isBalanced | R Documentation |

Assess whether an experimental design is balanced or not.

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

`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. |

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.

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

Andre Schuetzenmeister andre.schuetzenmeister@roche.com

```
## Not run:
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)
## End(Not run)
```

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