Intercept and age are systematically included in the model. -Additional covariates (actually two) can be included with the command:
+Additional covariates can be included with the command:model=list of covariates@@ -368,6 +373,19 @@ Additional covariates (actually two) can the product covariate*age +
In this example, we have two covariates in the data file +(fields 2 and 3). The number of covariates is defined with +statement ncov=2. If now you have 3 covariates in the datafile +(fields 2, 3 and 4), you have to set ncov=3. Then you can run the +programme with a new parametrisation taking into account the +third covariate. For example, model=V1+V3 estimates +a model with the first and third covariates. More complicated +models can be used, but it will takes more time to converge. With +a simple model (no covariates), the programme estimates 8 +parameters. Adding covariates increases the number of parameters +: 12 for model=V1, 16 for model=V1+V1*age +and 20 for model=V1+V2+V3.
+or, to simplify (in most of cases it converges but there is no warranty!):
+or, to simplify (in most of cases it converges but there is no +warranty!):
+12 0.0 0.0 @@ -406,6 +425,45 @@ aij bij 23 0.0 0.0
In order to speed up the convergence you can make a first run with +a large stepm i.e stepm=12 or 24 and then decrease the stepm until +stepm=1 month. If newstepm is the new shorter stepm and stepm can be +expressed as a multiple of newstepm, like newstepm=n stepm, then the +following approximation holds: +
aij(stepm) = aij(n . stepm) - ln(n) +and +
bij(stepm) = bij(n . stepm) .+ +
For example if you already ran for a 6 months interval and
+got:
+
# Parameters +12 -13.390179 0.126133 +13 -7.493460 0.048069 +21 0.575975 -0.041322 +23 -4.748678 0.030626 ++If you now want to get the monthly estimates, you can guess the aij by +substracting ln(6)= 1,7917
12 -15.18193847 0.126133 +13 -9.285219469 0.048069 +21 -1.215784469 -0.041322 +23 -6.540437469 0.030626 ++and get
12 -15.029768 0.124347 +13 -8.472981 0.036599 +21 -1.472527 -0.038394 +23 -6.553602 0.029856 + +which is closer to the results. The approximation is probably useful +only for very small intervals and we don't have enough experience to +know if you will speed up the convergence or not. +-ln(12)= -2.484 + -ln(6/1)=-ln(6)= -1.791 + -ln(3/1)=-ln(3)= -1.0986 +-ln(12/6)=-ln(2)= -0.693 ++Guess values for computing variances
This is an output if mle=1. But it can be @@ -484,15 +542,15 @@ prevalences and health expectancies - +102. It is possible to get extrapolated stationary prevalence by +age ranging from agemin to agemax.
-Setting bage=50 (begin age) and fage=100 (final age), makes the program computing -life expectancy from age 'bage' to age 'fage'. As we use a model, we -can interessingly compute life expectancy on a wider age range than the age -range from the data. But the model can be rather wrong on much larger -intervals. Program is limited to around 120 for upper age!
+Setting bage=50 (begin age) and fage=100 (final age), makes +the program computing life expectancy from age 'bage' to age +'fage'. As we use a model, we can interessingly compute life +expectancy on a wider age range than the age range from the data. +But the model can be rather wrong on much larger intervals. +Program is limited to around 120 for upper age!
pop_based=0-
The program computes status-based health expectancies, i.e health
-expectancies which depends on your initial health state. If you are
-healthy your healthy life expectancy (e11) is higher than if you were
-disabled (e21, with e11 > e21).
-To compute a healthy life expectancy independant of the initial status
-we have to weight e11 and e21 according to the probability to be in
-each state at initial age or, with other word, according to the
-proportion of people in each state.
-
-We prefer computing a 'pure' period healthy life expectancy based only
-on the transtion forces. Then the weights are simply the stationnary
-prevalences or 'implied' prevalences at the initial age.
-
-Some other people would like to use the cross-sectional prevalences
-(the "Sullivan prevalences") observed at the initial age during a
-period of time defined just above.
+
The program computes status-based health expectancies, i.e
+health expectancies which depends on your initial health state.
+If you are healthy your healthy life expectancy (e11) is higher
+than if you were disabled (e21, with e11 > e21).
+To compute a healthy life expectancy independant of the initial
+status we have to weight e11 and e21 according to the probability
+to be in each state at initial age or, with other word, according
+to the proportion of people in each state.
+We prefer computing a 'pure' period healthy life expectancy based
+only on the transtion forces. Then the weights are simply the
+stationnary prevalences or 'implied' prevalences at the initial
+age.
+Some other people would like to use the cross-sectional
+prevalences (the "Sullivan prevalences") observed at
+the initial age during a period of time defined
+just above.
starting-proj-date=1/1/1989 final-proj-date=1/1/1992 mov_average=0-
Prevalence and population projections are only available if the -interpolation unit is a month, i.e. stepm=1 and if there are no -covariate. The programme estimates the prevalence in each state at a -precise date expressed in day/month/year. The programme computes one -forecasted prevalence a year from a starting date (1 january of 1989 -in this example) to a final date (1 january 1992). The statement -mov_average allows to compute smoothed forecasted prevalences with a -five-age moving average centered at the mid-age of the five-age -period.
+Prevalence and population projections are only available if +the interpolation unit is a month, i.e. stepm=1 and if there are +no covariate. The programme estimates the prevalence in each +state at a precise date expressed in day/month/year. The +programme computes one forecasted prevalence a year from a +starting date (1 january of 1989 in this example) to a final date +(1 january 1992). The statement mov_average allows to compute +smoothed forecasted prevalences with a five-age moving average +centered at the mid-age of the five-age period.
Since you know how to run the program, it is time to test it on your own computer. Try for example on a parameter file named mailto:bro href="mailto:lievre@ined.fr">mailto:lievre@ined.fr .
Latest version (0.71a of March 2002) can be accessed at http://euroreves.ined.fr/imach
+href="http://euroreves.ined.fr/imach">http://euroreves.ined.fr/imach