# Plot QTE-style effect summaries

`plot.causalmixgpd_qte()` visualizes objects returned by
[`qte`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/qte.md),
[`qtt`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/qtt.md),
and
[`cqte`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/cqte.md).
The `type` parameter controls the plot style. When `type` is omitted,
[`cqte()`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/cqte.md)
objects default to `"effect"` and, when multiple quantile levels are
present, `facet_by = "id"`. Whenever quantile index appears on the
x-axis, it is shown as an ordered categorical axis with equidistant
spacing:

- `"both"` (default): Returns a list with both `trt_control` (treated vs
  control quantile curves) and `treatment_effect` (QTE curve) plots

- `"effect"`: QTE curve vs quantile levels (`probs`) with pointwise CI
  error bars

- `"arms"`: Treated and control quantile curves vs `probs`, with
  pointwise CI error bars

## Usage

``` r
# S3 method for class 'causalmixgpd_qte'
plot(
  x,
  y = NULL,
  type = c("both", "effect", "arms"),
  facet_by = c("tau", "id"),
  plotly = getOption("CausalMixGPD.plotly", FALSE),
  ...
)
```

## Arguments

- x:

  Object of class `causalmixgpd_qte`.

- y:

  Ignored.

- type:

  Character; plot type:

  - `"both"` (default): returns a list with both arm curves and
    treatment-effect plots

  - `"effect"`: QTE curve with pointwise CI error bars

  - `"arms"`: treated and control quantile curves with pointwise CI
    error bars

- facet_by:

  Character; faceting strategy when multiple prediction points exist:

  - `"tau"` (default): facets by quantile level

  - `"id"`: facets by prediction point

- plotly:

  Logical; if `TRUE`, convert the `ggplot2` output to a `plotly` /
  `htmlwidget` representation via `.wrap_plotly()`. Defaults to
  `getOption("CausalMixGPD.plotly", FALSE)`.

- ...:

  Additional arguments passed to ggplot2 functions.

## Value

A list of ggplot objects with elements `trt_control` and
`treatment_effect` (if `type="both"`), or a single ggplot object (if
`type` is `"effect"` or `"arms"`).

## Details

The effect view emphasizes the quantile contrast \\\tau \mapsto
Q\_{Y^1}(\tau) - Q\_{Y^0}(\tau)\\, while the arms view shows the treated
and control quantile functions that generate that contrast. For
conditional CQTE objects, faceting can separate covariate profiles so
the same quantile contrast is compared across prediction settings.

These graphics visualize posterior summaries of the effect object
itself. They are therefore downstream of model fitting and downstream of
the causal prediction step.

## See also

[`qte`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/qte.md),
[`cqte`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/cqte.md),
[`summary.causalmixgpd_qte`](https://arnabaich96.github.io/CausalMixGPD/pkgdown/reference/summary.causalmixgpd_qte.md).

## Examples

``` r
if (FALSE) { # \dontrun{
qte_result <- cqte(fit, probs = c(0.1, 0.5, 0.9), newdata = X_new)
plot(qte_result)  # CQTE default: effect plot (faceted by id when needed)
plot(qte_result, type = "effect")  # single QTE plot
plot(qte_result, type = "arms")    # single arms plot
} # }
```