unstableMaps | R Documentation |
Tips for exploring maps that are difficult to find a consistent optimal solution for.
Maps may be difficult to optimize or unstable for a variety of reasons, a common one with larger maps being simply that it is difficult to find a global optima and so many different local optima are found each time.
One approach that can sometimes
help is to consider running the optimizer with options = list(dim_annealing = TRUE)
(see see vignette("intro-to-antigenic-cartography")
for an explanation of the
dimensional annealing approach). However be wary that in our experience, while applying
dimensional annealing can sometimes significantly speed up finding a better minima, it
can also sometimes be more prone to getting stuck in worse local optima.
If there are many missing or non-detectable titers it is also
possible that points in map are too poorly connected to find a robust
solution, to check this see mapCohesion()
.
Other map diagnostic functions:
agCohesion()
,
bootstrapBlobs()
,
bootstrapMap()
,
checkHemisphering()
,
dimensionTestMap()
,
logtiterTable()
,
map-table-distances
,
mapBootstrapCoords
,
mapDistances()
,
mapRelaxed()
,
mapResiduals()
,
pointStress
,
ptBootstrapBlob
,
ptBootstrapCoords()
,
ptLeverage
,
ptTriangulationBlob
,
recalculateStress()
,
stressTable()
,
tableColbases()
,
tableDistances()
,
triangulationBlobs()
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