biv.rec.np {BivRec}  R Documentation 
Deprecated function from the previous version. Use bivrecNP
.
biv.rec.np(formula, data, CI, ai, u1, u2, conditional, given.interval)
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 listed in the formula. 
CI 
The level for confidence intervals the joint cdf, marginal survival and conditional cdf. Must be between 0.50 and 0.99. Default is 0.95. 
ai 
See details. 
u1 
A vector or single number to be used for the estimation of joint cdf P(Type I gap times ≤ u1, Type II gap times ≤ u2) in the nonparametric method. 
u2 
A vector or single number to be used for the estimation of joint cdf P(Type I gap times ≤ u1, Type II gap times ≤ u2) in the nonparametric method. 
conditional 
A logical value. If TRUE, this function will calculate the conditional cdf for the Type II gap time given an interval of the Type I gap time and the bootstrap standard error and confidence interval at the specified confidence level. Default is FALSE. 
given.interval 
A vector c(v1, v2) that must be specified if conditional = TRUE. The vector indicates an interval for the Type I gap time to use for the estimation of the cdf of the Type II gap time given this interval. If given.interval = c(v1, v2), the function calculates P(Type II gap times ≤ y  v1 ≤ Type I gap times ≤ v2). The given values v1 and v2 must be in the range of gap times in the estimated marginal survival. 
ai
indicates a real nonnegative function of censoring times to be used as weights in the nonparametric method. This variable can take on values of 1 or 2 which indicate:
ai=1
: the weights are simply 1 for all subjects, a(Ci) = 1 (default).
ai=2
: the weight for each subject is the subject's censoring time, a(Ci) = Ci.
See bivrecNP
.
Huang CY, Wang MC. (2005). Nonparametric estimation of the bivariate recurrence time distribution. Biometrics, 61: 392402. doi: 10.1111/j.15410420.2005.00328.x