Description Usage Arguments Value Examples
dprior
is C++ function. It matches 5 string types: tnorm
,
beta_lu
, gamma_l
, lnorm_l
, and constant
to
determine which density functions to call (via R API). For truncated normal
density, dprior
calls dtn_scalar
, an internal Rcpp function
built specific for ggdmc. Whetehr log the probability density is
determined by the boolean log
sent in via p.prior
. This
function is akin to DMC's log.prior.dmc
.
1 | dprior(pVec, pPrior)
|
pVec |
the user's supplied parameter vector or a sampler supplied theta/phi vector. |
pPrior |
a list of list usually created by prior.p.dmc to store the prior parameter setting. |
a double vector with probability density for each model parameter
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ## Use Drift-diffusion model as an example
ddm.prior <- prior.p.dmc(
dists = c("tnorm", "tnorm", "beta", "tnorm", "beta", "beta"),
p1 = c(a = 1, v = 0, z = 1, sz = 1, sv = 1, t0 = 1),
p2 = c(a = 1, v = 2, z = 1, sz = 1, sv = 1, t0 = 1),
lower = c(0,-5, NA, NA, 0, NA),
upper = c(2, 5, NA, NA, 2, NA))
view(ddm.prior)
## mean sd lower upper log dist untrans
## a 1 1 0 2 1 tnorm identity
## v 0 2 -5 5 1 tnorm identity
## z 1 1 0 1 1 beta_lu identity
## sz 1 1 -Inf Inf 1 tnorm identity
## sv 1 1 0 2 1 beta_lu identity
## t0 1 1 0 1 1 beta_lu identity
ddm.pVec <- c(a=1.15, v=-0.10, z=0.74, sz=1.23, sv=0.11, t0=0.87)
dprior(ddm.pVec, ddm.prior)
## a v z sz sv t0
##-0.5484734 -1.6008386 0.0000000 -0.9453885 0.0000000 0.0000000
## Use LBA model as an example
lba.prior <- prior.p.dmc(
dists = c("tnorm", "tnorm", "tnorm", "tnorm", "tnorm", "tnorm"),
p1 = c(A=.4, B=.6, mean_v.true=1, mean_v.false=0, sd_v.true=.5, t0=.3),
p2 = c(A=.1, B=.1, mean_v.true=.2, mean_v.false=.2, sd_v.true=.1, t0=.05),
lower = c(0, 0, NA, NA, 0, .1),
upper = c(NA, NA, NA, NA, NA, 1))
view(lba.prior)
## mean sd lower upper log dist untrans
## A 0.4 0.1 0 Inf 1 tnorm identity
## B 0.6 0.1 0 Inf 1 tnorm identity
## mean_v.true 1 0.2 -Inf Inf 1 tnorm identity
## mean_v.false 0 0.2 -Inf Inf 1 tnorm identity
## sd_v.true 0.5 0.1 0 Inf 1 tnorm identity
## t0 0.3 0.05 0.1 1 1 tnorm identity
lba.pVec <- c(A=0.398, B=0.614, mean_v.true=1.040,
mean_v.false=-0.032, sd_v.true=0.485, t0=0.271)
dprior(lba.pVec, lba.prior)
## A B mean_v.true mean_v.false sd_v.true
## 1.3834782 1.3738466 0.6704994 0.6776994 1.3723968
|
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