fastCrr {fastcmprsk} | R Documentation |
Estimates parameters for the proportional subdistribution hazards model using two-way linear scan approach.
fastCrr(formula, data, eps = 1e-06, max.iter = 1000, getBreslowJumps = TRUE, standardize = TRUE, variance = TRUE, var.control = varianceControl(B = 100, useMultipleCores = FALSE), returnDataFrame = FALSE)
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.
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)