`R/predict_curve.R`

`predict_curve.Rd`

Deprecated 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,
...
)
```

- data
A data.frame, tbl_df 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.- formula
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`...`

.- summary
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`...`

.- n
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
```