Gets probability or quantile estimates from a
Provided estimates conditional on regression parameters found in
model fit with
ic_par, the MLE estimate is returned. For
the MAP estimate is returned. To compute the posterior means, use
newdata is left blank, baseline estimates will be returned (i.e. all covariates = 0).
p is provided, will return the estimated F^-1(p | x). If
q is provided,
will return the estimated F(q | x). If neither
q are provided,
the estimated conditional median is returned.
In the case of
ic_sp, the MLE of the baseline survival is not necessarily unique,
as probability mass is assigned to disjoint Turnbull intervals, but the likelihood function is
indifferent to how probability mass is assigned within these intervals. In order to have a well
defined estimate returned, we assume probability is assigned uniformly in these intervals.
In otherwords, we return *a* maximum likelihood estimate, but don't attempt to characterize *all* maximum
likelihood estimates with this function. If that is desired, all the information needed can be
1 2 3 4 5 6 7 8 9 10
simdata <- simIC_weib(n = 500, b1 = .3, b2 = -.3, inspections = 6, inspectLength = 1) fit <- ic_par(Surv(l, u, type = 'interval2') ~ x1 + x2, data = simdata) new_data <- data.frame(x1 = c(1,2), x2 = c(-1,1)) rownames(new_data) <- c('grp1', 'grp2') estQ <- getFitEsts(fit, new_data, p = c(.25, .75)) estP <- getFitEsts(fit, q = 400)
Loading required package: survival Loading required package: Rcpp Loading required package: coda
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