Pooling and Selection of Logistic Regression Models"

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

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Introduction

With the psfmi package you can pool logistic regression models by using
the following pooling methods: RR (Rubin's Rules), D1, D2, D3 and MPR (Median R Rule).

You can also use forward or backward selection from the pooled model.

This vignette show you examples of how to apply these procedures.

Examples

• [Logistic Regression]
  • [Pooling without BS and method D1]
  • [Pooling with BS and method D1]
  • [Pooling with BS and method MPR]
  • [Pooling with BS including several interaction terms and method D2]
  • [Pooling with BS and forcing interaction terms and method D1]
  • [Pooling with BS including spline coefficient and method D1]

Pooling without BS and method D1

  library(psfmi)
  pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", 
                      formula = Chronic ~ Gender + Smoking + 
                      Function + JobControl + JobDemands + SocialSupport, 
                      method="D1")

  pool_lr$RR_model

Back to [Examples]

Pooling with BS and method D1

Pooling Logistic regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method D1 and forcing the predictor "Smoking" in the models during backward selection.

  library(psfmi)
  pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", 
                      formula = Chronic ~ Gender + Smoking + 
                      Function + JobControl + JobDemands + SocialSupport, 
                      keep.predictors = "Smoking", method="D1", p.crit=0.05, 
                      direction="BW")

  pool_lr$RR_model_final
  pool_lr$multiparm_final
  pool_lr$predictors_out

Back to [Examples]

Pooling with BS and method MPR

Pooling Logistic regression models over 5 imputed datasets with backward selection using a p-value of 0.05 and as method D1 and forcing the predictor "Smoking" in the models during backward selection.

  library(psfmi)
  pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", 
                      formula = Chronic ~ Gender + Smoking + 
                      Function + JobControl + JobDemands + SocialSupport, 
                      keep.predictors = "Smoking", method="MPR", p.crit=0.05, 
                      direction="BW")

  pool_lr$RR_model_final
  pool_lr$multiparm_final
  pool_lr$predictors_out  

Back to [Examples]

Pooling with BS including several interaction terms and method D2

Pooling Logistic regression models over 5 imputed datasets with BS using a p-value of 0.05 and as method D2. Several interaction terms, including a categorical predictor, are part of the selection procedure.

  library(psfmi)
  pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", 
                      formula = Chronic ~ Gender + Smoking + 
                        Function + JobControl + factor(Carrying) + 
                        factor(Satisfaction) +
                        factor(Carrying):Smoking + Gender:Smoking, 
                      method="D2", p.crit=0.05, 
                      direction="BW")

  pool_lr$RR_model_final
  pool_lr$multiparm_final
  pool_lr$predictors_out 

Back to [Examples]

Pooling with BS and forcing interaction terms and method D1

Same as above but now forcing several predictors, including interaction terms, in the model during BS.

  library(psfmi)
  pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", 
                      formula = Chronic ~ Gender + Smoking + 
                      Function + JobControl + factor(Carrying) + factor(Satisfaction) +
                        factor(Carrying):Smoking + Gender:Smoking, 
                      keep.predictors = c("Smoking*Carrying", "JobControl"), method="D1", 
                      p.crit=0.05, direction="BW")

  pool_lr$RR_model_final
  pool_lr$multiparm_final
  pool_lr$predictors_out 

Back to [Examples]

Pooling with BS including spline coefficient and method D1

Pooling Logistic regression models over 5 imputed datasets with BS using a p-value of 0.05 and as method D1. A spline predictor and interaction term are part of the selection procedure.

  library(psfmi)
  pool_lr <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", 
                      formula = Chronic ~ Gender + Smoking + 
                      JobControl + factor(Carrying) + factor(Satisfaction) +
                      factor(Carrying):Smoking + rcs(Function, 3), 
                      method="D1", 
                      p.crit=0.05, direction="BW")

  pool_lr$RR_model_final
  pool_lr$multiparm_final
  pool_lr$predictors_out 

Back to [Examples]

psfmi documentation built on July 9, 2023, 7:02 p.m.