KernelSmooth_cfg: Configuration for a Kernel Smoother

In tidyhte: Tidy Estimation of Heterogeneous Treatment Effects

Configuration for a Kernel Smoother

Description

KernelSmooth_cfg is a configuration class for non-parametric local-linear regression to construct a smooth representation of the relationship between two variables. This is typically used for displaying a surface of the conditional average treatment effect over a continuous covariate.

Kernel smoothing is handled by the nprobust package.

Super class

tidyhte::Model_cfg -> KernelSmooth_cfg

Public fields

model_class

The class of the model, required for all classes which inherit from Model_cfg.

neval

The number of points at which to evaluate the local regression. More points will provide a smoother line at the cost of somewhat higher computation.

eval_min_quantile

Minimum quantile at which to evaluate the smoother.

Methods

Public methods

• KernelSmooth_cfg$new()

• KernelSmooth_cfg$clone()

---

Method new()

Create a new KernelSmooth_cfg object with specified number of evaluation points.

Usage

KernelSmooth_cfg$new(neval = 100, eval_min_quantile = 0.05)

Arguments

neval

The number of points at which to evaluate the local regression. More points will provide a smoother line at the cost of somewhat higher computation.

eval_min_quantile

Minimum quantile at which to evaluate the smoother. A value of zero will do no clipping. Clipping is performed from both the top and the bottom of the empirical distribution. A value of alpha would evaluate over [alpha, 1 - alpha].

Returns

A new KernelSmooth_cfg object.

Examples

KernelSmooth_cfg$new(neval = 100)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

KernelSmooth_cfg$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

nprobust::lprobust

Examples

## ------------------------------------------------
## Method `KernelSmooth_cfg$new`
## ------------------------------------------------

KernelSmooth_cfg$new(neval = 100)

tidyhte documentation built on Aug. 14, 2023, 5:08 p.m.