Estimate Phi {cubfits} | R Documentation |
This generic function estimates Phi (expression value) either by posterior
mean (PM) or by maximum likelihood estimator (MLE) depending on options set
by init.function()
.
estimatePhi(fitlist, reu13.list, y.list, n.list, E.Phi = .CF.OP$E.Phi, lower.optim = .CF.OP$lower.optim, upper.optim = .CF.OP$upper.optim, lower.integrate = .CF.OP$lower.integrate, upper.integrate = .CF.OP$upper.integrate, control = list())
fitlist |
an object of format |
reu13.list |
an object of format |
y.list |
an object of format |
n.list |
an object of format |
E.Phi |
potential expected value of Phi. |
lower.optim |
lower bound to |
upper.optim |
upper bound to |
lower.integrate |
lower bound to |
upper.integrate |
upper bound to |
control |
control options to |
estimatePhi()
is a generic function first initialized by
init.function()
, then it estimates Phi accordingly.
By default, .CF.CT$init.Phi
sets the method PM
for the
posterior mean.
PM
uses a flat prior and integrate()
to estimate
Phi. While, MLE
uses optim()
to estimate Phi which
may have boundary solutions for some sequences.
Estimated Phi for every sequence is returned.
Wei-Chen Chen wccsnow@gmail.com.
https://github.com/snoweye/cubfits/
init.function()
and fitMultinom()
.
## Not run: suppressMessages(library(cubfits, quietly = TRUE)) set.seed(1234) # Convert data. reu13.list <- convert.reu13.df.to.list(ex.test$reu13.df) y.list <- convert.y.to.list(ex.test$y) n.list <- convert.n.to.list(ex.test$n) # Get phi.pred.Init init.function(model = "roc") fitlist <- fitMultinom(ex.train$reu13.df, ex.train$phi.Obs, ex.train$y, ex.train$n) phi.pred.Init <- estimatePhi(fitlist, reu13.list, y.list, n.list, E.Phi = median(ex.test$phi.Obs), lower.optim = min(ex.test$phi.Obs) * 0.9, upper.optim = max(ex.test$phi.Obs) * 1.1) ## End(Not run)