Plot true and estimated curves for P(Y=1|T=t)
Arguments
- theta_true
the coefficients of the data-generating model P(Y=1|T=t)
- theta.hat_joint
the estimated coefficients from the joint model
- theta.hat_midpoint
the estimated coefficients from midpoint imputation
- theta.hat_uniform
the estimated coefficients from uniform imputation
Examples
if (FALSE) {
theta_true = c(0.986, -3.88)
hazard_alpha = 1
hazard_beta = 0.5
sim_data = simulate_interval_censoring(
"theta" = theta_true,
"study_cohort_size" = 4500,
"preconversion_interval_length" = 365,
"hazard_alpha" = hazard_alpha,
"hazard_beta" = hazard_beta)
# extract the participant-level and observation-level simulated data:
sim_participant_data = sim_data$pt_data
sim_obs_data = sim_data$obs_data
rm(sim_data)
# joint model:
EM_algorithm_outputs = fit_joint_model(
obs_level_data = sim_obs_data,
participant_level_data = sim_participant_data,
bin_width = 7,
verbose = FALSE)
# midpoint imputation:
theta_est_midpoint = fit_midpoint_model(
obs_level_data = sim_obs_data,
participant_level_data = sim_participant_data
)
# uniform imputation:
theta_est_uniform = fit_uniform_model(
obs_level_data = sim_obs_data,
participant_level_data = sim_participant_data
)
plot2 = plot_phi_curves(
theta_true = theta_true,
theta.hat_uniform = theta_est_uniform,
theta.hat_midpoint = theta_est_midpoint,
theta.hat_joint = EM_algorithm_outputs$Theta)
print(plot2)
}