You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Would be good to have lcMethod subclasses which can fit an underlying lcMethod multiple times to select the best one automatically. This makes the latrend fitting procedure a lot less unwieldy for methods with poor convergence rate
Not sure yet if meta is the best term for this.
Proposed methods:
lcMetaConverged(method, maxRep = 10) fit repeatedly until a converged model is obtained
lcMetaRep(method, rep = 10, metric = "BIC") fit N models and return the best fit according to the given metric. Requires knowing whether the metric should be minimized or maximized
lcMetaStable(method, stableFun) fit repeatedly until the maximum metric value appears to have stabilized. Then return the best model. Might be difficult to define.
lcMetaGridsearch(method, options, metric = "BIC"): fit method for all combinations of parameters, and select the best model according to the given metric
Additionally, meta methods could enforce a certain clustering ordering of a fitted method, during postFit().
Ideas:
ordering by intercept
ordering by cluster size
ordering to match the partitioning of a reference model
The text was updated successfully, but these errors were encountered:
* Implemented lcMetaMethod and lcMetaConverged (#61)
* Added more tests and support for setting consecutive seeds (#61)
* Renamed lcMetaConverged to lcFitConverged (#61)
* Implemented lcFitRep and variants(#61)
* Added convergence status in messaging (#61)
Small changes:
* Added check for formula argument to LMKM method
* Added converged slot to lcModelPartition
* Made getLcMethod() generic
* Generic validate() checks for correct output length
* Fixed doc check issues
* Added workaround for erroneous R CMD check rmarkdown import note
Would be good to have lcMethod subclasses which can fit an underlying lcMethod multiple times to select the best one automatically. This makes the latrend fitting procedure a lot less unwieldy for methods with poor convergence rate
Not sure yet if meta is the best term for this.
Proposed methods:
lcMetaConverged(method, maxRep = 10)
fit repeatedly until a converged model is obtainedlcMetaRep(method, rep = 10, metric = "BIC")
fit N models and return the best fit according to the given metric. Requires knowing whether the metric should be minimized or maximizedlcMetaStable(method, stableFun)
fit repeatedly until the maximum metric value appears to have stabilized. Then return the best model. Might be difficult to define.lcMetaGridsearch(method, options, metric = "BIC")
: fit method for all combinations of parameters, and select the best model according to the given metricAdditionally, meta methods could enforce a certain clustering ordering of a fitted method, during postFit().
Ideas:
The text was updated successfully, but these errors were encountered: