knitr::opts_chunk$set( collapse = TRUE, comment = "#>", error = TRUE, fig.width = 6, fig.height = 6 )

library(designit) library(tidyverse)

This example demonstrates that by using customized shuffling functions, it is possible to restrain the design optimization to only score sample arrangements that fit the given constraints.

Key idea is that every reshuffling produces a 'valid' sample permutation that is not violating those constraints, even if the suggested solution may be quite bad. During optimization, we pick the best design solution from the possible ones by appropriate scoring.

The user is free to implement custom shuffling functions and pass those to the optimizer. However, some knowledge is
required regarding the internal workings of the optimization and batch container setup. Therefore the package provides
a little generic constructor for customized shufflings **shuffle_grouped_data** in which certain types of constraints
that relate to grouped samples may be specified by the passed parameters.

We refer to a simplified version of the *in vivo* example which is examined deeper in a dedicated vignette.

data("invivo_study_samples") invivo_study_samples <- dplyr::mutate(invivo_study_samples, Litter_combine_females = ifelse(Sex == "F", "female_all", Litter) ) str(invivo_study_samples) invivo_study_samples |> dplyr::count(Strain, Sex, Litter_combine_females) |> gt::gt()

We will use the litter as a factor to form cages in our design. However, in order to indicate the compatibility
of female animals (see *in vivo* study vignette), a pseudo-litter `female_all`

is created here
to group all the females together, marking them as interchangeable for the subgroup (i.e. cage) allocation.

In the simplified setup we want to assign two treatments to those animals, balancing for strain and sex as the primary suspected confounders. The batch container is prepared as follows:

treatments <- factor(rep(c("Treatment A", "Treatment B"), c(30, 29))) table(treatments) bc <- BatchContainer$new(locations_table = data.frame(Treatment = treatments, Position = seq_along(treatments))) bc <- assign_in_order(bc, invivo_study_samples) scoring_f <- osat_score_generator(batch_vars = "Treatment", feature_vars = c("Strain", "Sex")) bc

As noted, we have to assign animals to cages in this example. The cage is thus acting as the grouping factor for the samples (animals) on which we may want to put further constraints. Concretely:

- We want to form cages with ideally 3 animals each (tolerated/preferred range is from 2-5)
- Variables Strain, Sex and Treatment must be homogeneous within cage
- Animals of different litters must not be put into the same cage
- If at all possible, avoid putting animals with the same ear markings into one cage

We will tackle the usual factor balancing (using the osat score) and the additional constraints at the same time, combined in one conceptional framework.

As stated, the main idea is to provide a customized shuffling function that ensures that only 'suitable' design proposals are generated and passed to the scoring function which will then identify a good one.

Also keep in mind that what is the cage here could be any subgroup into which samples have to be partitioned.

The wrapper `shuffle_grouped_data`

allows to construct a shuffling function that satisfies all constraints
defined above at the same time. It can be passed to the optimizer together with other user defined options
such as the scoring or acceptance functions.

bc2 <- optimize_design( bc, scoring = scoring_f, shuffle_proposal_func = shuffle_grouped_data(bc, allocate_var = "Treatment", keep_together_vars = c("Strain", "Sex"), keep_separate_vars = c("Earmark"), subgroup_var_name = "Cage", n_min = 2, n_ideal = 3, n_max = 5, strict = TRUE, report_grouping_as_attribute = TRUE ), max_iter = 600 ) design <- bc2$get_samples()

`allocate_var`

is the batch container variable that should be primarily assigned to individual samples.

`keep_together_vars`

is a list of variables that must be homogeneous within a subgroup (here: cage).

`keep_separate_vars`

lists variables which should have different values within a subgroup (here: cage), if at all possible. This is a soft constraint and will be relaxed in a stepwise way until solutions can be found.

`subgroup_var_name`

allows to give the generated subgroup variable a useful name.

`n_min`

, `n_max`

and `n_ideal`

specify the minimal, maximal and ideal group sizes, respectively. It is often necessary
to release the `strict`

criterion to find any solution at all that satisfies those size criteria.

`report_grouping_as_attribute`

allows, if TRUE, to add the updated group variable into the batch container at each
iteration, so that scoring functions could make use of this variable (here: cage)!

Following the output of the optimizer, we see that a solution was identified that satisfies all constraints, with the exception of tolerating one violation of earmark-uniqueness within a cage.

The following cages (homogeneous in strain, sex and treatment) have been generated in the process:

design |> dplyr::count(Cage, Strain, Sex, Treatment) |> gt::gt()

`shuffle_grouped_data`

is a wrapper that consecutively calls other helper function. As an addendum, let us break
the whole procedure down into parts that show what is happening internally at each step.

We have to divide our animal cohort into subgroups with same strain and sex, meeting size constraints as
stated above. Since 2-5 animals should go into one cage, we specify `n_min`

and `n_max`

accordingly. `n_ideal`

would be selected by default as the mean of those two, but we specify it explicitly here, too.

The homogeneity of subgroups regarding strain and sex is achieved by listing those two parameters as `keep_together_vars`

.

Assignment of treatments should be performed as well at some point. We thus specify Treatment as the `allocation variable`

.

Note that the `Treatment`

variable is technically a batch container location and not a part of the sample list. This
distinction does not matter at this point. However, all required variables must exist in the batch container object.

The following call to `form_homogeneous_subgroups()`

produces an object that holds all relevant information
about the samples, the allocation variable and the sizes of the subgroups that have to be formed. It is NOT
decided, however, which animal will end up in which subgroup. This will be a matter of optimization later on.

subg <- form_homogeneous_subgroups( batch_container = bc, allocate_var = "Treatment", keep_together_vars = c("Strain", "Sex", "Litter_combine_females"), subgroup_var_name = "Cage", n_min = 2, n_ideal = 3, n_max = 5 )

In this example, `r sum(purrr::map_int(subg$Subgroup_Sizes, length))`

subgroups have to be formed to meet all constraints.

It is possible to obtain more information from the returned list object. Inspection of element `Subgroup_Sizes`

tells us that `r length(subg$Subgroup_Sizes)`

'animal pools' have to be formed which are homogeneous in the relevant
parameters (here: strain and sex). Each of those groups happens to be split in subgroups with a size between
`r min(unlist(subg$Subgroup_Sizes))`

and `r max(unlist(subg$Subgroup_Sizes))`

animals , which will
later constitute the individual cages.

```
subg$Subgroup_Sizes
```

Each subgroup of animals receives one specific treatment. Or more generally: subgroups have to be homogeneous regarding the allocation variable.

This introduces another type of constraint, since numbers have to add up to 10 'Control' and 10 'Compound' cases, as
given by the `treatments`

variable. As a next step, we have to find all possible combinations of subgroups which produce
valid options for the treatment allocation. That's done with the next call.

This will find a large number of different ways to assign treatments to subgroups that lead to the correct overall number of treated animals.

possible <- compile_possible_subgroup_allocation(subg)

So far we only know the sizes of subgroups (i.e. cages). Thus, in a last step we have to assign specific animals to the various subgroups. Ideally each group of 'equivalent animals' (in terms of strain and sex) is split up into more than one subgroup, so there's many potential ways to assign animals to those.

To allow optimization as usual, we want to generate a shuffling function that produces only valid solutions in terms of our
constraints, so that optimization can iterate efficiently over this solution space. The function can be generated by calling
*shuffle_with_subgroup_formation()* with the previously created subgrouping object and the list of possible treatment allocations.

Every call to this shuffling function will return a permutation index (of the original samples) that constitutes a valid solution to be scored.

The permutation function actually also constructs a 'Cage' variable (see parameter `subgroup_var_name`

in the call to
*form_homogeneous_subgroups()*). To make this parameter available and join it to the samples in the batch container,
use flag `report_grouping_as_attribute`

in the construction of the permutation function.

shuffle_proposal <- shuffle_with_subgroup_formation(subg, possible, report_grouping_as_attribute = TRUE) shuffle_proposal()

Calling the shuffle proposal function repeatedly produces a valid (constraint-aware) sample arrangement each time, with the grouping variable (here: Cage) reported alongside. (The optimizer will merge the 'Cage' variable into the batch container after each iteration, so that it can be used for scoring as if it would have been in the container from the beginning!)

We can finally use the customized shuffling function in the optimization.

bc3 <- optimize_design( bc, scoring = scoring_f, shuffle_proposal_func = shuffle_proposal, max_iter = 300 ) design <- bc3$get_samples() # Obeying all constraints does not lead to a very balanced sample allocation: dplyr::count(design, Treatment, Strain) |> gt::gt() dplyr::count(design, Treatment, Sex) |> gt::gt()

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