stan4bartgenerics  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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