Pooling and Predictor selection functions 


Pooling and Predictor selection function for backward or forward selection of Linear regression models across multiply imputed data. 

Pooling and Predictor selection function for backward or forward selection of Logistic regression models across multiply imputed data. 

Pooling and Predictor selection function for backward or forward selection of Cox regression models across multiply imputed data. 

Pooling and Predictor selection function for multilevel models in multiply imputed datasets 

Multiparameter pooling methods called by psfmi_mm 

Functions to evaluate Model performance 

Internal validation and performance of logistic prediction models across Multiply Imputed datasets 

Pooling performance measures across multiply imputed datasets 

Provides pooled adjusted intercept after shrinkage of pooled coefficients in multiply imputed datasets 

External Validation of logistic prediction models in multiply imputed datasets 

Functions to evaluate Model stability 

Function to evaluate bootstrap predictor and model stability in multiply imputed datasets. 

Function to evaluate bootstrap predictor and model stability. 

Functions to compare prediction models 

Compare the fit and performance of prediction models across Multipy Imputed data 

Function to pool NRI measures over Multiply Imputed datasets 

Net Reclassification Index for Cox Regression Models 

Extra Functions 

Function for backward selection of Linear and Logistic regression models. 

Function for forward selection of Linear and Logistic regression models. 

Predictor selection function for backward selection of Cox regression models in single complete dataset. 

Predictor selection function for forward selection of Cox regression models in single complete dataset. 

Calculates the Hosmer and Lemeshow goodness of fit test. 

Calculates the pooled Cstatistic (Area Under the ROC Curve) across Multiply Imputed datasets 

Calculates the scaled Brier score 

Nagelkerke's Rsquare calculation for logistic regression / glm models 

Rsquare calculation for Cox regression models 

Function to combine estimates by using Rubin's Rules 

Combines the Chi Square statistics across Multiply Imputed datasets 

Pools the Likelihood Ratio tests across Multiply Imputed datasets ( method D4) 

Calculation of Net Reclassification Index measures 

KaplanMeier risk estimates for Net Reclassification Index analysis 

KaplanMeier (KM) estimate at specific time point 

Risk calculation at specific time point for Cox model 

Datasets used in examples and tutorials 

Example dataset for psfmi_perform function, method boot_MI 

Data from a placebocontrolled RCT with leukemia patients 

Dataset of patients with a aortadissection 

Data of a nonexperimental study in more than 300 elderly women 

Data about concentration of ß2microglobuline in urine as indicator for possible damage to the kidney 

Long dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS) 

Wide dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS) 

Dataset of low back pain patients with missing values 

Dataset of elderly patients with a hip fracture 

External Dataset of elderly patients with a hip fracture 

Dataset of the Hoorn Study 

Data of a patientcontrol study regarding the relationship between MI and smoking 

Data of the development of lung and heartvolume of unborn babies 

Data of a study among women with breast cancer 

Data of 613 patients with meningitis 

Dataset with blood pressure measurements 

Dataset with blood pressure measurements 

Survival data about smoking 

Dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS) 

Example dataset for the psfmi_mm function 

Example dataset for psfmi_coxr function 

Example dataset for psfmi_lr function 

Example dataset for mivalext_lr function 

Example dataset of Low Back Pain Patients for external validation 

Deprecated functions 

Predictor selection function for backward selection of Linear and Logistic regression models. 

Internal validation and performance of logistic prediction models across Multiply Imputed datasets 