VCA (version 1.5.1)

isBalanced: Check Whether Design Is Balanced Or Not

Description

Assess whether an experimental design is balanced or not.

Usage

isBalanced(form, Data, na.rm = TRUE)

Value

(logical) TRUE if data is balanced, FALSE if data is unbalanced (according to the definition of balance used)

Arguments

form

(formula) object defining the experimental design.

Data

(data.frame) containing all variables appearing in 'form'.

na.rm

(logical) TRUE = delete rows where any is NA, FALSE = NAs are not removed, if there are NAs in the response variable and all information in independent variables is available, then only the design is checked.

Author

Andre Schuetzenmeister

Details

This function is for internal use only. Thus, it is not exported.

The approach taken here is to check whether each cell defined by one level of a factor are all equal or not. Here, data is either balanced or unbalanced, there is no concept of "planned unbalancedness" as discussed e.g. in Searle et al. (1992) p.4. The expanded (simplified) formula is divided into main factors and nested factors, where the latter are interaction terms. The \(N\)-dimensional contingency table, \(N\) being the number of main factors, is checked for all cells containing the same number. If there are differences, the dataset is classified as "unbalanced". All interaction terms are tested individually. Firstly, a single factor is generated from combining factor levels of the first \((n-1)\) variables in the interaction term. The last variable occuring in the interaction term is then recoded as factor-object with \(M\) levels. \(M\) is the number of factor levels within each factor level defined by the first \((n-1)\) variables in the interaction term. This is done to account for the independence within sub-classes emerging from the combination of the first \((n-1)\) variables.

Examples

if (FALSE) {
data1 <- data.frame(site=gl(3,8), lot=factor(rep(c(2,3,1,2,3,1), 
rep(4,6))), day=rep(1:12, rep(2,12)), y=rnorm(24,25,1))

# not all combinations of 'site' and 'lot' in 'data1'

VCA:::isBalanced(y~site+lot+site:lot:day, data1)

# balanced design for this model

VCA:::isBalanced(y~lot+lot:day, data1)

# gets unbalanced if observation is NA

data1[1,"y"] <- NA
VCA:::isBalanced(y~lot+lot:day, data1)
VCA:::isBalanced(y~lot+lot:day, data1, FALSE)
}