KernelSmooth_cfg | R Documentation |
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.
tidyhte::Model_cfg
-> KernelSmooth_cfg
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.
new()
Create a new KernelSmooth_cfg
object with specified number of evaluation points.
KernelSmooth_cfg$new(neval = 100, eval_min_quantile = 0.05)
neval
The number of points at which to evaluate the local regression. More points will provide a smoother line at the cost of somewhat higher computation.
eval_min_quantile
Minimum quantile at which to evaluate the smoother. A value of zero will do no clipping. Clipping is performed from both the top and the bottom of the empirical distribution. A value of alpha would evaluate over [alpha, 1 - alpha].
A new KernelSmooth_cfg
object.
KernelSmooth_cfg$new(neval = 100)
clone()
The objects of this class are cloneable with this method.
KernelSmooth_cfg$clone(deep = FALSE)
deep
Whether to make a deep clone.
nprobust::lprobust
## ------------------------------------------------
## Method `KernelSmooth_cfg$new`
## ------------------------------------------------
KernelSmooth_cfg$new(neval = 100)
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