# CalcWeibullCalibP: Calculating the probabilities of positive binary exposure... In daniel258/CoxBinChange: Cox Model with Interval-Censored Starting Time of a Covariate

## Description

For a given time point, calculate the probability of positive exposure value for multiple observations (participants). The function uses the results of a Weibull calibration model fit, and given collected data on the history of the binary exposure for each participant.

## Usage

 `1` ```CalcWeibullCalibP(w, w.res, point, weib.params) ```

## Arguments

 `w` A matrix of time points when measurements on the binary covariate were obtained. `w.res` A matrix of measurement results of the binary covariate. Each measurement corresponds to the time points in `w` `point` The time point at which the probabilities are estimated. `weib.params` A bivariate vector. Shape and scale parameters of the Weibull calibration model.

## Value

A vector of estimated probabilities of positive exposure status at time `point`.

`Weibull`
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# Simulate data set sim.data <- ICcalib:::SimCoxIntervalCensSingle(n.sample = 200, lambda = 0.1, alpha = 0.25, beta0 = log(0.5), mu = 0.2, n.points = 2, weib.shape = 1, weib.scale = 2) # Fit a Weibull calibration model for the covariate starting time distribution calib.weib.params <- FitCalibWeibull(w = sim.data\$w, w.res = sim.data\$w.res) # Calculate the conditional probabilities of binary covariate=1 at time one probs <- CalcWeibullCalibP(w = sim.data\$w, w.res = sim.data\$w.res, point = 1, weib.params = calib.weib.params) summary(probs) ```