cv_MI

<code>cv_MI</code> Cross-validation by applying multiple single imputation runs in train
 and test folds. Called by function <code>psfmi_perform</code>.

internal

Pooling, backward and forward selection of linear, logistic and Cox regression models in
multiply imputed datasets. Backward and forward selection can be done
from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and
the median p-values method. This is also possible for Mixed models.
The models can contain continuous, dichotomous, categorical and restricted
cubic spline predictors and interaction terms between	all these type of predictors.
The stability of the models	can be evaluated using (cluster) bootstrapping. The package
further contains functions to pool model performance measures as ROC/AUC, Reclassification,
R-squared, scaled Brier score, H&L test and calibration	plots for logistic regression models.
Internal validation can be done across multiply imputed datasets with cross-validation or
bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients
can be obtained. Backward and forward selection as part of internal validation is possible.
A function to externally validate logistic prediction models in multiple imputed
datasets is available and a function to compare models. For Cox models a strata variable
can be included.
Eekhout (2017) <doi:10.1186/s12874-017-0404-7>.
Wiel (2009) <doi:10.1093/biostatistics/kxp011>.
Marshall (2009) <doi:10.1186/1471-2288-9-57>.

Martijn Heymans

psfmi

Prediction Model Pooling, Selection and Performance Evaluation
Across Multiply Imputed Datasets

Iris Eekhout

cv_MI function

<dl><dt>pobj</dt>
<dd>An object of class <code>pmods</code> (pooled models), produced by a previous
call to <code>psfmi_lr</code>.</dd>
<dt>data_orig</dt>
<dd>dataframe of original dataset that contains missing data.</dd>
<dt>folds</dt>
<dd>The number of folds, default is 3.</dd>
<dt>nimp_cv</dt>
<dd>Numerical scalar. Number of (multiple) imputation runs.</dd>
<dt>BW</dt>
<dd>If TRUE backward selection is conducted within cross-validation. Default is FALSE.</dd>
<dt>p.crit</dt>
<dd>A numerical scalar. P-value selection criterium used for backward during
cross-validation. When set at 1, pooling and internal validation is done without
backward selection.</dd>
<dt>miceImp</dt>
<dd>Wrapper function around the <code>mice</code> function.</dd>
<dt>...</dt>
<dd>Arguments as predictorMatrix, seed, maxit, etc that can be adjusted for
the <code>mice</code> function.</dd></dl>

Arguments

Author

Cross-validation in Multiply Imputed datasets — cv_MI

<dl>

<dt>pobj</dt>
<dd>An object of class <code>pmods</code> (pooled models), produced by a previous
call to <code>psfmi_lr</code>.</dd>


<dt>data_orig</dt>
<dd>dataframe of original dataset that contains missing data.</dd>


<dt>folds</dt>
<dd>The number of folds, default is 3.</dd>


<dt>nimp_cv</dt>
<dd>Numerical scalar. Number of (multiple) imputation runs.</dd>


<dt>BW</dt>
<dd>If TRUE backward selection is conducted within cross-validation. Default is FALSE.</dd>


<dt>p.crit</dt>
<dd>A numerical scalar. P-value selection criterium used for backward during
cross-validation. When set at 1, pooling and internal validation is done without
backward selection.</dd>


<dt>miceImp</dt>
<dd>Wrapper function around the <code>mice</code> function.</dd>


<dt>...</dt>
<dd>Arguments as predictorMatrix, seed, maxit, etc that can be adjusted for
the <code>mice</code> function.</dd>

</dl>

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cv_MI: Cross-validation in Multiply Imputed datasets

Description

Usage

Arguments

Author

See Also