markov_two_states: Markov two-state missingness model ('missingness_model')
In gmwmx2: Estimate Functional and Stochastic Parameters of Linear Models with Correlated Residuals and Missing Data
View source: R/time_series_model.R
markov_two_states R Documentation
Markov two-state missingness model (missingness_model)
Description
Constructs a missingness_model representing a two-state Markov process
for missing/observed indicators. The process takes values in {0, 1},
where 1 indicates observed and 0 indicates missing.
Usage
markov_two_states(p1 = NULL, p2 = NULL)
Arguments
p1
Transition probability from observed (1) to missing (0).
p2
Transition probability from missing (0) to observed (1).
Value
A missingness_model object.
Examples
mod <- markov_two_states(p1 = 0.05, p2 = 0.95)
mod
z <- generate(mod, n = 200, seed = 123)
plot(z)
gmwmx2 documentation built on June 10, 2026, 5:06 p.m.
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View source: R/time_series_model.R
| markov_two_states | R Documentation |
Markov two-state missingness model (missingness_model)
Description
Constructs a missingness_model representing a two-state Markov process
for missing/observed indicators. The process takes values in {0, 1},
where 1 indicates observed and 0 indicates missing.
Usage
markov_two_states(p1 = NULL, p2 = NULL)
Arguments
p1 |
Transition probability from observed (1) to missing (0). |
p2 |
Transition probability from missing (0) to observed (1). |
Value
A missingness_model object.
Examples
mod <- markov_two_states(p1 = 0.05, p2 = 0.95)
mod
z <- generate(mod, n = 200, seed = 123)
plot(z)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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