genSurvData | R Documentation |
This is a function for generating synthetic datasets from the log-logistic accelerated failure time model. The purpose of this function is to provide structured data for the examples of the other functions' usage.
genSurvData(n, p, s, mag, cens.quant = 0.6)
n |
The numer of subjects to be included in the dataset. |
p |
Dimension of the predictor. Note that the function computes the square-root of a |
s |
The number of nonzero regression coefficients in |
mag |
The magnitude of the |
cens.quant |
The quantile of true survival times used to set the mean of the exponential distribution from which censoring times are drawn. Default is 0.6. |
This function generates predictors to follow a p
-dimensional multivariate normal distribution whose covariance has an AR(1) structure with lag 0.7. Then, log survival times are generated as
\log(T) = X \beta + \epsilon
where \epsilon
has independent components drawn from a logistic distribution with location parmeter zero and scale parameter two. Then censoring times are drawn from an exponential distribution with mean equal to the quantile cens.quant
of T
.
beta |
The true data generating regression coefficient vector. |
logY |
The observed failure times or censoring times on the log scale. |
status |
Indicator of censoring; a value of 1 indicates the corresponding component of logY is an observed log failure time and a value of 0 indicates a log censoring time. |
Xn |
The |
# --------------------------------------
# Generate data
# --------------------------------------
set.seed(1)
genData <- penAFT::genSurvData(n = 50, p = 100, s = 10, mag = 1, cens.quant = 0.6)
X <- genData$X
logY <- genData$logY
delta <- genData$status
str(X)
head(logY)
head(delta)
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