splitd {maxEff} | R Documentation |
Split-Dichotomized Regression Model
Description
Split-dichotomized regression model.
Usage
splitd(start.model, x_, data, id, ...)
Arguments
start.model |
a regression model |
x_ |
|
data |
|
id |
logical vector, indices of training ( |
... |
additional parameters, currently not in use |
Value
Function splitd()
returns a function,
the dichotomizing rule \mathcal{D}
based on the training set (y_0, x_0)
,
with additional attributes
attr(,'p1')
double scalar,
p_1 = \text{Pr}(\mathcal{D}(x_1)=1)
attr(,'effsize')
double scalar, univariable regression coefficient estimate of
y_1\sim\mathcal{D}(x_1)
Split-Dichotomized Regression Model
Function splitd()
performs a univariable regression model on the test set with a dichotomized predictor, using a dichotomizing rule determined by a recursive partitioning of the training set.
Specifically, given a training-test sample split,
find the dichotomizing rule
\mathcal{D}
of the predictorx_0
given the responsey_0
in the training set (via functionnode1()
);fit a univariable regression model of the response
y_1
with the dichotomized predictor\mathcal{D}(x_1)
in the test set.
Currently the Cox proportional hazards (coxph) regression for Surv response, logistic (glm) regression for logical response and linear (lm) regression for gaussian response are supported.