These functions override the crr
function provided in cmprsk
to produce more manageable competing risks model results (i.e. include
model.frame and formula in output) so that results can leverage functions
like broom::tidy
the same way other regression function results do.
crr(formula, data, ...)
# S3 method for formula
crr(formula, data, ...)
# S3 method for default
crr(...)
Arguments
formula 
formula object with response on the left of a ~ operator
and terms on the right. Event variable can have multiple levels for use in
competing risks. 
data 
a data.frame in which to interpret the variables named in the
formula 
... 
Arguments passed on to cmprsk::crr
ftime vector of failure/censoring times
fstatus vector with a unique code for each failure type and a separate code for
censored observations
cov1 matrix (nobs x ncovs) of fixed covariates (either cov1, cov2, or both
are required)
cov2 matrix of covariates that will be multiplied by functions of time;
if used, often these covariates would also appear in cov1
to give a prop hazards effect plus a time interaction
tf functions of time. A function that takes a vector of times as
an argument and returns a matrix whose jth column is the value of
the time function corresponding to the jth column of cov2 evaluated
at the input time vector. At time tk , the
model includes the term cov2[,j]*tf(tk)[,j] as a covariate.
cengroup vector with different values for each group with
a distinct censoring distribution (the censoring distribution
is estimated separately within these groups). All data in one group, if
missing.
failcode code of fstatus that denotes the failure type of interest
cencode code of fstatus that denotes censored observations
subset a logical vector specifying a subset of cases to include in the
analysis
na.action a function specifying the action to take for any cases missing any of
ftime, fstatus, cov1, cov2, cengroup, or subset.
gtol iteration stops when a function of the gradient is < gtol
maxiter maximum number of iterations in Newton algorithm (0 computes
scores and var at init , but performs no iterations)
init initial values of regression parameters (default=all 0)
variance If FALSE , then suppresses computation of the variance estimate
and residuals

Examples