# ScoreSingleMod 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. ```r ScoreSingleMod(fsm, precision.lv=c(0.0001, 0.005, 0.005), plaus.pen=500) ``` ## 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. ## 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. ## Returns 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 ## 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 ## Author(s) Daniel J. Laydon, Aaron Sim, Charles R.M. Bangham, Becca Asquith ## See Also `FitSingleMod` ## Examples ```r 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) ```

ScoreSingleMod function