knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5 )

```
library(adoptr)
```

While adopt also allows implementation of custom scores via subclassing, for most applications a simple point-wise arithmetic on scores is sufficient. For instance, consider the case of a utility maximizing approach to planning where not a hard constraint on power but rather a trade-off betweem power and expected sample size is required. The simplest utility function would just be a weightes sum of both power (negative weight since we minimize costs!) and expected sample size.

Consider the following situation

H_0 <- PointMassPrior(.0, 1) H_1 <- PointMassPrior(.2, 1) datadist <- Binomial(.1, two_armed = FALSE) ess <- ExpectedSampleSize(datadist, H_1) power <- Power(datadist, H_1) toer <- Power(datadist, H_0)

Adoptr supports such `CompositeScores`

via the `composite`

function:

objective <- composite({ess - 50*power})

The new unconditional score can be evaluated as usual, e.g.

design <- TwoStageDesign( n1 = 100, c1f = .0, c1e = 2.0, n2_pivots = rep(150, 5), c2_pivots = sapply(1 + adoptr:::GaussLegendreRule(5)$nodes, function(x) -x + 2) ) evaluate(objective, design)

Note that conditional and unconditional scores cannot be mixed in an
expression passed to `composite`

.
Composite conditional score, however, are possible as well.

cp <- ConditionalPower(datadist, H_1) css <- ConditionalSampleSize() cs <- composite({css - 50*cp})

evaluate(cs, design, c(0, .5, 1))

Of course, composite conditional scores can also be integrated

evaluate(expected(cs, datadist, H_1), design)

and (due to linearity) the result is exactly the same as before.

Composite scores are not restricted to linear operations but support any valid numerical expression:

cs <- composite({log(css) - 50*sin(cp)}) evaluate(cs, design, c(0, .5, 1))

Even control flow is supported:

cs <- composite({ res <- 0 for (i in 1:3) { res <- res + css } res }) evaluate(cs, design, c(0, .5, 1))

The only real constraint is that the expression must be vectorized.

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