Description Usage Arguments Details Value Author(s) References See Also Examples

Perform analysis of variance and other important complementary analyzes in factorial and split plot scheme, with balanced and unbalanced data.

1 |

`data` |
data is a data.frame see how the input data in the examples |

`design` |
1 = double factorial in completely randomized design 2 = double factorial in randomized block design 3 = double factorial in latin square design 4 = split plot in completely randomized design 5 = split plot in randomized block design 6 = split plot in latin square design 7 = triple factorial in completely randomized design 8 = triple factorial in randomized block design 9 = double factorial in split plot (completely randomized) 10 = double factorial in split plot (randomized in block) 11 = joint analysis of experiments with hierarchical blocks 12 = joint analysis of repetitions of latin squares (hierarchical rows) 13 = joint analysis of repetitions of latin squares (hierarchical rows and columns) |

`alpha` |
significance level for multiple comparisons |

`cov` |
for split plot designs 1 = Autoregressive 2 = Heterogenius Autoregressive 3 = Continuous Autoregressive Process 4 = Compound Symetry 5 = Unstructured |

`list` |
FALSE = a single response variable TRUE = multivariable response |

`p.adjust` |
1="none"; 2="holm"; 3="hochberg"; 4="hommel"; 5="bonferroni"; 6="BH", 7="BY"; 8="fdr"; for more details see function "p.adjust" |

`plot` |
1 = box plot for residuals; 2 = standardized residuals vs sequence data; 3 = standardized residuals vs theoretical quantiles |

The response variable must be numeric. Other variables can be numeric or factors.

Returns analysis of variance, means (adjusted means), multiple comparison test (tukey, snk, duncan, t and scott knott) and residual analysis.

Emmanuel Arnhold <[email protected]>

KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.

SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.

PIMENTEL-GOMES, F. and GARCIA C.H. Estatistica aplicada a experimentos agronomicos e florestais: exposicao com exemplos e orientacoes para uso de aplicativos. Editora Fealq, v.11, 2002. 309p.

RAMALHO, M. A. P.; FERREIRA, D. F. and OLIVEIRA, A. C. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA, 2005, 322p.

ea1, ec

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | ```
# double factorial
# completely randomized design
data(data5)
r1=ea2(data5, design=1)
r1
# randomized block design
# data(data6)
# r2=ea2(data6, design=2)
# r2
# names(r1)
# names(r2)
# triple factorial
# completely randomized design
# data(data9)
# r3=ea2(data9[,-4], design=7)
# r3[1]
# split plot
# completely randomized design
# data(data7)
# r4=ea2(data7, design=4)
# r4
# randomized block design
# data(data8)
# r5=ea2(data8, design=5)
# r5
# hierarchical blocks
# Ramalho et al. (2005)
# data(data18)
# data18
# r6=ea2(data18, design=11)
# r6
# hierarchical latin squares
# Sampaio (2010)
# data(data19)
# data19
# r7=ea2(data19, design=12)
# r8=ea2(data19, design=13)
# hierarchical rows
# r7
# hierarchical rows and columns
# r8
``` |

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