invert uppercase and lowercase letters in graph for factorial scheme the subdivided plot with significant interaction

ibarplot.double(analysis)

Arguments

analysis

FAT2DIC, FAT2DBC, PSUBDIC or PSUBDBC object

Value

Return column chart for two factors

Examples

data(covercrops)
attach(covercrops)
#> The following objects are masked from covercrops (pos = 4):
#> 
#>     A, B, Bloco, Resp
a=FAT2DBC(A, B, Bloco, Resp, ylab=expression("Yield"~(Kg~"100 m"^2)),
legend = "Cover crops",alpha.f = 0.3,family = "serif")
#> 
#> -----------------------------------------------------------------
#> Normality of errors
#> -----------------------------------------------------------------
#>                          Method Statistic   p.value
#>  Shapiro-Wilk normality test(W) 0.9758908 0.6061712
#> 
#> 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
#> -----------------------------------------------------------------
#>                               Method Statistic   p.value
#>  Bartlett test(Bartlett's K-squared)  10.19232 0.2517862
#> 
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, the variances can be considered homogeneous
#> 
#> -----------------------------------------------------------------
#> Independence from errors
#> -----------------------------------------------------------------
#>                  Method Statistic   p.value
#>  Durbin-Watson test(DW)  2.335214 0.3781148
#> 
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered independent
#> 
#> -----------------------------------------------------------------
#> Additional Information
#> -----------------------------------------------------------------
#> 
#> CV (%) =  2.65
#> Mean =  37.4583
#> Median =  37.45
#> Possible outliers =  9 10 18 20 28 31
#> 
#> -----------------------------------------------------------------
#> Analysis of Variance
#> -----------------------------------------------------------------
#>               Df     Sum Sq   Mean.Sq   F value        Pr(F)
#> Fator1         2 146.585000 73.292500 74.645449 4.979960e-11
#> Fator2         2  23.021667 11.510833 11.723318 2.805884e-04
#> bloco          3   2.167500  0.722500  0.735837 5.409183e-01
#> Fator1:Fator2  4   6.008333  1.502083  1.529811 2.252749e-01
#> Residuals     24  23.565000  0.981875                       
#> 
#> -----------------------------------------------------------------
#> 
#> Significant interaction: analyzing the interaction
#> 
#> -----------------------------------------------------------------
#> 
#> -----------------------------------------------------------------
#> Analyzing  F1  inside of each level of  F2
#> -----------------------------------------------------------------
#>                     Df  Sum Sq Mean Sq F value    Pr(>F)    
#> bloco                3   2.168   0.723  0.7358 0.5409183    
#> Fator2               2  23.022  11.511 11.7233 0.0002806 ***
#> Fator2:Fator1        6 152.593  25.432 25.9017 2.296e-09 ***
#>   Fator2:Fator1: B1  2  51.165  25.583 26.0547 9.667e-07 ***
#>   Fator2:Fator1: B2  2  34.427  17.213 17.5311 2.027e-05 ***
#>   Fator2:Fator1: B3  2  67.002  33.501 34.1192 9.629e-08 ***
#> Residuals           24  23.565   0.982                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> -----------------------------------------------------------------
#> Analyzing  F2  inside of the level of  F1
#> -----------------------------------------------------------------
#> 
#>                     Df  Sum Sq Mean Sq F value   Pr(>F)    
#> bloco                3   2.168   0.723  0.7358 0.540918    
#> Fator1               2 146.585  73.293 74.6454 4.98e-11 ***
#> Fator1:Fator2        6  29.030   4.838  4.9276 0.002054 ** 
#>   Fator1:Fator2: A1  2   1.995   0.998  1.0159 0.377120    
#>   Fator1:Fator2: A2  2  15.495   7.748  7.8905 0.002325 ** 
#>   Fator1:Fator2: A3  2  11.540   5.770  5.8765 0.008371 ** 
#> Residuals           24  23.565   0.982                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> -----------------------------------------------------------------
#> Final table
#> -----------------------------------------------------------------
#>          B1      B2      B3
#> A1  34.5 bA 34.8 bA 35.4 cA
#> A2 37.8 aAB 36.2 bB 39.0 bA
#> A3  39.4 aB 38.9 aB 41.1 aA
#> 
#> 
#> Averages followed by the same lowercase letter in the column 
#> and uppercase in the row do not differ by the tukey (p< 0.05 )

ibarplot.double(a)