easyanova (version 1.1)

ea3: Analysis of variance in split.plot designs (mixed model)

Description

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

Usage

ea3(data, cov, design)

Arguments

data

data is a data.frame

data frame with four columns, factor 1 (plot), repetitions or blocks, factor 2 (split.plot) and response (split.plot in completely randomized design or block design)

data frame with five columns, factor 1 (plot), cols, rows, factor

cov

Evaluated Structures

1 = Autoregressive

2 = Heterogenius Autoregressive

3 = Continuous Autoregressive Process

4 = Compound Symetry

5 = Unstructured

design

Design

1 = completely randomized design

2 = randomized block design

3 = latin square design

Value

• Returns analysis of variance, parameters of model fitting, normality test, test of homogeneity of variance, means (adjusted means), multiple comparisons tests.

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.

See Also

ea1, ea2, ea4, ea5, ea6, ea7, ea8, lme, glht

Examples

#Kaps and Lamberson (2009)
data(data7)
data(data8)

# analysis in completely randomized design (Autoregressive)
r1<-ea3(data7,1,1)

names(r1)

r1[15]

# analysis in randomized design (Compound Symetry)
r2<-ea3(data7,4,1)

r2["Adjusted Means (interaction)"]

# comparing models
r1[2];r2[c(2,15)]

# analysis in randomized block design (option 2, the third argument)
r3<-ea3(data8,1,2) # Autoregressive

names(r3)

r3["Evaluated Structures" ]

r4<-ea3(data8,4,2) # Compound Symetry 

r4[1]

r4[10]

r4[11]