predict {dlmtree} | R Documentation |
predict
Description
predict generic function for S3method
Usage
predict(
x,
new.data,
new.exposure.data,
ci.level = 0.95,
type = "response",
outcome = NULL,
fixed.idx = list(),
est.dlm = FALSE,
verbose = TRUE,
...
)
## S3 method for class 'hdlm'
predict(
x,
new.data,
new.exposure.data,
ci.level = 0.95,
type = "response",
outcome = NULL,
fixed.idx = list(),
est.dlm = FALSE,
verbose = TRUE,
...
)
## S3 method for class 'hdlmm'
predict(
x,
new.data,
new.exposure.data,
ci.level = 0.95,
type = "response",
outcome = NULL,
fixed.idx = list(),
est.dlm = FALSE,
verbose = TRUE,
...
)
Arguments
x |
fitted dlmtree model with class 'hdlm', 'hdlmm' |
new.data |
new data frame which contains the same covariates and modifiers used to fit the model |
new.exposure.data |
new data frame/list which contains the same length of exposure lags used to fit the model |
ci.level |
credible interval level for posterior predictive distribution |
type |
type of prediction: "response" (default) or "waic". "waic" must be specified with 'outcome' parameter |
outcome |
outcome required for WAIC calculation |
fixed.idx |
fixed index |
est.dlm |
flag for estimating dlm effect |
verbose |
TRUE (default) or FALSE: print output |
... |
not used |
Value
list with the following elements:
- ztg
posterior predictive mean of fixed effect
- ztg.lims
lower/upper bound of posterior predictive distribution of fixed effect
- dlmest
estimated exposure effect
- dlmest.lower
lower bound of estimated exposure effect
- dlmest.upper
upper bound of estimated exposure effect
- fhat
posterior predictive mean of exposure effect
- fhat.lims
lower/upper bound of posterior predictive distribution of exposure effect
- y
posterior predictive mean
- y.lims
lower/upper bound of posterior predictive distribution