infer_mixture()
,
simulate_and_assess_reference()
(maybe we should shorten that to assess_reference_loo()
), assess_bp_bias_correction()
, and we need one that is self_assign()
, and assess_reference_mccv()
These all
should spit out tidy data, to the extent possible. We should try to expose very few other
functions. if(getRversion() >= "2.15.1") utils::globalVariables(c("my_var"))
to
keep CRAN checks from creating notes for variable my_var
used in a dplyr context.
Do this for all variables that create NOTEs ERICeca_funcs.R
.
Use the .Deprecated Just internalize them. BEN
function here.
We want to keep them around for a few iterations for getting results
for the paper, but a lot of the paper
stuff needed to come out of them. BEN#' @keywords internal
to keep the function
documentation out of the help files (if users aren't going to use them directly, there is
no reason to have them.) BEN.self_assign()
.infer_mixture
so that multiple different mixture samples can
be specified in a single data frame input. With really large baselines, the vast majority of the
time in the function is spent processing the data, counting alleles, etc., and it is a shame to have
to do this each time you want to analyze a different mixture sample. I'm not sure how to go about this, but Ben might! BEN looked into it, will start on new versioninfer_mixture
returns a list at the moment. Can that be cleaned up. ERICassess_bp_bias_correction
spits out some nice tidy data at this point. It looks like:# A tibble: 700 × 6
iter repunit true_rho rho_mcmc rho_bh rho_pb
<int> <fctr> <dbl> <dbl> <dbl> <dbl>
1 1 CAN 0.04573827 0.06489907 0.04550360 0.06226238
2 1 NNE 0.06962820 0.07385272 0.05886538 0.06360804
3 1 MB 0.14588315 0.21344097 0.21242781 0.22291767
4 1 NUN 0.46891013 0.14646259 0.35338886 0.24710874
5 1 BIS 0.06644381 0.09336929 0.15767998 0.08005169
6 1 LIS 0.15719031 0.35012560 0.11664560 0.27002139
7 1 MidAtlantic 0.04620614 0.05784975 0.05548877 0.05403009
8 2 CAN 0.02751659 0.02866411 0.02224521 0.02645824
9 2 NNE 0.23190134 0.27633524 0.20446014 0.26219858
10 2 MB 0.16320168 0.07701908 0.17914736 0.11657221
But, we need to
+ [x] remove the rho_bh
calculation and the rho_bh
column in the output.
+ [x] include a true_n
column in the output, which gives the
actual number of individuals sampled into that population on that iteration.
assess_reference
, so Eric still needs to clean that up a bit. Ben after eric pulls together the loo version`assess_reference
. ERICAdd the following code to your website.
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