Adjusts error estimates for repeated measures data by use of the cluster bootstrap.
ir_clustBoot(fit, ID, bs_samples = 1000)
Either an ic_par or ic_sp model
Number of bootstrap samples
Standard models in icenReg assume independence between each observation.
This assumption is broken if we can have multiple observations from a single subject,
which can lead to an underestimation of the standard errors.
addresses this by using a cluster bootstrap to fix up the standard errors.
Note that this requires refitting the model
bs_samples, which means this can be
fairly time consuming.
Sherman, Michael, and Saskia le Cessie. "A comparison between bootstrap methods and generalized estimating equations for correlated outcomes in generalized linear models." Communications in Statistics-Simulation and Computation 26.3 (1997): 901-925.
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# Simulating repeated measures data simdata = simIC_cluster(nIDs = 10, nPerID = 4) # Fitting with basic model fit = ic_par(cbind(l,u) ~ x1 + x2, data = simdata) fit # Updating covariance ir_clustBoot(fit, ID = simdata$ID, bs_samples = 10) # (Low number of bootstrap samples used for quick testing by CRAN, # never use this few!!) # Note that the SE's have changed from above fit
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