North Carolina Designs I, II and III

Source: R/carolina.R

Statistic analysis of the Carolina I, II and III genetic designs.

model = 1,2 and 3 is I, II and III see carolina1,2 and 3.

carolina(model, data)

Arguments

model

Constant
data

Data frame

Value

model model analysis (I, II or III) of caroline design

variance additive variance of male, female and male.female interaction.

References

Biometrical Methods in Quantitative Genetic Analysis, Singh, Chaudhary. 1979

See also

DC

Examples



library(agricolae)
data(DC)
carolina1 <- DC$carolina1
# str(carolina1)
output<-carolina(model=1,carolina1)
#> Response(y):  yield 
#> 
#> Analysis of Variance Table
#> 
#> Response: y
#>                             Df  Sum Sq Mean Sq F value    Pr(>F)    
#> set                          1  0.5339  0.5339  7.2120 0.0099144 ** 
#> set:replication              2  2.9894  1.4947 20.1914 4.335e-07 ***
#> set:male                     4 22.1711  5.5428 74.8743 < 2.2e-16 ***
#> set:male:female              6  4.8250  0.8042 10.8630 1.311e-07 ***
#> set:replication:male:female 10  3.2072  0.3207  4.3325 0.0002462 ***
#> Residuals                   48  3.5533  0.0740                      
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> CV: 8.286715 	Mean: 3.283333 
output[][-1]
#> $var.m
#> [1] 0.3948843
#> 
#> $var.f
#> [1] 0.08057407
#> 
#> $var.A
#> [1] 1.579537
#> 
#> $var.D
#> [1] -1.257241
#> 

carolina2 <- DC$carolina2
# str(carolina2)
majes<-subset(carolina2,carolina2[,1]==1)
majes<-majes[,c(2,5,4,3,6:8)]
output<-carolina(model=2,majes[,c(1:4,6)])
#> Response(y):  yield 
#> 
#> Analysis of Variance Table
#> 
#> Response: y
#>                 Df  Sum Sq Mean Sq F value    Pr(>F)    
#> set              1  847836  847836 45.6296 1.097e-09 ***
#> set:replication  4  144345   36086  1.9421  0.109652    
#> set:male         8  861053  107632  5.7926 5.032e-06 ***
#> set:female       8  527023   65878  3.5455  0.001227 ** 
#> set:male:female 32  807267   25227  1.3577  0.129527    
#> Residuals       96 1783762   18581                      
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> CV: 19.08779 	Mean: 714.1301 
output[][-1]
#> $var.m
#> [1] 2746.815
#> 
#> $var.f
#> [1] 1355.024
#> 
#> $var.mf
#> [1] 2215.415
#> 
#> $var.Am
#> [1] 10987.26
#> 
#> $var.Af
#> [1] 5420.096
#> 
#> $var.D
#> [1] 8861.659
#> 

carolina3 <- DC$carolina3
# str(carolina3)
output<-carolina(model=3,carolina3)
#> Response(y):  yield 
#> 
#> Analysis of Variance Table
#> 
#> Response: y
#>                 Df Sum Sq Mean Sq F value   Pr(>F)   
#> set              3  2.795 0.93167  1.2784 0.300965   
#> set:replication  4  3.205 0.80125  1.0995 0.376215   
#> set:female       4  1.930 0.48250  0.6621 0.623525   
#> set:male        12 20.970 1.74750  2.3979 0.027770 * 
#> set:female:male 12 27.965 2.33042  3.1978 0.005493 **
#> Residuals       28 20.405 0.72875                    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> CV: 21.95932 	Mean: 3.8875 
output[][-1]
#> $var.mi
#> [1] 0.8008333
#> 
#> $var.m
#> [1] 0.2546875
#> 
#> $var.A
#> [1] 1.01875
#> 
#> $var.D
#> [1] 1.601667
#>