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)

Arguments

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.

Value

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

Author(s)

Daniel Laydon, Aaron Sim, Charles Bangham, Becca Asquith

References

Laydon, D., 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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(DivE)
Loading required package: deSolve

Attaching package: 'deSolve'

The following object is masked from 'package:graphics':

    matplot

Loading required package: FME
Loading required package: rootSolve
Loading required package: coda
Loading required package: rgeos
rgeos version: 0.3-19, (SVN revision 524)
 GEOS runtime version: 3.5.0-CAPI-1.9.0 r4084 
 Linking to sp version: 1.2-3 
 Polygon checking: TRUE 

Loading required package: sp
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DivE/scoresinglemod.Rd_%03d_medium.png", width=480, height=480)
> ### Name: scoresinglemod
> ### Title: scoresinglemod
> ### Aliases: scoresinglemod scoresinglemod.default print.scoresingleMod
> ###   summary.scoresingleMod print.summary.scoresingleMod
> ### Keywords: diversity
> 
> ### ** 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
Fitting loop 1 
Performing fitting routine for sample  1 
Choosing optimal initial parameters for global fit 
Performing global fit 
Performing local fit 
Performing fitting routine for sample  2 
Choosing optimal initial parameters for global fit 
Performing global fit 
Performing local fit 
> 
> 
> # Score the model
> ssm <- scoresinglemod(fsm)
> 
> ssm
Scoring results ($[colnames]):
         discrepancy accuracy similarity plausibility
logistic         162     55.5         69          500
> summary(ssm)
Call:
scoresinglemod.default(fsm = fsm)

Single model scoring setup summary:
     Model.name precision.fit precision.accuracy precision.similarity
[1,] "logistic" "1e-04"       "0.005"            "0.005"             
     Plausibility.penalty
[1,] "500"               
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>