compute_max_length

Function which computes the maximum number of regressions with fixed number of variables based on the rule of thumb

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_length function

<dl><dt>vari_col</dt>
<dd>number of predictors</dd>
<dt>k</dt>
<dd>maximum weight of the predictors</dd>
<dt>c</dt>
<dd>array of all indices of the predictors</dd>
<dt>we</dt>
<dd>array of weights of the predictors. Continuous or categorical numerical variable with more then 5 categories has weight 1, otherwise it has weight <code>n-1</code> where <code>n</code> is the number of categories</dd>
<dt>minx</dt>
<dd>minimum number of predictors, 1 by default</dd>
<dt>maxx</dt>
<dd>maximum number of predictors, total number of variables by default</dd>
<dt>st</dt>
<dd>a subset of predictors to be always included into a predictive model</dd></dl>

Arguments

Maximum number of the regressions — compute_max_length

<dl>

<dt>vari_col</dt>
<dd>number of predictors</dd>


<dt>k</dt>
<dd>maximum weight of the predictors</dd>


<dt>c</dt>
<dd>array of all indices of the predictors</dd>


<dt>we</dt>
<dd>array of weights of the predictors. Continuous or categorical numerical variable with more then 5 categories has weight 1, otherwise it has weight <code>n-1</code> where <code>n</code> is the number of categories</dd>


<dt>minx</dt>
<dd>minimum number of predictors, 1 by default</dd>


<dt>maxx</dt>
<dd>maximum number of predictors, total number of variables by default</dd>


<dt>st</dt>
<dd>a subset of predictors to be always included into a predictive model</dd>

</dl>

compute_max_length: Maximum number of the regressions

Description

Usage

Value

Arguments

References

See Also

Examples