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Fig. 1 | BMC Psychiatry

Fig. 1

From: Escape and absconding among offenders with schizophrenia spectrum disorder – an explorative analysis of characteristics

Fig. 1

Overview of statistical procedures. Step 1 – Data Preparation: Multiple categorical variables were converted to binary code. Continuous and ordinal variables were not manipulated. Outcome variable escape or absconding vs. no escape or absconding and 507 predictor variables were defined. Step 2 – Datasplitting: Split into 70% training dataset and 30% validation dataset. Step 3 a, b, c, d, e – Model building and testing on training data I: Imputation by mean/mode; upsampling of outcome “escape/absconding” ×7; variable reduction via random forest; model building via ML algorithms - logistic regression, trees, random forest, gradient boosting, KNN (k-nearest neighbor), support vector machines (SVM), and naive bayes; testing (selection) of best ML algorithm via ROC parameters. Step 4 – Model building and testing on training data II: Nested resampling with imputation, upsampling, variable reduction and model building in inner loop and model testing on outer loop. Step 5–5 Model building and testing on validation data I: Imputation with stored weights from Step 3a. Step6 – Model building and testing on validation data II: Best model identified in Step 3e applied on imputed validation dataset and evaluated via ROC parameters. Step7: Sensitivity analysis and ranking of variables by indicative power

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