compute_max_weight

Function which computes maximal weight (multiplied by the corresponding EPV rule) of a regression according to the rule of thumb applied to the outcome variable. Weight of a regression equals the sum of weights of its predictors.

Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.

Alina Bazarova

CARRoT

Predicting Categorical and Continuous Outcomes Using One in Ten
Rule

Marko Raseta

compute_max_weight function

<dl><dt>outi</dt>
<dd>set of outcomes</dd>
<dt>mode</dt>
<dd>indicates the mode: 'linear' (linear regression), 'binary' (logistic regression), 'multin' (multinomial regression)</dd></dl>

Arguments

Maximum feasible weight of the predictors — compute_max_weight

<dl>

<dt>outi</dt>
<dd>set of outcomes</dd>


<dt>mode</dt>
<dd>indicates the mode: 'linear' (linear regression), 'binary' (logistic regression), 'multin' (multinomial regression)</dd>

</dl>

compute_max_weight: Maximum feasible weight of the predictors

Description

Usage

Value

Arguments

Details

References

Examples