mark_first_state: Mark leading-NA cells with an explicit state label

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Mark leading-NA cells with an explicit state label

Description

Mirror of mark_terminal_state() for left-censored sequence data. Replaces every cell before each row's first observed state with the label given by state. The resulting chain has a structurally recurrent "Start" state that everyone enters from - useful for cohort-entry analyses where students join at different time points and you want a uniform pre-observation marker.

Usage

mark_first_state(data, state = "Start", cols = NULL)

Arguments

data	A wide-format matrix or data.frame (rows = actors, cols = time steps) of state labels with NA for missing observations.
state	Character. Label to insert in leading-NA cells. Default "Start".
cols	Optional state-column names; otherwise all columns.

Details

Unlike mark_terminal_state(), the marked state is not absorbing in the resulting transition matrix - every transition from "Start" goes to one of the original states (the actor's first observed state), and the "Start" row is row-stochastic exactly as the data dictates.

Value

A data.frame of the same shape as data with leading NAs filled by state.

See Also

mark_terminal_state(), actor_endpoints()

Examples

M <- mark_first_state(trajectories, state = "Start")
# In a chain built from M, "Start" is a transient entry point.

Nestimate documentation built on July 11, 2026, 1:09 a.m.