Description Usage Arguments Details Value References Examples
Estimates parameters for the proportional subdistribution hazards model using two-way linear scan approach.
1 2 3 4 |
formula |
a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a Crisk object as returned by the |
data |
a data.frame in which to interpret the variables named in the formula. |
eps |
Numeric: algorithm stops when the relative change in any coefficient is less than |
max.iter |
Numeric: maximum iterations to achieve convergence (default is 1000) |
getBreslowJumps |
Logical: Output jumps in Breslow estimator for the cumulative hazard. |
standardize |
Logical: Standardize design matrix. |
variance |
Logical: Get standard error estimates for parameter estimates via bootstrap. |
var.control |
List of options for variance estimation. |
returnDataFrame |
Logical: Return (ordered) data frame. |
Fits the 'proportional subdistribution hazards' regression model described in Fine and Gray (1999) using a novel two-way linear scan approach.
By default, the Crisk
object will specify which observations are censored (0), the event of interest (1), or competing risks (2).
Returns a list of class fcrr
.
coef |
the estimated regression coefficients |
var |
estimated variance-covariance matrix via bootstrap (if |
logLik |
log-pseudo likelihood at the estimated regression coefficients |
logLik.null |
log-pseudo likelihood when the regression coefficients are 0 |
lrt |
log-pseudo likelihood ratio test statistic for the estimated model vs. the null model. |
iter |
iterations of coordinate descent until convergence |
converged |
logical. |
breslowJump |
Jumps in the Breslow baseline cumulative hazard (used by |
uftime |
vector of unique failure (event) times |
isVariance |
logical to return if variance is chosen to be estimated |
df |
returned ordered data frame if |
Fine J. and Gray R. (1999) A proportional hazards model for the subdistribution of a competing risk. JASA 94:496-509.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | library(fastcmprsk)
set.seed(10)
ftime <- rexp(200)
fstatus <- sample(0:2, 200, replace = TRUE)
cov <- matrix(runif(1000), nrow = 200)
dimnames(cov)[[2]] <- c('x1','x2','x3','x4','x5')
fit <- fastCrr(Crisk(ftime, fstatus) ~ cov, variance = FALSE)
# Not run: How to set up multiple cores for boostrapping
# library(doParallel) # make sure necessary packages are loaded
# myClust <- makeCluster(2)
# registerDoParallel(myClust)
# fit1 <- fastCrr(Crisk(ftime, fstatus) ~ cov, variance = TRUE,
# var.control = varianceControl(B = 100, useMultipleCores = TRUE))
# stopCluster(myClust)
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