get_posterior | R Documentation |
get_post_samples()
extracts posterior samples of the specified
parameters from a model-fit object.
get_post_summary()
extracts posterior summary of the specified
parameters from a model-fit object.
get_post_samples(
fit,
parameter = c("z", "pi", "phi", "theta", "psi", "alpha", "beta", "gamma",
"alpha_shared", "beta_shared", "gamma_shared", "Mu", "sigma", "rho"),
output_dataframe = FALSE
)
get_post_summary(
fit,
parameter = c("z", "pi", "phi", "theta", "psi", "alpha", "beta", "gamma",
"alpha_shared", "beta_shared", "gamma_shared", "Mu", "sigma", "rho"),
output_dataframe = FALSE
)
fit |
An |
parameter |
A string of parameter name. See Details for possible choices and corresponding parameters. |
output_dataframe |
If |
The functions return posterior samples or a summary of one of the
following parameters in the model, stored in the model-fit object
fit
:
z
Site occupancy status of species.
pi
Multinomial probabilities of species sequence read counts.
phi
Sequence relative dominance of species.
theta
Sequence capture probabilities of species.
psi
Site occupancy probabilities of species.
alpha
Species-specific effects on sequence relative dominance
(phi
).
beta
Species-specific effects on sequence capture
probabilities (theta
).
gamma
Species-specific effects on site occupancy
probabilities (psi
).
alpha_shared
Effects on sequence relative dominance
(phi
) common across species.
beta_shared
Effects on sequence capture probabilities
(theta
) that are common across species.
gamma_shared
Effects on site occupancy probabilities
(psi
) that are common across species.
Mu
Community-level averages of species-specific effects
(alpha
, beta
, gamma
).
sigma
Standard deviations of species-specific effects
(alpha
, beta
, gamma
).
rho
Correlation coefficients of the species-specific effects
(alpha
, beta
, gamma
).
See the package vignette for details of these parameters.
The parameter may have dimensions corresponding to species, sites,
replicates, and effects (covariates), and when
output_dataframe = FALSE
, the dimension
and label
attributes are added to the output object to inform these dimensions.
If the sequence read count data y
have species, site, or replicate
names appended as the dimnames
attribute (see Details in
occumbData()
), they are copied into the label
attribute of the returned object.
By default, get_post_samples()
returns a vector, matrix, or array of
posterior samples for a selected parameter.
get_post_summary()
returns, by default, a table (matrix) of the
posterior summary of the selected parameters. The elements of the posterior
summary are the same as those obtained with the jags()
function in the jagsUI
package: they include the mean, standard
deviation, percentiles of posterior samples; the Rhat
statistic;
the effective sample size, n.eff
; overlap0
, which checks if 0
falls in the parameter's 95% credible interval; and the proportion of the
posterior with the same sign as the mean, f
.
The dimension
and label
attributes of the output object
provide information regarding the dimensions of the parameter.
When output_dataframe = TRUE
, the results are returned in data
frame format where the attributes obtained when
output_dataframe = FALSE
are incorporated into the table.
# Generate the smallest random dataset (2 species * 2 sites * 2 reps)
I <- 2 # Number of species
J <- 2 # Number of sites
K <- 2 # Number of replicates
y_named <- array(sample.int(I * J * K), dim = c(I, J, K))
dimnames(y_named) <- list(c("species 1", "species 2"),
c("site 1", "site 2"), NULL)
data_named <- occumbData(y = y_named)
# Fitting a null model
fit <- occumb(data = data_named, n.iter = 10100)
# Extract posterior samples
(post_sample_z <- get_post_samples(fit, "z"))
# Look dimensions of the parameter
attributes(post_sample_z)
# Extract posterior summary
(post_summary_z <- get_post_summary(fit, "z"))
# Look dimensions of the parameter
attributes(post_summary_z)
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