DivE (version 1.3)

ScoreSingleMod: ScoreSingleMod

Description

Determines the set of scores corresponding to a single model fit to a diversity values of subsamples of a given sample and its nested samples.

Usage

ScoreSingleMod(fsm, precision.lv=c(0.0001, 0.005, 0.005), plaus.pen=500)

Value

A list of class ScoreSingleMod containing the scores of the fit of the model to the diversity samples. This includes the following:

discrepancy

score for discrepancy, aggregated across all nested subsamples

accuracy

score for accuracy of full sample prediction, aggregated across all nested subsamples

similarity

score for similarity of curves for different samples

plausibility

score for plausibility criterion

binsize

vector of user-specified precision values used to translate values associated with each criterion into scores

plausibility.penalty

penalty score for implausible diversity curve

modname

model name

Arguments

fsm

FitSingleMod object

precision.lv

vector of precision level values for each criterion: 1. discrepancy -- mean percentage error between rarefaction data points and model predicion, 2. Sample accuracy -- percentage error between observed diversity of full rarefaction data and estimated diversity of full data from subsample, 3. local similarity. The scores for each criteria are defined as 1 + (multiples of bin sizes)

plaus.pen

penalty score for breaking the plausibility criterion: a model fit should be monotonically increasing and should have a slowing rate of species accumulation.

Author

Daniel J. Laydon, Aaron Sim, Charles R.M. Bangham, Becca Asquith

Details

The score for a given model is only meaningful when compared with scores of other models. Lower score = better for predicting the population diversity. To assess the performance of a single model, it is more informative to use FitSingleMod function.

References

Laydon, D. J., Melamed, A., Sim, A., Gillet, N. A., Sim, K., Darko, S., Kroll, S., Douek, D. C., Price, D., Bangham, C. R. M., Asquith, B., Quantification of HTLV-1 clonality and TCR diversity, PLOS Comput. Biol. 2014

See Also

FitSingleMod

Examples

require(DivE)
data(Bact1)
data(ModelSet)
data(ParamSeeds)
data(ParamRanges)

testmodels <- list()
testmeta <- list()
paramranges <- list()   

# Choose a single model

testmodels <- c(testmodels, ModelSet[1])
# testmeta <- (ParamSeeds[[1]]) # Commented out for sake of brevity)
testmeta <- matrix(c(0.9451638, 0.007428265, 0.9938149, 1.0147441, 0.009543598, 0.9870419),
                        nrow=2, byrow=TRUE, dimnames=list(c(), c("a1", "a2", "a3"))) # Example seeds
paramranges <- ParamRanges[[1]]

# Create DivSubsamples object (NB: For quick illustration only -- not default parameters)
dss_1 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=40, NResamples=5)
dss_2 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=65, NResamples=5)
dss <- list(dss_2, dss_1)

# Fit the model (NB: For quick illustration only -- not default parameters)
fsm <- FitSingleMod(model.list=testmodels, init.param=testmeta, param.range=paramranges,
                    main.samp=Bact1, dssamps=dss, fitloops=1, data.default=FALSE,
                    subsizes=c(65, 40), 
                    numit=2) # numit chosen to be extremely small to speed up example


# Score the model
ssm <- ScoreSingleMod(fsm)

ssm
summary(ssm)