check_mon_within {baskexact} | R Documentation |
Checks whether the within-trial monotonicity condition holds.
check_mon_within(
design,
n,
lambda,
epsilon,
tau,
logbase = 2,
prune,
details,
...
)
## S4 method for signature 'OneStageBasket'
check_mon_within(
design,
n,
lambda,
epsilon,
tau,
logbase = 2,
prune,
details,
...
)
design |
An object of class |
n |
The sample size per basket. |
lambda |
The posterior probability threshold. See details for more information. |
epsilon |
A tuning parameter that determines the amount of borrowing. See details for more information. |
tau |
A tuning parameter that determines how similar the baskets have to be that borrowing occurs. See details for more information. |
logbase |
A tuning parameter that determines which logarithm base is used to compute the Jensen-Shannon divergence. See details for more information. |
prune |
Whether baskets with a number of responses below the critical pooled value should be pruned before the final analysis. |
details |
Whether the cases where the monotonicity condition is violated should be returned, in case there are any. |
... |
Further arguments. |
check_mon_within
checks whether the within-trial
monotonicity condition holds. For a single-stage design with equal
prior distributions and equal sample sizes for each basket this condition
states that there are no cases where the null hypothesis of a basket is
rejected when there is at least one other basket with more observed
responses for which the null hypothesis cannot be rejected.
If prune = TRUE
then the baskets with an observed number of baskets
smaller than the pooled critical value are not borrowed from. The
pooled critical value is the smallest integer c for which all null
hypotheses can be rejected if the number of responses is exactly c for
all baskets.
This method is implemented for the class OneStageBasket
.
If details = FALSE
then only a logical value is returned.
If details = TRUE
then if there are any cases where the
within-trial monotonicity condition is violated, a list of these cases and
their results are returned.
OneStageBasket
: Within-trial monotonicity condition for a
single-stage design.
design <- setupOneStageBasket(k = 4, shape1 = 1, shape2 = 1, theta0 = 0.2)
check_mon_within(design = design, n = 24, lambda = 0.99, epsilon = 0.5,
tau = 0, prune = FALSE, details = TRUE)