Rerun clustord using the results of a previous run as the starting point.

This function is designed for two purposes. (1) You tried to run clustord and the results did not converge. You can supply this function with the previous results and the previous data object, and it will carry on running clustord from the endpoint of the previous run, which is quicker than starting the run again from scratch with more iterations.

Usage

rerun(
  results.original,
  long.df,
  EM.control = NULL,
  verbose = FALSE,
  optim.control = NULL
)

Arguments

results.original: The results of the previous run that you want to use as a starting point. The model, number of clusters, and final parameter values will be used, and the cluster controls such as EMcycles will be reused unless the user specifies new values. But the row cluster and/or column cluster memberships will NOT be reused, and nor will the dataset, so you can change the dataset slightly and the rest of the details will be applied to this new dataset.
long.df: The dataset to use for this run, which may be slightly different to the original. Please note that the only compatibility check performed is comparing the sizes of the original and new datasets, and it is up to the user to check that the new dataset is sufficiently similar to the old one.
EM.control: Options to use for this run such as EMcycles (number of EM iterations). Note that "startEMcycles" will not be relevant as this run will not generate random starts, it will run from the end parameters of the other run. See clustord documentation for more info.
verbose: (default FALSE) changes how much is reported to the console during the algorithm's progress. See clustord documentation for more info.
optim.control: Options to use for this run within optim(), which is used to estimate the parameters during each M-step. See clustord documentation for more info.

Value

An object of class clustord. See clustord for more info.

Details

(2) The previous result converged, but you have changed the dataset slightly, and want to rerun from the previous endpoint to save time.

Either way, you call the function in the same way, supplying the previous results object and a dataset, and optionally a new number of iterations (`EM.control=list(EMcycles=XXX)`, where `XXX` is the new number of iterations.)

The output parameters of the old result will be used as the new initial parameters.

Examples

set.seed(1)
long.df <- data.frame(Y=factor(sample(1:3,5*20,replace=TRUE)),
               ROW=factor(rep(1:20,times=5)),COL=rep(1:5,each=20))
results.original <- clustord(Y ~ ROWCLUST, model="OSM", nclus.row=4,
                             long.df=long.df, EM.control=list(EMcycles=2))
#> Converting factor ROW to numeric.
#> EM algorithm has not converged. Please try again, or with a different random seed, or with more starting points.
results.original$EM.status$converged
#> [1] FALSE
# FALSE

## Since original run did not converge, rerun from that finishing point and
## allow more iterations this time
results.new <- rerun(results.original, long.df, EM.control=list(EMcycles=10))
#> Converting factor ROW to numeric.
#> EM algorithm has successfully converged.

## Alternatively, if dataset has changed slightly then rerun from the
## previous finishing point to give the new results a helping hand
long.df.new <- long.df[-c(4,25,140),]
results.new <- rerun(results.original, long.df.new)
#> Converting factor ROW to numeric.
#> EM algorithm has successfully converged.