posterior_epred_ordbeta.brmsfit | R Documentation |
This function is an alternative to the brms
default posterior_epred
to allow for predictions of
the probability of the bottom, top, or middle (i.e. continuous) parts
of the response. Useful when wanting to understand what the effect of a covariate
is on bottom or top values of the scale.
## S3 method for class 'brmsfit'
posterior_epred_ordbeta(
object,
component = "all",
newdata = NULL,
re_formula = NULL,
re.form = NULL,
resp = NULL,
dpar = NULL,
nlpar = NULL,
ndraws = NULL,
draw_ids = NULL,
sort = FALSE,
...
)
object |
An ordbetareg/brms object |
component |
The type of response component, i.e., the probability of the bottom end of the scale, the top end, or the middle (i.e.) continuous values. |
newdata |
see brms::posterior_epred |
re_formula |
see brms::posterior_epred |
re.form |
see brms::posterior_epred |
resp |
see brms::posterior_epred |
dpar |
see brms::posterior_epred |
nlpar |
see brms::posterior_epred |
ndraws |
see brms::posterior_epred |
draw_ids |
see brms::posterior_epred |
sort |
see brms::posterior_epred |
... |
see brms::posterior_epred |
To predict the top, bottom, or "middle" (i.e. continuous) components of the
response, set the component
argument to "top", "bottom" or "continuous". By
default, component
is set to "all", which will replicate behavior of the
default posterior_epred
function.
All other arguments besides component
are the same as the
standard generic posterior_predict
.
For more information on the relevant arguments for posterior_epred
,
see brms::posterior_epred.
An S x N matrix where S is the number of posterior draws and N is the number of observations.
data('ord_fit_mean')
# use function to calculate probability of top end of scale
pr_1s <- posterior_epred_ordbeta(ord_fit_mean,component="top")
# use function to calculate probability of bottom end of scale
pr_0s <- posterior_epred_ordbeta(ord_fit_mean,component="top")
# use function to calculate probability of continuous /
# beta-distributed part of scale
pr_beta <- posterior_epred_ordbeta(ord_fit_mean,component="top")
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