pool_intadj: Provides pooled adjusted intercept after shrinkage of pooled...

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

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

Description

pool_intadj Provides pooled adjusted intercept after shrinkage of the pooled coefficients in multiply imputed datasets for models selected with the psfmi_lr function and internally validated with the psfmi_perform function.

Usage

pool_intadj(pobj, shrinkage_factor)

Arguments

pobj	An object of class smodsmi (selected models in multiply imputed datasets), produced by a previous call to psfmi_lr.
shrinkage_factor	A numerical scalar. Shrinkage factor value as a result of internal validation with the psfmi_perform function.

Details

The function provides the pooled adjusted intercept after shrinkage of pooled regression coefficients in multiply imputed datasets. The function is only available for logistic regression models without random effects.

Value

A pool_intadj object from which the following objects can be extracted: int_adj, the adjusted intercept value, coef_shrink_pooled, the pooled regression coefficients after shrinkage, coef_orig_pooled, the (original) pooled regression coefficients before shrinkage and nimp, the number of imputed datasets.

References

F. Harrell. Regression Modeling Strategies. With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (2nd edition). Springer, New York, NY, 2015.

EW. Steyerberg (2019). Clinical Prediction MOdels. A Practical Approach to Development, Validation, and Updating (2nd edition). Springer Nature Switzerland AG.

http://missingdatasolutions.rbind.io/

Examples

 res_psfmi <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", Outcome="Chronic",
           predictors=c("Gender", "Pain","Tampascale","Smoking","Function", 
           "Radiation", "Age"), p.crit = 1, method="D1", direction="BW")
 res_psfmi$RR_Model

## Not run: 
 set.seed(100)
 res_val <- psfmi_perform(res_psfmi, method = "MI_boot", nboot=10, 
   int_val = TRUE, p.crit=1, cal.plot=FALSE, plot.indiv=FALSE)
 res_val$intval

 res <- pool_intadj(res_psfmi, shrinkage_factor = 0.9774058)
 res$int_adj
 res$coef_shrink_pooled
 
## End(Not run) 
  

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