Description Usage Arguments Value Examples

View source: R/CalcCoxCalibP.R

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

1 | ```
CalcCoxCalibP(w, w.res, point, fit.cox, hz.times, Q)
``` |

`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 |

`point` |
The time point at which the probabilities are estimated |

`fit.cox` |
The result of |

`hz.times` |
Times used for calculating the baseline hazard function from PH calibration model |

`Q` |
Matrix of covariates for the PH calibration model |

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

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
sim.data <- ICcalib:::SimCoxIntervalCensCox(n.sample = 200, lambda = 0.1,
alpha = 0.25, beta0 = 0,
gamma.q = c(log(0.75), log(2.5)),
gamma.z = log(1.5), mu = 0.2,
n.points = 2)
# The baseline hazard for the calibration model is calculated in observation times
cox.hz.times <- sort(unique(sim.data$obs.tm))
# Fit proprtional hazards calibration model
fit.cox <- FitCalibCox(w = sim.data$w, w.res = sim.data$w.res, Q = sim.data$Q,
hz.times = cox.hz.times, n.int = 5, order = 2)
# Calculate the conditional probabilities of binary covariate=1 at time one
probs <- CalcCoxCalibP(w = sim.data$w, w.res = sim.data$w.res, point = 1,
Q = sim.data$Q, fit.cox = fit.cox, hz.times = cox.hz.times)
summary(probs)
``` |

daniel258/CoxBinChange documentation built on Aug. 7, 2018, 4:10 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.