# getFitEsts: Get Survival Curve Estimates from icenReg Model In icenReg: Regression Models for Interval Censored Data

## Description

Gets probability or quantile estimates from a `ic_sp`, `ic_par` or `ic_bayes` object. Provided estimates conditional on regression parameters found in `newdata`.

## Usage

 `1` ```getFitEsts(fit, newdata, p, q) ```

## Arguments

 `fit` model fit with `ic_par` or `ic_sp` `newdata` `data.frame` containing covariates `p` Percentiles `q` Quantiles

## Details

For the `ic_sp` and `ic_par`, the MLE estimate is returned. For `ic_bayes`, the MAP estimate is returned. To compute the posterior means, use `sampleSurv`.

If `newdata` is left blank, baseline estimates will be returned (i.e. all covariates = 0). If `p` is provided, will return the estimated F^-1(p | x). If `q` is provided, will return the estimated F(q | x). If neither `p` nor `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 extracted with `getSCurves`.

## Author(s)

Clifford Anderson-Bergman

## Examples

 ``` 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) ```

### Example output

```Loading required package: survival