R/predict_curve.R
predict_curve.RdDeprecated function for generating prediction curves (or a density for a prediction curve).
predict_curve(data, formula, summary = median, ...)
predict_curve_density(
data,
formula,
summary = function(...) density_bins(..., n = n),
n = 50,
...
)A data.frame, tibble, or
grouped_df representing posteriors from a Bayesian model as
might be obtained through spread_draws(). Grouped data frames
as returned by group_by() are supported.
A formula specifying the prediction curve. The left-hand side
of the formula should be a name representing the name of the column that
will hold the predicted response in the returned data frame. The right-hand
side is an expression that may include numeric columns from data and
variables passed into this function in ....
The function to apply to summarize each predicted response.
Useful functions (if you just want a curve) might be median(),
mean(), or Mode(). If you want predictive distribution
at each point on the curve, try density_bins() or
histogram_bins().
Variables defining the curve. The right-hand side of
formula is evaluated for every combination of values of variables in
....
For predict_curve_density, the number of bins to use to
represent the distribution at each point on the curve.
If formula is in the form lhs ~ rhs and summary
is a function that returns a single value, such as median or
mode, then predict_curve returns a data.frame with a
column for each group in data (if it was grouped), a column for each
variable in ..., and a column named lhs with the value of
summary(rhs) evaluated for every group in data and combination
of variables in ....
If summary is a function that returns a data.frame, such as
density_bins(), predict_curve has the same set of columns
as above, except that in place of the lhs column is a set of columns
named lhs.x for every column named x returned by
summary. For example, density_bins() returns a data frame
with the columns mid, lower, upper, and density,
so the data frame returned by predict_curve with summary = density_bins will have columns lhs.mid, lhs.lower,
lhs.upper, and lhs.density in place of lhs.
This function is deprecated. Use modelr::data_grid() combined
with point_interval() or dplyr::do() and
density_bins() instead.
The function generates a predictive curve given posterior draws
(data), an expression (formula), and a set of variables
defining the curve (...). For every group in data (if it is a
grouped data frame—see group_by(); otherwise the entire data
frame is taken at once), and for each combination of values in ...,
the right-hand side of formula is evaluated and its results passed to
the summary function. This allows a predictive curve to be generated,
given (e.g.) some samples of coefficients in data and a set of
predictors defining the space of the curve in ....
Given a summary function like median() or mean(),
this function will produce the median (resp. mean) prediction at each point
on the curve.
Given a summary function like density_bins(), this function will
produce a predictive distribution for each point on the curve.
predict_curve_density is a shorthand for such a call, with a
convenient argument for adjusting the number of bins per point on the
curve.
See density_bins().
# Deprecated; see examples for density_bins