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

STRIPLOT(
f1,
f2,
block,
response,
norm = "sw",
alpha.f = 0.05,
transf = 1,
textsize = 12,
labelsize = 4,
constant = 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)

transf

Applies data transformation (default is 1; for log consider 0)

textsize

Font size (default is 12)

labelsize

Label size (default is 4)

constant

Add a constant for transformation (enter value)

## 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 function also returns a standardized residual plot.

## 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 block randomized design in strip-plot.
library(AgroR)
data(tomate)
with(tomate, STRIPLOT(parc, subp, bloco, resp))
#>
#> -----------------------------------------------------------------
#> Normality of errors
#> -----------------------------------------------------------------
#>                          Method Statistic   p.value
#>  Shapiro-Wilk normality test(W) 0.9851279 0.3522956
#>
#> 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
#> -----------------------------------------------------------------
#> Interaction
#>                               Method Statistic   p.value
#>  Bartlett test(Bartlett's K-squared)  37.60184 0.0280913
#>
#> As the calculated p-value is less than the 5% significance level, H0 is rejected. Therefore, the variances are not homogeneous
#>
#> -----------------------------------------------------------------
#> -----------------------------------------------------------------
#>
#> CV1 (%) =  8.64
#> CV2 (%) =  4.78
#> CV3 (%) =  6.36
#> Mean =  0.2433
#> Median =  0.2402
#>
#>
#> -----------------------------------------------------------------
#> Analysis of Variance
#> -----------------------------------------------------------------
#>           Df      Sum Sq       Mean Sq   F value      Pr(>F)
#> Block      1 0.0010326353 0.0010326353  3.056282 0.140844575
#> F1         5 0.0127791593 0.0025558319  5.781571 0.038403336
#> Error A    5 0.0022103264 0.0004420653
#> F2         3 0.0333335724 0.0111111908 81.994663 0.002237162
#> Error B    3 0.0004065334 0.0001355111
#> F1:F2     15 0.0040128492 0.0002675233  1.116060 0.361356071
#> Residuals 63 0.0151013155 0.0002397034
#>
#>
#> Your analysis is not valid, suggests using a try to transform the data
#>