Version -0.71a, March 2002
+0.8, March 2002This is a comment. Comments start with a '#'.
@@ -311,7 +311,7 @@ INED-EUROREVESftol=1.e-08 stepm=1 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0+
ftol=1.e-08 stepm=1 ncovcol=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0
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
+(fields 2 and 3). The number of covariates included in the data file
+between the id and the date of birth is ncovcol=2 (it was named ncov
+in version prior to 0.8). If you have 3 covariates in the datafile
+(fields 2, 3 and 4), you will set ncovcol=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
@@ -394,7 +399,7 @@ optimization. The number of parameters,
number of absorbing states and non-absorbing states and on the
number of covariates.
N is given by the formula N=(nlstate +
-ndeath-1)*nlstate*ncov .
+ndeath-1)*nlstate*ncovmodel .
Thus in the simple case with 2 covariates (the model is log
(pij/pii) = aij + bij * age where intercept and age are the two
@@ -430,9 +435,40 @@ a large stepm i.e stepm=12 or 24 and the
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(n stepm) = aij(stepm) +ln(n) +aij(stepm) = aij(n . stepm) - ln(n)and -bij(n stepm) = bij(stepm) .+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
and running
+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 @@ -446,20 +482,13 @@ matrix of the parameters, that is the in matrix, and the variances of health expectancies. Each line consists in indices "ij" followed by the initial scales (zero to simplify) associated with aij and bij.
- -
-# Scales (for hessian or gradient estimation) +
- If mle=1 you can enter zeros:
+- -# Scales (for hessian or gradient estimation) 12 0. 0. 13 0. 0. 21 0. 0. 23 0. 0.@@ -469,22 +498,16 @@ consists in indices "ij" follo
- If mle=0 you must enter a covariance matrix (usually obtained from an earlier run).
This is an output if mle=1. But it can be used as an input to get the various output data files (Health expectancies, stationary prevalence etc.) and figures without -rerunning the rather long maximisation phase (mle=0).
- -Each line starts with indices "ijk" followed by the -covariances between aij and bij:
- +rerunning the rather long maximisation phase (mle=0).
+Each line starts with indices "ijk" followed by the +covariances between aij and bij:
121 Var(a12) 122 Cov(b12,a12) Var(b12) ... 232 Cov(b23,a12) Cov(b23,b12) ... Var (b23)-- -
- If mle=1 you can enter zeros.
-- -# Covariance matrix 121 0. 122 0. 0. @@ -494,12 +517,8 @@ covariances between aij and bij: 212 0. 0. 0. 0. 0. 0. 231 0. 0. 0. 0. 0. 0. 0. 232 0. 0. 0. 0. 0. 0. 0. 0.-
- If mle=0 you must enter a covariance matrix (usually - obtained from an earlier run).
+ obtained from an earlier run).
-Age range for calculation of stationary @@ -507,20 +526,19 @@ prevalences and health expectanciesagemin=70 agemax=100 bage=50 fage=100 -
Once we obtained the estimated parameters, the program is able +
+age ranging from agemin to agemax. -
Once we obtained the estimated parameters, the program is able to calculated stationary prevalence, transitions probabilities and life expectancies at any age. Choice of age range is useful for extrapolation. In our data file, ages varies from age 70 to 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 +
- +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!
- agemin= Minimum age for calculation of the stationary prevalence
@@ -537,11 +555,10 @@ color="#FF0000"> the observed prevalencebegin-prev-date=1/1/1984 end-prev-date=1/6/1988-Statements 'begin-prev-date' and 'end-prev-date' allow to +
- +data survey collected between 1 january 1984 and 1 june 1988.
Statements 'begin-prev-date' and 'end-prev-date' allow to select the period in which we calculate the observed prevalences in each state. In this example, the prevalences are calculated on -data survey collected between 1 january 1984 and 1 june 1988.: rbiaspar.imach
- begin-prev-date= Starting date (day/month/year)
@@ -569,7 +586,7 @@ 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. +just above.
- popbased= 0 Health expectancies are @@ -594,7 +611,7 @@ programme computes one forecasted preval 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. +centered at the mid-age of the five-age period.
- starting-proj-date= starting date @@ -618,7 +635,7 @@ including age and number of persons aliv ‘popfiledate’, you can forecast the number of persons in each state until date ‘last-popfiledate’. In this example, the popfile pyram.txt -includes real data which are the Japanese population in 1989. +includes real data which are the Japanese population in 1989.
- Running Imach with this example -
We assume that you entered your 1st_example -parameter file as explained above. To -run the program you should click on the imach.exe icon and enter +We assume that you typed in your 1st_example +parameter file as explained above. +
To run the program you should either: +-
- click on the imach.exe icon and enter the name of the parameter file which is for example C:\usr\imach\mle\biaspar.txt -(you also can click on the biaspar.txt icon located in
-C:\usr\imach\mle and put it with -the mouse on the imach window).
- +href="C:\usr\imach\mle\biaspar.imach">C:\usr\imach\mle\biaspar.imach +- You also can locate the biaspar.imach icon in +C:\usr\imach\mle with your mouse and drag it with +the mouse on the imach window). +
- With latest version (0.7 and higher) if you setup windows in order to +understand ".imach" extension you can right click the +biaspar.imach icon and either edit with notepad the parameter file or +execute it with imach or whatever. +
The time to converge depends on the step unit that you used (1 +The time to converge depends on the step unit that you used (1 month is cpu consuming), on the number of cases, and on the -number of variables.
+number of variables. -The program outputs many files. Most of them are files which -will be plotted for better understanding.
+
The program outputs many files. Most of them are files which +will be plotted for better understanding.
@@ -668,9 +690,9 @@ with a grapher. We use Gnuplot which is program copyrighted but freely distributed. A gnuplot reference manual is available here.
When the running is finished, the user should enter a caracter -for plotting and output editing. +for plotting and output editing. -These caracters are:
+
These caracters are:
- 'c' to start again the program from the beginning.
@@ -708,7 +730,7 @@ people aged 71 is 625+2=627.
- Estimated parameters and -covariance matrix: rbiaspar.txt
+covariance matrixThis file contains all the maximisation results:
@@ -1031,8 +1053,8 @@ are in state 2. One year latter, 512892Since 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 imachpar.txt which is a copy of mypar.txt included in the +href="..\mytry\imachpar.imach">imachpar.imach which is a copy of mypar.imach included in the subdirectory of imach, mytry. Edit it to change the name of the data file to ..\data\mydata.txt if you don't want to @@ -1044,8 +1066,8 @@ question:'Enter the parameter fi
@@ -1063,7 +1085,7 @@ href="imachrun.LOG">this Log file # title=MLE datafile=..\data\mydata.txt lastobs=3000 firstpass=1 lastpass=3 -ftol=1.000000e-008 stepm=24 ncov=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0 +ftol=1.000000e-008 stepm=24 ncovcol=2 nlstate=2 ndeath=1 maxwav=4 mle=1 weight=0
- IMACH, Version 0.71 Enter - the parameter file name: ..\mytry\imachpar.txt
+IMACH, Version 0.8 Enter + the parameter file name: ..\mytry\imachpar.imach
Total number of individuals= 2965, Agemin = 70.00, Agemax= 100.92 @@ -1168,7 +1190,7 @@ edit the master file mypar.htm. < - Observed prevalence in each state: pmypar.txt
- Estimated parameters and the covariance matrix: rmypar.txt
+ href="..\mytry\rmypar.txt">rmypar.imach
- Stationary prevalence in each state: plrmypar.txt
- Transition probabilities: <Graphs + -One-step transition probabilities
- -One-step transition - probabilities
- -Convergence to the - stationary prevalence
- -Observed and stationary - prevalence in state (1) with the confident interval
- -Observed and stationary - prevalence in state (2) with the confident interval
- -Health life - expectancies by age and initial health state (1)
- -Health life - expectancies by age and initial health state (2)
- -Total life expectancy by - age and health expectancies in states (1) and (2).
+ -Convergence to the stationary prevalence
+ -Observed and stationary prevalence in state (1) with the confident interval
+ -Observed and stationary prevalence in state (2) with the confident interval
+ -Health life expectancies by age and initial health state (1)
+ -Health life expectancies by age and initial health state (2)
+ -Total life expectancy by age and health expectancies in states (1) and (2).This software have been partly granted by mailto:brouard@ined.fr and mailto:lievre@ined.fr .
-Latest version (0.71a of March 2002) can be accessed at Latest version (0.8 of March 2002) can be accessed at http://euroreves.ined.fr/imach
