stan4bart-generics | R Documentation |
Commonly expected utility functions to derive useful quantities from fitted models.
## S3 method for class 'stan4bartFit'
extract(
object,
type = c("ev", "ppd", "fixef", "indiv.fixef", "ranef", "indiv.ranef",
"indiv.bart", "sigma", "Sigma", "k", "varcount", "stan",
"trees", "callback"),
sample = c("train", "test"),
combine_chains = TRUE,
sample_new_levels = TRUE,
include_warmup = FALSE,
...)
## S3 method for class 'stan4bartFit'
fitted(
object,
type = c("ev", "ppd", "fixef", "indiv.fixef", "ranef", "indiv.ranef",
"indiv.bart", "sigma", "Sigma", "k", "varcount", "stan",
"callback"),
sample = c("train", "test"),
sample_new_levels = TRUE,
...)
## S3 method for class 'stan4bartFit'
predict(
object, newdata, offset,
type = c("ev", "ppd", "indiv.fixef", "indiv.ranef", "indiv.bart"),
combine_chains = TRUE,
sample_new_levels = TRUE,
...)
object |
a fitted model resulting from a call to
|
type |
a character vector; one of the options listed below. |
sample |
one of |
combine_chains |
logical controlling if chain information should be discarded and the result returned as a matrix instead of an array. |
sample_new_levels |
logical; if |
include_warmup |
logical or |
newdata |
data frame for making out of sample predictions. |
offset |
optional vector which will be added to test predictors. |
... |
not currently in use, but provided to match signatures of other generics. |
extract
is used to obtain raw samples using the training or test data,
fitted
averages those samples, and predict
operates on data
not available at the time of fitting. Note: predict
requires that the
model be fit with args_bart = list(keepTrees = TRUE)
.
The type argument accepts:
"ev"
- the individual level expected value, that is draws
from E[Y \mid X^b, X^f, Z] \mid Y = f(X^b) + X^f\beta + Zb
\mid Y
where the expectation is with respect to the posterior
distribution of the parameters given the data
"ppd"
- draws from the individual level posterior predictive
distribution, generally speaking adding noise to the result for
"ev"
or simulating new Bernoulli trials.
"fixef"
- draws from the posterior of the fixed effects
(also known as the “unmodeled” coefficients),
\beta \mid Y
"indiv.fixef"
- draws from the posterior distribution of the
individual level mean component deriving from the fixed effects,
X^f\beta
"ranef"
- the random effects, varying intercepts and slopes,
or “modeled” coefficients, b
; b
has substantial
structure that is represented as the returned value, where coefficients
are reported within their grouping factors
"indiv.ranef"
- individual level mean component deriving
from the random effects, Zb
"indiv.bart"
- individual level mean component deriving
from the BART model, f(X^b)
"sigma"
- for continuous responses, the residual standard
error
"Sigma"
- when applicable, the covariance matrices of the
random effects
"stan"
- raw matrix or array of Stan sampled transformed
parameters.
"trees"
- a data frame of flatted trees; see the subsection
on extracted trees in bart
and note that stan4bart variable
names can be found in the bartData@x
element of a fitted stan4bart
model
"callback"
- if a callback function was provided while
fitting, the results of that for each sample
extract
and predict
return either arrays of dimensions equal to
n.observations x n.samples x n.chains
when combine_chains
is
FALSE
, or matrices of dimensions equal to
n.observations x (n.samples * n.chains)
when combine_chains
is
TRUE
.
fitted
returns a vector of the appropriate length by averaging the
result of a call to extract
.
Vincent Dorie: vdorie@gmail.com.
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