biv.rec.fit {BivRec}  R Documentation 
Deprecated function from the previous version. Use bivrecReg
.
biv.rec.fit(formula, data, method, CI)
formula 
A formula with six variables indicating the bivariate alternating gap time response on the left of the ~ operator and the covariates on the right. The six variables on the left must have the same length and be given as

data 
A data frame that includes all the vectors/covariates listed in the formula above. 
method 
A string indicating which method to use to estimate effects of the covariates. See details. 
CI 
The level to be used for confidence intervals. Must be between 0.50 and 0.99. The default is 0.95. 
Two different estimation methods are available:
method = "Lee.et.al" (default) is a Ustatisticsbased smooth estimating function approach. See Lee, Huang, Xu, Luo (2018) for further details.
method = "Chang" is a rankbased estimating function approach. See Chang (2004) for further details. Note that following the Chang method, the variances of the estimated regression coefficients are approximated using the resampling techniques developed by Parzen, Wei and Ying (1994). This approximation requires extensive computing time for a relatively small sample size. In addition, using the Chang method does not guarantee convergence for the estimation of the coefficients.
See bivrecReg
Chang SH. (2004). Estimating marginal effects in accelerated failure time models for serial sojourn times among repeated events. Lifetime Data Analysis, 10: 175190. doi: 10.1023/B:LIDA.0000030202.20842.c9
Lee CH, Huang CY, Xu G, Luo X. (2018). Semiparametric regression analysis for alternating recurrent event data. Statistics in Medicine, 37: 9961008. doi: 10.1002/sim.7563
Parzen MI, Wei LJ, Ying Z. (1994). A resampling method based on pivotal estimating functions. Biometrika, 81: 341350. http://www.people.fas.harvard.edu/~mparzen/published/parzen1.pdf