 80949.099 20237.2747 19.9069145 p<0.001
#> F1 x F2  4 15004.875  3751.2187  3.6899826 0.012  
#> Error B 40 40663.810  1016.5952                   
#> 
#> -----------------------------------------------------------------
#> Significant interaction: analyzing the interaction
#> -----------------------------------------------------------------
#> Analyzing  F1  inside of each level of  F2
#> -----------------------------------------------------------------
#>                      GL         SQ        QM       Fc  p.value
#> F1 : F2 T 1     1.00000   227.9408  227.9408 0.208469 0.650633
#> F1 : F2 T 2     1.00000  2625.5208 2625.5208  2.40123 0.129731
#> F1 : F2 T 3     1.00000  3971.2408 3971.2408 3.631989 0.064451
#> F1 : F2 T 4     1.00000  4500.8133 4500.8133 4.116322 0.049695
#> F1 : F2 T 5     1.00000  9218.5633 9218.5633 8.431048  0.00618
#> Combined error 37.09388 40558.6915 1093.4066                  
#> 
#> -----------------------------------------------------------------
#> Analyzing  F2  inside of the level of  F1
#> -----------------------------------------------------------------
#>             GL       SQ        QM        Fc p.value
#> F2 : F1 EL   4 47554.07 11888.517 11.694445   2e-06
#> F2 : F1 L    4 48399.91 12099.976 11.902452   2e-06
#> Error b     40 40663.81  1016.595                  
#> 
#> -----------------------------------------------------------------
#> Final table
#> -----------------------------------------------------------------
#>         T 1      T 2       T 3       T 4       T 5
#> EL 173.2 aB 230.2 aA  116.2 aC 129.5 aBC 160.0 aBC
#> L  164.5 aA 200.6 aA 152.6 aAB   90.8 bC 104.6 bBC
#> 
#> 
#> Averages followed by the same lowercase letter in the column and uppercase in the row do not differ by the tukey (p< 0.05 )