cif-stat-arguments: These arguments are shared by 'cifplot()', 'cifpanel()', and...

In cifmodeling: Visualization and Polytomous Modeling of Survival and Competing Risks

These arguments are shared by cifplot(), cifpanel(), and cifcurve().

Description

These arguments are shared by cifplot(), cifpanel(), and cifcurve().

Arguments

data	A data frame containing variables in the formula.
weights	Optional name of the weight variable in data. Weights must be nonnegative.
n.risk.type	Character string; one of "weighted", "unweighted", or "ess". Controls which risk set size is returned in ⁠$n.risk⁠ without affecting estimates or SEs (default "weighted").
subset.condition	Optional character string giving a logical condition to subset data (default NULL).
na.action	A function specifying the action to take on missing values (default na.omit).
outcome.type	Character string specifying the type of time-to-event outcome. One of "survival" (Kaplan-Meier) or "competing-risk" (Aalen-Johansen). If NULL (default), the function automatically infers the outcome type from the data: if the event variable has more than two unique levels, "competing-risk" is assumed; otherwise, "survival" is used. You can also use abbreviations such as "S" or "C". An explicit "competing-risk" remains in effect when a cause is absent in the analysis data. Mixed or ambiguous inputs (e.g., c("S", "C")) trigger automatic detection based on the event coding.
code.event1	Integer code of the event of interest (default 1).
code.event2	Integer code of the competing risk (default 2).
code.censoring	Integer code of censoring (default 0).
error	Character string specifying the method for SEs and CIs used internally. For "survival" without weights, choose one of "greenwood" (default), "tsiatis", "if", or "fay". For "competing-risk" without weights, choose one of "delta" (default), "aalen", "if", or "fay". Weighted analysis defaults to influence-function SEs ("if"). The C++ curve engine also supports "fay", a Fay–Graubard-type correction. Supplied weights are held fixed; uncertainty from estimating a propensity score is not included.
conf.type	Character specifying the method of transformation for CIs used internally (default ⁠arcsine-square root⁠).
conf.int	Numeric two-sided level of CIs (default 0.95).

cifmodeling documentation built on Nov. 11, 2026, 9:06 a.m.