Create transition matrix for state transitions

To satisify the positivity assumption of causal inference, we must ensure that the exposure occurs in the data. This function computes a transition matrix for a given state variable across subjects, tracking changes between consecutive observations. The function handles both numeric and factor state variables, excluding NA values in the transition count.

Usage

create_transition_matrix(data, state_var, id_var)

Arguments

data

A data frame containing the observations.

state_var

The name of the state variable column in `data` as a string. This variable tracks the state changes to be analyzed.

id_var

The name of the identifier variable column in `data` as a string. This variable distinguishes between different subjects or entities.

Value

A matrix indicating the number of transitions between states. The rows represent the initial state ('from'), and the columns represent the subsequent state ('to'). Diagonal entries indicate the number of times the state did not change, while off-diagonal entries indicate transitions from one state to another.

Note

This function explicitly excludes NA values from the transition matrix calculation. It treats numeric state variables by converting them to factors, ensuring a consistent analysis approach for both numeric and factor types.

Examples

df <- read.table(header=TRUE, text="
id wave year_measured religion_believe_god
3 0 1 0
3 1 1 1
4 0 1 0
4 1 1 1
5 0 1 1
5 1 1 0")

transition_matrix <- create_transition_matrix(df, "religion_believe_god", "id")
print(transition_matrix)
#>     to
#> from 0 1
#>    0 0 2
#>    1 1 0