Description Usage Arguments Details Value Author(s) References See Also
View source: R/seqBoundariesGrid.r
Estimate the expected utility for sequential boundaries
parameterized by (b0,b1). Expected utility is estimated on a grid of
(b0,b1) values based on a forward simulation output such as that
generated by the function forwsimDiffExpr
.
1  seqBoundariesGrid(b0, b1, forwsim, samplingCost, powmin = 0, f = "linear", ineq = "less")

b0 
Vector with b0 values. Expected utility is evaluated for a grid defined by all combinations of (b0,b1) values. 
b1 
Vector with b1 values. 
forwsim 

samplingCost 
Cost of obtaining one more data batch, in terms of the number of new truly differentially expressed discoveries that would make it worthwhile to obtain one more data batch. 
powmin 
Constraint on power. Optimization chooses the optimal

f 
Parametric form for the stopping boundary. Currently only
'linear' and 'invsqrt' are implemented. For 'linear', the boundary
is 
ineq 
For 
Intuitively, the goal is to stop collecting new data when the expected
benefit of obtaining one more data batch is small, i.e. below a
certain boundary. We consider two simple parametric forms for such a
boundary (linear and inverse square root), which allows to easily evaluate
the expected utility for each boundary within a grid of parameter
values.
The optimal boundary is defined by the parameter values achieving the
largest expected utility, restricted to parameter values with an
estimated power greater or equal than powmin
.
Here power is defined as the expected number of true discoveries
divided by the expected number of differentially expressed entities.
The routine evaluates the expected utility, as well as expected FDR, FNR, power and sample size for each specified boundary, and also reports the optimal boundary.
A list with two components:
opt 
Vector with optimal stopping boundary ( 
grid 

David Rossell.
Rossell D., Mueller P. Sequential sample sizes for highthroughput hypothesis testing experiments. http://sites.google.com/site/rosselldavid/home.
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. Annals of Applied Statistics, 2009, 3, 10351051.
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