easyanova (version 11.0)

ea2: Analysis of variance in factorial and split plot

Description

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

Usage

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

Value

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

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

Author

Emmanuel Arnhold <[e-mail]>

Details

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

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.

See Also

ea1, ec

Examples

# 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

#split.plot in latin square
        #data(data3)
        #d=rbind(data3,data3)
        #d=data3[,-4];d=data.frame(d,time=rep(1:2,each=16),response=rnorm(32,45,4))
# r9=ea2(d,design=6)
# r9