Description Usage Arguments Value Functions Examples
Bayesian nonparametric phylodynamic reconstruction.
1 2 3 4 5 6 7 | BNPR(data, lengthout = 100, pref = FALSE, prec_alpha = 0.01,
prec_beta = 0.01, beta1_prec = 0.001, fns = NULL, log_fns = TRUE,
simplify = TRUE, derivative = FALSE, forward = TRUE)
BNPR_PS(data, lengthout = 100, prec_alpha = 0.01, prec_beta = 0.01,
beta1_prec = 0.001, fns = NULL, log_fns = TRUE, simplify = TRUE,
derivative = FALSE, forward = TRUE)
|
data |
|
lengthout |
numeric specifying number of grid points. |
pref |
logical. Should the preferential sampling model be used? |
prec_alpha, prec_beta |
numerics specifying gamma prior for precision τ. |
beta1_prec |
numeric specifying precision for normal prior on β_1. |
fns |
list containing functions of covariates. |
log_fns |
logical whether or not to to apply a log-transformation to
the output of the functions in |
simplify |
logical whether to fully bucket all Poisson points. |
derivative |
logical whether to calculate estimates of the log-derivative. |
forward |
logical whether to use the finite difference approximations of the log-derivative as a forward or backward derivative. |
Phylodynamic reconstruction of effective population size at grid
points. result
contains the INLA output, data
contains the
information passed to INLA, grid
contains the grid end points,
x
contains the grid point centers, effpop
contains a vector
of the posterior median effective population size estimates,
effpop025
and effpop975
contain the 2.5th and 97.5th
posterior percentiles, summary
contains a data.frame of the
estimates, and derivative
(if derivative = TRUE
) contains a
data.frame summarizing the log-derivative.
BNPR_PS
: Uses preferential sampling model.
1 2 3 4 5 |
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