Description Usage Arguments Details Value See Also Examples

View source: R/CalcWeibullRSP.R

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.

1 | ```
CalcWeibullRSP(w, w.res, point, weib.params)
``` |

`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. |

`weib.params` |
A bivariate vector. Shape and scale parameters of the Weibull calibration model. |

At its present form this function is identical to `CalcWeibullCalibP`

. This is because the current version of the `ICcalib`

package
(Version 1.0.005), the user loop over the main event times. Then, at each event time point, the user should include the appropriate Weibull
parameters as estimated by `FitCalibWeibullRS`

.

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 15 16 17 18 19 20 | ```
# 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)
case.times <- sim.data$obs.tm[sim.data$delta==1]
# Fit Weibull risk-set calibration models
calib.weib.params <- FitCalibWeibullRS(w = sim.data$w, w.res = sim.data$w.res,
tm = sim.data$obs.tm,
event = sim.data$delta)
# Calculate the conditional probabilities of binary covariate=1 at time one
probs <- CalcWeibullRSP(w = sim.data$w, w.res = sim.data$w.res, point = 1,
weib.params = calib.weib.params)
summary(probs)
## Not run:
if(interactive()){
#EXAMPLE1
}
## End(Not run)
``` |

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

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