stab_single {psfmi} | R Documentation |
Function to evaluate bootstrap predictor and model stability.
Description
stab_single
Stability analysis of predictors and prediction models selected with
the glm_bw
.
Usage
stab_single(pobj, nboot = 20, p.crit = 0.05, start_model = TRUE)
Arguments
pobj |
An object of class |
nboot |
A numerical scalar. Number of bootstrap samples to evaluate the stability. Default is 20. |
p.crit |
A numerical scalar. Used as P-value selection criterium during bootstrap model selection. |
start_model |
If TRUE the bootstrap evaluation takes place from the start model of object pobj, if FALSE the final model is used for the evaluation. |
Details
The function evaluates predictor selection frequency in bootstrap samples.
It uses as input an object of class smods
as a result of a
previous call to the glm_bw
.
Value
A psfmi_stab
object from which the following objects can be extracted: bootstrap
inclusion (selection) frequency of each predictor bif
, total number each predictor is
included in the bootstrap samples as bif_total
, percentage a predictor is selected
in each bootstrap sample as bif_perc
and number of times a prediction model is selected in
the bootstrap samples as model_stab
.
References
Heymans MW, van Buuren S. et al. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Methodol. 2007;13:7-33.
Sauerbrei W, Schumacher M. A bootstrap resampling procedure for model building: application to the Cox regression model. Stat Med. 1992;11:2093–109.
Royston P, Sauerbrei W (2008) Multivariable model-building – a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. (2008). Chapter 8, Model Stability. Wiley, Chichester.
Heinze G, Wallisch C, Dunkler D. Variable selection - A review and recommendations for the practicing statistician. Biom J. 2018;60(3):431-449.
http://missingdatasolutions.rbind.io/
Examples
model_lr <- glm_bw(formula = Radiation ~ Pain + factor(Satisfaction) +
rcs(Tampascale,3) + Age + Duration + JobControl + JobDemands + SocialSupport,
data=lbpmilr_dev, p.crit = 0.05)
## Not run:
stab_res <- stab_single(model_lr, start_model = TRUE, nboot=20, p.crit=0.05)
stab_res$bif
stab_res$bif_perc
stab_res$model_stab
## End(Not run)