partition_dataset: Partition latin-hypercube summary file to training, testing,...
In spartan: Simulation Parameter Analysis R Toolkit ApplicatioN: 'spartan'

Description Usage Arguments Value Examples

Used in the development of emulations of a simulation using a latin-hypercube summary file

partition_dataset(dataset, parameters, measures, percent_train = 75,
  percent_test = 15, percent_validation = 10, seed = NULL,
  normalise = FALSE, sample_mins = NULL, sample_maxes = NULL,
  timepoint = NULL)

`dataset`	LHC summary file to partition
`parameters`	Simulation parameters the emulation will be fed as input
`measures`	Simulation responses of interest
`percent_train`	Percent of the dataset to use as training
`percent_test`	Percent of the dataset to use as testing
`percent_validation`	Percent of the dataset to use as validation
`seed`	For specifying a particular seed when randomly splitting the set
`normalise`	Whether the data needs to be normalised (to be between 0 and 1). For emulation creation to be successful, all data must be normalised prior to use in training and testing
`sample_mins`	The minimum value used for each parameter in generating the latin-hypercube sample
`sample_maxes`	The maximum value used for each parameter in generating the latin-hypercube sample
`timepoint`	Simulation timepoint for which this summary file was created

Partitioned dataset containing training, testing, and validation sets, in addition to the sample mins and maxes such that any predictions that are generated using this normalised data can be rescaled correctly

data("sim_data_for_emulation")
parameters<-c("stableBindProbability","chemokineExpressionThreshold",
"initialChemokineExpressionValue","maxChemokineExpressionValue",
"maxProbabilityOfAdhesion","adhesionFactorExpressionSlope")
measures<-c("Velocity","Displacement","PatchArea")
sample_maxes <- cbind(100,0.9,0.5,0.08,1,5)
sample_mins <-cbind(0,0.1,0.1,0.015,0.1,0.25)
partitionedData <- partition_dataset(sim_data_for_emulation, parameters,
measures, percent_train=75, percent_test=15, percent_validation=10, normalise=TRUE,
sample_mins = sample_mins, sample_maxes = sample_maxes)