View source: R/predictive_interval.R
predictive_interval.stapreg | R Documentation |
The predictive_interval
function computes Bayesian predictive intervals.
The method for stapreg objects calls posterior_predict
internally, whereas the method for objects of class "ppd"
accepts the
matrix returned by posterior_predict
as input and can be used to avoid
multiple calls to posterior_predict
.
## S3 method for class 'stapreg' predictive_interval(object, prob = 0.9, newsubjdata = NULL, newdistdata = NULL, newtimedata = NULL, draws = NULL, subject_ID = NULL, group_ID = NULL, re.form = NULL, fun = NULL, seed = NULL, offset = NULL, ...) ## S3 method for class 'ppd' predictive_interval(object, prob = 0.9, ...)
object |
Either a fitted model object returned by one of the
rstap modeling functions (a stapreg
object) or, for the |
prob |
A number p (0 < p < 1) indicating the desired
probability mass to include in the intervals. The default is to report
90% intervals ( |
newsubjdata |
Optionally, a data frame of the subject-specific data
in which to look for variables with which to predict.
If omitted, the original datasets are used. If |
newdistdata |
If newsubjdata is provided a data frame of the subject-distance must also be given for models with a spatial component |
newtimedata |
If newsubjdata is provided, a data frame of the subject-time data |
draws, fun, offset, re.form, seed |
Passed to
|
subject_ID |
same as |
group_ID |
same as |
... |
Currently ignored. |
A matrix with two columns and as many rows as are in newsubjdata
.
If newsubjdata
is not provided then the matrix will have as many rows as
the data used to fit the model. For a given value of prob
, p,
the columns correspond to the lower and upper 100p% central interval
limits and have the names 100α/2% and 100(1 -
α/2)%, where α = 1-p. For example, if prob=0.9
is
specified (a 90% interval), then the column names will be
"5%"
and "95%"
, respectively.
predictive_error
, posterior_predict
,
posterior_interval
if (!exists("example_model")) example(example_model) predictive_interval(example_model) newdata <- data.frame(subj_ID = c(1,1), measure_ID = c(1,2), centered_income = c(-1,-.7), sex = c(0,0), centered_age = c(-1,-.7)) # newdata predictive_interval(example_model, newsubjdata = newdata, newdistdata = distdata, newtimedata = timedata, subject_ID = "subj_ID", group_ID = "measure_ID")
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