Description Usage Arguments Value Examples
This function computes many iterations of the estimHI
function for compute the variability of the health indicators.
1 2 3 4 |
t |
year of the projections for health indicators. |
intervention |
0 = no change; 1 = reduction by two of risk factor distribution; 2 = risk factor distribution considered as null. |
year_intervention |
year of the intervention in risk factor distribution takes place. |
nb_people |
number of people whose trajectory will be simulated for each generation. |
nb_iter |
number of iterations for the algorithm. |
data_pop |
data source for demographics data. |
gender |
gender for computation. "W" for women and "M" for men. |
data_prev |
data source for the prevalence of the exposition. |
data_incid |
data source for the incidence of the exposition. |
a010 |
incidence of disease on non exposed peoples. |
a011 |
incidence of disease on exposed peoples. |
a01_global |
global incidence of disease. |
a020 |
mortality of healthy subjects on non exposed peoples. |
a021 |
mortality of healthy subjects on exposed peoples. |
a02_global |
global mortality of healthy subjects. |
a120 |
mortality of diseased subjects on non exposed peoples. |
a121 |
mortality of diseased subjects on exposed peoples. |
a12_global |
global mortality of diseased subjects. |
data_theta01 |
data source for the relative risks associated with the exposure for disease. |
data_theta02 |
data source for the relative risks associated with the exposure for mortality among healthy subjects. |
data_theta12 |
data source for the relative risks associated with the exposure for mortality among diseased subjects. |
RR |
relative risks associated with the disease for mortality |
prb_dem |
life-long probability of disease |
age_dem |
average age at disease onset |
Ncpus |
The number of processors available. |
a list containing the variability of health indicators
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | varHI(t = t,
intervention = intervention,
year_intervention = year_intervention,
nb_people = nb_people,
nb_iter = nb_iter,
data_pop = data_pop,
gender = gender,
data_prev = data_prev,
data_incid = data_incid,
a010 = a010,
a011 = a011,
a01_global = a01_global,
a020 = a020,
a021 = a021,
a02_global = a02_global,
a120 = a120,
a121 = a121,
a12_global = a12_global,
data_theta01 = data_theta01,
data_theta02 = data_theta02,
data_theta12 = data_theta12,
RR = RR,
prb_dem = prb_dem,
age_dem = age_dem,
Ncpus = Ncpus)
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