Description Usage Arguments Details Value
estimates
and estimate
objects and methods
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## S3 method for class 'estimate'
as.matrix(x, burn = 0, thin = 1,
what = c("params", "probs"))
## S3 method for class 'estimates'
as.matrix(x, burn = 0, thin = 1,
what = c("params", "probs"))
as.mcmc.estimate(x, ..., burn = 0, thin = 1, what = c("params",
"probs"))
as.mcmc.list.estimates(x, ..., burn = 0, thin = 1, what = c("params",
"probs"))
as_tibble.estimate(x, ..., burn = 0, thin = 1, what = c("params",
"probs"), details = T)
as_tibble.estimates(x, ..., burn = 0, thin = 1, what = c("params",
"probs"), details = T)
|
burn |
number or percent of iterations to discard |
thin |
thinning interval. Keep every |
what |
returns either parameter values or posterior probabilities Pr(Z = 1) |
... |
use to select variables a la |
details |
a |
An estimates
object is merely a list
of
estimate
objects. Each estimate
object contains parameter
samples for a given chain. They have a fairly complicated structure that
I describe in more details below. Those objects should never be used
directly, but rather be converted to more familiar data types.
In particular, the as.mcmc
and as.mcmc.list
methods
should be used to perform additional diagnostics using the coda
package.
An estimate
object contains the following fields:
beta
a kBeta x 2 x nSamples
array
with draws of beta coefficients
gamma
a kGamma x nSamples
matrix
with draws of gamma coefficients
pZ
a nMun x nSamples
matrix
with posterior Pr(Z = 1)
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