# ea2: Analysis of variance in factorial and split plot In easyanova: Analysis of Variance and Other Important Complementary Analyses

## Description

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

## Usage

 `1` ```ea2(data, design = 1, alpha = 0.05, cov = 4, list = FALSE, p.adjust=1, plot=2) ```

## Arguments

 `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

## Details

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

## Value

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

## Author(s)

Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>

## References

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.

 ``` 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 # 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 ```