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Summary of Statistics

All Survival Stats: health_stats

A combination of all the statistics. For static and trend models, if transition probabilities are provided, then one simulation is run and all the statistics are calculated from that simulation (this is to keep results consistent). For frailty model, 'n' number of simulations are performed from which the statistics are calculated.

The function returns all the information (mean and variance of each statistic) as a dataframe.

health_stats(model_type, n_states, init_age, init_state, trans_probs, simulated_path, female, year, wave_index, latent, param_file, n = 1000)

   Parameters:

     model_type : character

       'S' for static model, 'T' for trend model, 'F' for frailty model

     n_states : numeric

       take values 3 or 5, use 3 for 3-state model, and 5 for 5-state model

     init_age : numeric

       numeric denoting initial age of indiviudal

     init_state : numeric

       initial state of individual: 0 for healthy, 1 for disabled

     trans_probs : list

       list of transition probability matrices, only needed for static and trend models.

     simulated_path : matrix

       matrix containing life path simulations, only needed for static and trend models.

     female : numeric

       0 for male, 1 for female, compulsory variable for frailty model

     year : numeric

       numeric denoting current year, compulsory variable for frailty model

     wave_index : numeric

       integer for the wave index = (interview year - 1998)/2 + 1, required in 5-state model and ignored in 3-state model

     latent : numeric

       initial value of latent factor, normally take the value 0

     param_file : character OR dataframe/tibble

       File path, or dataframe/tibble of parameters (generally, use US_HRS or china_CLHLS), compulsory variable for frailty model

     n : numeric

       numeric denoting number of unique latent factor simulations

   Returns:

     Mean and variance of all statistics

   Usage:

# 3 state trend model
trans_probs_3state=get_trans_probs(n_states=3, model_type = 'T', param_file = US_HRS, init_age = 65, female = 0, year = 2022, wave_index = 13, latent = 0)
health_stats(model_type = 'T', n_states=3, init_age=65, init_state=0, trans_probs=trans_probs_3state)
# 3 state frailty model
health_stats(model_type = 'F', n_states=3, init_age=65, init_state=0, trans_probs= NULL , simulated_path = NULL, female = 0, year = 2022, wave_index = 13, latent = 0, param_file = US_HRS)

# 5 state trend model
trans_probs_5state=get_trans_probs(n_states=5, model_type = 'T', param_file = US_HRS_5, init_age = 65, female = 0, year = 2022, wave_index = 13, latent = 0)
health_stats(model_type = 'T', n_states=5, init_age=65, init_state=0, trans_probs=trans_probs_5state)
# 5 state frailty model
health_stats(model_type = 'F', n_states=5, init_age=65, init_state=0, trans_probs= NULL , simulated_path = NULL, female = 0, year = 2022, wave_index = 13, latent = 0, param_file = US_HRS_5)