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Agnes added a direct estimation of mortality (without the need of
computing period prevalence and differential mortality). Thus here
is version 0.97a which has been distributed to some people at
REVES 16 in Brugge using an Inno setup.exe for PCs. Estimates of
mortality using covariates is not done today. Estimating direct
mortality is a very different process because it doesn't need
interpolation because it is easy to get the lx from the force of
the mortality mux in the simplest case as for a Gompertz (log mux
= a + b*x . But we have been able to incorporate the new code
within former imach program (0.96d) without deteriorating too much
the understanding of the program.
Gnuplot is now installed in the same directory (on Windows) as imach.
Thus the full path of gnuplot is executed in order to access the
current version.
MZ @ !L!This program cannot be run in DOS mode.
$ PE L 7@ B 8 8 4 > 0 P @ $ .text 3 4 ` `.data P 8 @ .bss ` .idata $ : @ U($ PB ED$PB E D$PB D$ ED$0 ]Ít& UPB tFPB B uQt@0$PB D$X0 B t@P$PB D$:0 %0 PB ]É' @T$$0 B 뙍v ' U]E1ۉu1 = = sg= t؋u]] $ D$ / t"t$ & ' л$ D$ S/ 㐾 $ D$ 7/ tz$ $ D$
/ t 딉= t= t= U$@ ]{1 I . PB $PB T$ D$艒 j. $B1 U$ tB 1]ÉU$ tB h1]ÉUE$B ]Ív ' UE$B ]ÐU] US4} E$0 EEEEEUEUE *P!NbEUD$
D$ $T$w EU]EUD$@B D$ $T$L ]EUD$@B D$ $T$ C} tE=(`B u n/ (`B ] B )ЉU |B B ]U(E$ / E} u EG D$\ E$. E} u7D$ E $ u E ED$E$. fE E$. E} u E ED$E$M. UE)ЉD$ED$E $ . UE)ЉE E $3. ED$. E$
. EE ED$E$- E$- EE$- @EUE)ЉD$ED$E$- UE)ЉE E EUE E E $- EE E;E~.EUEE EE 8\u EE /E UE EE E E E$ - EE E;E~EE :EuE E ܋EUEEE E E E$, H9Ev+EE :EtEE@ :EuE@EE E$, EE E;E|EUEE E ܋EE E E;E~)E@9E|UE)E PEE E ERREUR ...
%s
UD$@ B @$+ ED$D$@ B @$+ $ c+ allocation failure in vector UUE )$+ E} u $=@ iE E)ЃUE E$* allocation failure in ivector UUE )$* E} u $@ E E)ЃUE E$H* UUE )$:* E} u $@ E E)ЃUE E$) Ðt& allocation failure 1 in matrix() allocation failure 2 in matrix() UWVS UE )@EUE)@EE$]) E} u $@ E E E)E uEE$) E E< u $@ SE ME EE< uE ME )Љ>E@EE;E ~+E MEEPEE ˋE [^_]UE UE
$"( E E$( UWVS UE )@EUE)@EE$' E} u $@ 9E E E)E uEE$' E E< u $@ E ME EE< uE ME )Љ>E@EE;E ~+E MEEPEE ˋE [^_]UE UE
$& E E$& Ðallocation failure 3 in matrix() UWVS UE )@EUE)@EUE )@EE$1& E܃} u $@ E܃ E E)E u܋EE$% E E܃< u $@ 'E M܋E E܋E< u܋E M܋E )Љ>E@EE;E ~+E M܋EE܍PEE ˋE U܋E4
EEE$% 3E M܋E < u $ @ :E U܋E< 4
E M܋E >E U܋E4 <
E U܋E
E )Љ7E@EE;E~EE U܋E 4
E U܋E
PE E 뱋E@EE;E ~ E U܋E 4
EE܍PE EE
E@EE;E~EE U܋E 4
E U܋E
PE E 뱍E QE܃ [^_]USE UE
E $" E UE
$" E E$" []/ UD$lB $@tB " D$ @ $@tB |" ED$$@tB i" @tB UD$lB $@tB " D$ @ $@tB 4" E D$$@tB !" ED$$@tB " @tB UD$lB $@tB M" D$ @ $@tB ! E D$$@tB ! ED$$@tB ! ED$$@tB ! @tB UWVS<E]0tB D$$ _EE E;0tB ~AE4 }ԋE 0vB E @mB E 7E 벋Eԉ$gB ]ء0tB D$D$ Eԉ$ E<[^_]. Too many iterations in brent & ?|=ŋ!r?U E]E]E]E$]ݝ8EEw Eݝ( Eݝ(݅(]EEw Eݝ Eݝ ݅ ]EݕXݕPݝH݅H $E ]EU]E } ~ EE!@ ݝ@݅HM!@ ݕpݝh$!@ B $p D$!@ oB $ oB $N ݅Hܥ@Ee!@ ݅hsE,݅HEݝ0% E]݅8݅pw ݅HܥPEeݝx݅HܥXEe]݅HܥXM݅HܥP܍x]Eܥx]EwEpE`݅8]Eݝ8EE!@ Ms0EܥHMEsEܥHMEsS݅H݅@sEܥHݝEܥHݝ݅ݕ8!@ ] Eu]݅HEݝ`݅`e݅hwEܥ`݅hw ݅pݝ݅@ܥHw݅]N݅H݅@sEܥHݝEܥHݝ݅ݕ8!@ ]E݅ps݅HEݝ <Ew݅p܅Hݝ ݅p݅Hݝ ݅ ݝ`݅` $E ]EEsf݅`݅Hs ݅H] ݅H]݅PݝX݅HݝP݅`ݝHE]E]E] ݅`݅Hw ݅`] ݅`]EEs݅P݅Hzt&݅PݝX݅`ݝPE]E]OEEs0݅X݅Hzt݅X݅Pzt݅`ݝXE]E 4$!@ E,݅HEݝ0݅0Ív w?#B ; Y@USdE $E ЋEE $E ЋEEU w4E ]ЋUE E EE ]ЋUE EEM] E U "x'@ E $E ЋE EU w E U "EU "]E U "EU "]؋E EċE U "]EM]E U "ME]Eew\Ee 8`B '@ H`B 8`B H`B w8`B ]H`B ]Eu]^Ee 8`B '@ H`B 8`B H`B w8`B ]H`B ]Eu]E e]M EU "'@ ]E eEE wE $E ] E eEewrE $E ]ȋE E w U E EEMEU "x'@ ]UE E EE $E ]]EeEE sE]E $E ]*MEU "x'@ ]E $E ]ȋUE U E EEUE UE E E6d[]Ít& -C 6*?UWVSlE0tB ED$$ &0vB ED$$ @mB EgB E E;E~ME
0vB E E E
@mB E E E ]]D$ @ ED$ED$ED$ EЉD$ED$Eȉ$E؉D$$+@ \$ D$ @ E\$E\$E $EE E;E~ZE ] E E M E4 }E ME E 7E 뜋ED$D$ @mB $ED$D$ 0vB $l[^_]Í& %d day(s) %d hour(s) %d minute(s) %d second(s) U(ME. )ЉEME. )iQ )ȉEMų )ЉEMų )i )ȉEM )ЉEM )))ȉEED$ED$ED$ ED$D$,@ E $( E Ív
Powell iter=%d -2*LL=%.12f %ld sec. %ld sec. %d %.12f %d %.12lf %.12lf
Considering the time needed for this last iteration #%d: %ld seconds,
- if your program needs %d iterations to converge, convergence will be
reached in %s or
on %s (current time is %s);
%d powell exceeding maximum iterations. UWVS E]ED$$ EċED$$ zEED$$ dEED$$ NEE$E$ЋE E E;E~'E܍ MċE܍ E E ϋE E ]E ]С`iB diB aB $aB D$nB $`iB "qB `iB )ЉD$ aB `iB )ЉD$E \$E D$$ .@ B $b E E;E~ E E\$ED$$N.@ 7 E E\$ ED$D$X.@ oB $W E E\$D$c.@ PqB $. E i$k.@ D$k.@ oB $ D$k.@ PqB $ PqB $ E 8p $`iB I sB BsB BsB B sB B tB BtB BtB B tB B tB $@oB D$$`jB `iB diB fB fB $`jB EE_jB 8
u E_jB aB `iB )ЉD$E D$$.@ aB `iB )ЉD$ E D$D$.@ oB $ E
} ~[ E E)ы aB `iB )`iB fB $fB
@oB BDoB BHoB B LoB BPoB BToB BXoB B \oB B `oB $@oB D$$qB H $qB \ EEqB 8
u EqB D$@tB `iB fB )Љ$CD$`jB D$ qB D$ED$$.@ m D$@tB `iB fB )Љ$D$`jB D$qB D$ ED$D$.@ oB $p E
E E;E~ E E;E~4E܍< uE܍ M E >E E ]ED$$_/@
B $
ED$D$_/@ oB $
oB $X
E$D$E D$ ED$ED$E$E E EwE E ]ЋEEE E E EE MsmED$D$ E$ ED$D$ E$ED$D$ E$ED$D$ Eĉ$9 E 8 u $/@ E E;E~ E܍ ]E܍ EE܍ E$ E܍4 }E܍ ME܍ E$ 7E܍ MċE܍ E E gE$E$]EEwX E EE]E E eX`B uzX`B
X`B E]
E]EeX`B uzX`B
X`B ME]EE]E]Ew E$D$E D$ ED$ED$E$wE E;E~nE܍ U E< 4
E܍ M E >E܍ U E
E܍ E E 눋E >Č [^_]Ív I@ (@UWVS, E]E$]]08@ ݝ@E PB E 9E~xE PB E 9E~XEЍ U E8
4E;Eu
ݝ(ݝ(݅(84 E 뙍E yݝ`PB 88@ EݝHEܥ@݅Hs E T݅HݝhE E;`B ~{Uȉ$Eȍ aB 4 =0gB E, jB Eȍ aB 7$ݜhE xE E; `B ~?Eȍ aB Eȍ aB ݄h܍hݜhE E E;dB ~ Eȍ dB Eȍ sB 4 =0gB E, jB Eȍ sB 47Eȍ sB 4 =0gB E, jB Eȍ sB 7 3
P$d$ ݜhE E D$ED$ @uB D$XD$ sB $ E D$ PB E D$ D$ PB E D$D$ PB E D$ D$ T$T$ EE E TE ]E E;E ~ ]]E E;E ~a ]E E;PB ~0Eԍ TEE E]E ËEԍ UE̍4 <
Eԍ TE̍ e 7E 8`B Eԍ ME̍ H`B 8`B H`B w8`B ݝ H`B ݝ݅]E 8`B Eԍ ME̍ H`B 8`B H`B w8`B ݝ H`B ݝ݅]E Ee]E 8`B E H`B 8`B H`B w8`B ݝ H`B ݝ ݅ ]E EEw E<!PB 88@ ݅HݝH<, [^_]UWVS<E E;E~[ E E;E| E ]E;E~QEHEM؋UJPB HHEEЍ MEЍ E E]E 륋E܍ ME؍ E E bE@EءPB E9E~ E ]E;E~SEHEM؋UJPB HEEЍ MEЍ E E]E 룋E܍ ME؍ E E [E E E;E~ ]E E;E|4E܍ ME؍ $ E]E E@EءPB E9E~4E܍ ME؍ $ E]E 뽋E܍ UE܍
E E E;E|fE܍ UE؍4 <
E܍ ME؍ $G E܍ ME܍ 7E 됋E@EءPB E9E~fE܍ UE؍4 <
E܍ ME؍ $ E܍ ME܍ 7E 닍E :E@E̡PB E9E~bE PB E9E~EE̍ MEȍ E̍ ME̍ E 묍E 돋E<[^_]UWVS EEE;E~ E EE;E$~ EEE ME E;E ~ E UEE
ME UEE܋
M؋E U E4 <
E M(E 7 E܋UEU E cE 'E
E [^_]Í& (@UWVS E]E PB E 9E~ E PB E 9E~ E܍ U(EE
|E;Eu
ݝpݝp݅pE| E܍ ME؍ lE;Eu
ݝ`ݝ`݅`lE NE +E E;E ~h E E;E~ E,E]EHEEHE$P$d$C@ E]E E;`B ~xẺ\E̍ aB 4 =0gB E0 jB E̍ aB 7\\E {E E; `B ~6E̍ aB E̍ aB DŘM\͘E E E;dB ~ E̍ dB XE̍ sB 4 =0gB E0 jB E̍ sB T47PE̍ sB 4 =0gB E0 jB E̍ sB 7TP
P$d$X\ŘE E D$E D$ @uB D$ED$ sB $D$ PB E D$ D$ PB E D$D$ PB E D$ D$ E(D$E$EȋE(E,EE(E *E PB E 9E~kE PB E 9E~NE܍ UE؍
EЍ< 4E܍ ME؍ >E 룍E 농E E [^_] (@:0yE>UWVS ݝ@E E;PB ~EݜxE =bB E Dž ݝ(E;0uB ~ E E;`B ~@u؋E؍ aB
jB E ݜHE E E pqB H9E~ E PB PB 9E~ E PB PB 9E~ E gB E
E;Eu
ݝݝ݅ E oB E
E;Eu
ݝݝ݅ E 9E E Eԍ nB E E;
| oB piB Eԍ
0oB E nB E
EPB P$d$G@ ݝHE E; `B ~dE̍ aB 4E̍ aB aB
jB E ܍HݜHE 돡PB D$ED$ @uB D$8D$ sB $rD$ PB PB D$ D$ PB PB D$D$ PB PB D$ D$ gB D$piB $4gB oB piB gB E BEԍ
0oB E
`dB E E0oB HE
`dB E Eԍ
oB E PB ݝ;PB ~\ 44 <
oB 7$ $ ݝ l݅ 4
oB ݅ $L ݝ E `uB ݅(ݝ(Eԍ
0oB E
`dB E 4Eԍ
0oB E
`dB E E `uB ܍ ݄xݜxE E Y=bB E Dž ݝ(E;0uB ~ E E;`B ~@u؋E؍ aB
jB E ݜHE E E pqB H9E~. E PB PB 9E~ E PB PB 9E~ E gB E
E;Eu
ݝݝ݅ E oB E
E;Eu
ݝݝ݅ E 9E E Eԍ nB E E;
~ oB piB Eԍ
0oB E nB E
EPB P$d$G@ ݝHE E; `B ~dE̍ aB 4E̍ aB aB
jB E ܍HݜHE 돡PB D$ED$ @uB D$8D$ sB $D$ PB PB D$ D$ PB PB D$D$ PB PB D$ D$ gB D$piB $4gB oB piB gB E BEԍ
0oB E
`dB E E0oB HE
`dB E Eԍ
oB E PB ݝ
oB G@ wn݅ 4
oB ݅ $ ݝ>݅ 4 $r ݝ݅ݝ E `uB ݅(ݝ(Eԍ
0oB E
`dB E 4Eԍ
0oB E
`dB E E `uB ܍ ݄xݜxE E @=bB E Dž ݝ(E;0uB ~ E E;`B ~@u؋E؍ aB
jB E ݜHE E E pqB H9E~S E PB PB 9E~ E PB PB 9E~ E gB E
E;Eu
ݝݝ݅ E oB E
E;Eu
ݝݝ݅ E 9E E Eԍ nB E E;
| oB piB Eԍ
0oB E nB E
EPB P$d$G@ ݝHE E; `B ~dE̍ aB 4E̍ aB aB
jB E ܍HݜHE 돡PB D$ED$ @uB D$8D$ sB $D$ PB PB D$ D$ PB PB D$D$ PB PB D$ D$ gB D$piB $(4gB oB piB gB E BEԍ
0oB E
`dB E E0oB HE
`dB E Eԍ
oB E PB ݝ
oB G@ w ݅ݝ 4 $ ݅ݝ
oB $ ܍݅ݝ>݅ 4 $g ݝ݅ݝ E `uB ݅(ݝ(Eԍ
0oB E
`dB E 4Eԍ
0oB E
`dB E E `uB ܍ ݄xݜxE E =bB E Dž ݝ(E;0uB ~
E E;`B ~@u؋E؍ aB
jB E ݜHE E E pqB H9E~ E PB PB 9E~ E PB PB 9E~ E gB E
E;Eu
ݝݝ݅ E oB E|
xE;Eu
ݝpݝp݅p|x E 9E E Eԍ nB E E;
| oB piB Eԍ
0oB E nB E
EPB P$d$G@ ݝHE E; `B ~dE̍ aB 4E̍ aB aB
jB E ܍HݜHE 돡PB D$ED$ @uB D$8D$ sB $D$ PB PB D$ D$ PB PB D$D$ PB PB D$ D$ gB D$piB $ 4gB oB piB gB E BEԍ
0oB E
`dB E E0oB HE
`dB E ;PB ~_ 44 <
oB 7$ $* ݝ Eԍ
0oB E
`dB E 4 4E0oB HE
`dB E 7 $ ݝ E `uB ݅(ݝ(Eԍ
0oB E
`dB E 4Eԍ
0oB E
`dB E E `uB ܍ ݄xݜxE E PE Dž ݝ(E;0uB ~3 E E;`B ~@u؋E؍ aB
jB E ݜHE E E pqB H9E~ E PB PB 9E~ E PB PB 9E~ E gB El
hE;Eu
ݝ`ݝ`݅`lh E oB E\
XE;Eu
ݝPݝP݅P\X E 9E E Eԍ nB E E;
| oB piB Eԍ
0oB E nB E
EPB P$d$G@ ݝHE E; `B ~dE̍ aB 4E̍ aB aB
jB E ܍HݜHE 돡PB D$ED$ @uB D$8D$ sB $D$ PB PB D$ D$ PB PB D$D$ PB PB D$ D$ gB D$piB $T4gB oB piB gB E BEԍ
0oB E
`dB E E0oB HE
`dB E Eԍ
0oB E
`dB E 4 4E0oB HE
`dB E 7 $) ݝ E `uB ݅(ݝ(Eԍ
0oB E
`dB E 4Eԍ
0oB E
`dB E E `uB ܍ ݄xݜxE 2E E ]E;PB ~EE܄x]E ۅMܵ(]E [^_]Ív %9d %6d %1d %1d %1d %1d %3d %10.6f %6.4f %10.6f %10.6f %10.6f %10.6f %10.6f
(@:0yE>UWVS| ݝ@E E;PB ~EݜxE E lB uB E;0uB ~
E E;`B ~@u؋E؍ aB
jB E ݜHE E E pqB H9E~(
E PB PB 9E~ E PB PB 9E~ E gB E
E;Eu
ݝݝ݅ E oB E
E;Eu
ݝݝ݅ E 9E E Eԍ nB E E;
| oB piB Eԍ
0oB E nB E
EPB P$d$pe@ ݝHE E; `B ~dE̍ aB 4E̍ aB aB
jB E ܍HݜHE 돡PB D$ED$ @uB D$8D$ sB $D$ PB PB D$ D$ PB PB D$D$ PB PB D$ D$ gB D$piB $4Eԍ
0oB E
`dB E E0oB HE
`dB E gB oB piB gB E Eԍ
0oB E
`dB E E0oB HE
`dB E Eԍ
oB E PB ݝ;PB ~h=bB _ 44 <
oB 7$ $Z ݝ( =bB uq݅ 4
oB ݅ $ ݝ( =bB
oB xe@ wn݅ 4
oB ݅ $+ ݝ>݅ 4 $ ݝ݅ݝ( =bB
oB xe@ w ݅ݝ 4 $O ݅ݝ
oB $ ܍݅ݝ>݅ 4 $ ݝ݅ݝ(s=bB u6 4 $z ݝ(4 4 $D ݝ(lB E `uB uB uB Eԍ
0oB E
`dB E 4Eԍ
0oB E
`dB E E `uB ܍(݄xݜx=puB ݅( $[
oB \$D 4 \$<E `uB ܍(\$4E `uB \$,\$$Eԍ
nB E D$ Eԍ
0oB E D$ EԉD$D$ D$ED$ E dB D$D$ e@ hB $ E ݝ ]E;PB ~bE oB ܌xdB ݅ ݝ E݄x oB dB \$D$_e@ hB $^ E ݅ \$D$ge@ hB $6 E E FE ]E;PB ~EE܄x]E lB M5 uB ]=puB u:lB oB uB ٽf
f٭ dB ٭E| [^_]ilk w Problem with resultfile: %s
#individual(line's_record) s1 s2 wave# effective_wave# number_of_matrices_product pij weight -2ln(pij)*weight 0pij_x 0pij_(x-stepm) cumulating_loglikeli_by_health_state(reweighted=-2ll*weightXnumber_of_contribs/sum_of_weights) and_total
#num_i i s1 s2 mi mw dh likeli weight 2wlli out sav -2*gipw/gsw*weight*ll[%d]++ & ' -2*gipw/gsw*weight*ll(total)
<br>File of contributions to the likelihood: <a href="%s">%s</a><br>
USE8 D$#q@ $ rB D$PsB $ rB D$'q@ $ rB hB =hB u1D$ rB $)q@ D$ rB D$)q@ oB $ D$`q@ hB $ D$`r@ hB $ E E;E~#ED$D$r@ hB $ E D$r@ hB $ E $E(ЋE$E8 tQhB $ $ rB m$ rB _\$ D$D$r@ gB $, gB $ []Powell
pow # Powell
# iter -2*LL p%1d%1d
#Number of iterations = %d, -2 Log likelihood = %.12f
&
#Number of iterations = %d, -2 Log likelihood = %.12f
& #Number of iterations = %d, -2 Log likelihood = %.12f
UXE ]ED$ D$ ED$$ 认EE E;E~[E E;E~CE UEEԋ
MЋE;Eu]]EȋEԋU E 볍E $t@ D$t@ oB $Z D$t@ $ hB f D$PsB $ hB D$'q@ $ hB n PqB =PqB u1D$ hB $)q@ D$ hB D$)q@ oB $ D$t@ PqB $ E E;E~OE PB E9E~2E;Et#ED$ ED$D$t@ PqB $x E 뿍E D$k.@ PqB $U E$D$ ED$ED$E\$ ED$ED$E $PqB $S E $E$\$ED$$t@ E $E$\$ ED$D$ u@ oB $ E $E$\$ ED$D$`u@ E$ Ít& '
Calculation of the hessian matrix. Wait...
.%d%d '
Inverting the hessian to get the covariance matrix. Wait...
#Hessian matrix#
%.3e UWVS\E]ED$ D$ ED$$ 茡E$ x@ i D$ x@ oB $ E E;E~ EȉD$$_/@ - B $
EȉD$D$_/@ oB $Q oB $ ED$E D$ED$EȉD$ E\$E $ Eȍ M̋Eȍ E RE E;E~ E E;E~ E;E EĉD$EȉD$$Mx@ Q B $1 EĉD$ EȉD$D$Mx@ oB $n oB $ ED$E D$EĉD$ EȉD$ED$E $ Eȍ M̋Eč Eč ŰEȍ< 4
Eȍ M̋Eč >E E $k.@ l D$k.@ oB $ $`x@ K D$`x@ oB $ ED$ D$ ED$$ (EED$ D$ ED$$ EED$$ ӛE܋ED$$ <EE E;E~YE E;E~AEȍ UEč< 4
Eȍ M̋Eč >E 뵍E 띍EЉD$ ED$ED$E$ E E;E~ E E;E~Eȍ E E Eč E E܉D$ ED$ED$E$ E E;E~4Eȍ UEč
Eȍ E E E H$x@ k D$x@ oB $ E E;E~ E E;E~jEȍ M̋Eč \$$x@ Eȍ M̋Eč \$D$x@ oB $ E $k.@ D$k.@ oB $ E ME E;E~YE E;E~AEȍ UEč< 4
Eȍ MEč >E 뵍E 띍EЉD$ ED$ED$E$ ED$D$ ED$D$ E$3ED$D$ ED$D$ E$
ED$D$ E܉$ED$D$ E$JED$D$ ED$D$ Ẻ$譜\[^_]Ív -C 6? $@ @U E ]E E @ ݝ@ ݝݝ@ ݝDž| Džx
E$E ݝE E;E ~!ME EݜE E E;E~ E\$@ $7 ݝp݅ݝDž| |;x| ۅ|܍pEݝME E܅ݜ$E ܥ]؋ME Eܥݜ$E ܥ]EEܵܵ@ ݝ݅ܵ@ Ew"݅ܵ@ Ewx| ݅ܵEw݅ܵEw@x|E@ ٽnnf
fl٭l]٭n:݅ܵEw݅ܵEw ݅ݝ| @E E E݅ ݅Ív @ @UVSp E E E$E]Dž ~~ E E;E ~!ME EݜͨE ՋuE ]E E ۅ ݜuE ]E E ۅ ݜ$Ee]uE ]E E ۅ ݜuE ]E E ۅ ݜ$Ee]؋uE ]E E ۅ ݜuE ]E E ۅ ݜ$Ee]ЋuE ]E E ۅ ݜuE ]E E ۅ ݜ$Ee]EeeE@ E E 4ۅɋE E 4ۅ@ ] tEp [^]ÐSingular matrix in routine ludcmp #B ;UWVSLE D$$ .EEE E;E ~ ]E E;E ~@E ME ]EEwE]؍E Ezt $@ 6E Eu E `E E;E ~c E E;E| E ME ]E E;E|IE UE4 <
E ME 7 E]ȍE 뭋E ME E E O]؋EEE;E ~ E ME ]E E;E|IE UE4 <
E ME 7 E]ȍE 뭋E ME E E EEUEs E]؋EEE E;E E E;E ~ E ME ]ЋE UE< 4
E ME >E ME E E oUE E ME E E UE
E ME zt#E ME @ E;E twE ME 4]ЋE@EE;E ~DE UE4 <
E ME M 7E 벍E E D$D$ E$1L[^_]UWVS E E E;E ~ E EEE E]؋E ME E } tMEEEH9E~TE UE
E E E]؍E EuzEEE EE E %E E} E E]؋E@EE;E ~<E UE
E E E]؍E 뺋E4 }E ME E4 7E[ [^_]p v Problem with prevalence resultfile: %s
#********** Variable V%d=%d **********
# Age Prev(%d) N(%d) N Total See log file for details...
Age %d %d.=%.0f loss[%d]=%.1f%% %d.=%.0f loss[%d]=NaNQ%% %d.=%.0f prev[%d]=%.1f%% %d.=%.0f prev[%d]=NaNQ%% %d %.5f %.0f %.0f %d NaNq %.0f %.0f %d%d=%.0f Others in log...
(@|= Y@h㈵>UWVSl ]]E D$$ 豊EED$ E D$E D$$ 褍ED$@ $ ED$$r D$'q@ $ E} uAD$$ @ - D$D$ @ oB $^ $ B ED$E D$PB E D$ D$PB E D$$膎EE `B E=`B E E, E E E;E~o E E؍ U,E;
~D E EPB E 9E~jEPB E 9E~ME EE9E~3E UE܍
E E E 뤍E E E;E ~@E EE9E~&E ME E ͍E ]]E E;E ~ E =`B E E;`B ~ Eȍ E$ jB E
Eȍ E$4 }(EЍ
jB Eȍ 7uzE E L} `B EE;PB ~ E U4E4 <
E M0E `@ 7]E ME zt)E UE E@P$d$
E ME zt+E UE EP$d$
E ME < 6 E ME ;E E ME 4 }E ME ٽf
f٭٭47E ME 4 }E ME ٭٭ 7E `uB E;PB E ME uEEHE < 4 E ME ٽf
f٭٭<>E ME uEEHE < 4 E ME ٭٭ >E `uB E ME uEEHE E
xE ME uEEHE E
HE `uB E ME wIE UE EP$d$
wEE]E]E E =`B D$H@ E$裭 E E;`B ~|Eȍ E$4 }(EЍ
jB Eȍ 7D$ Eȍ E$D$D$_@ E$ E wD$g@ E$ E E;E ~(ED$ ED$D$t@ E$ά E D$k.@ E$贬 E EE9E~- E9EuD$@ oB $~ 5}uE $@ ED$D$@ oB $G E E;E ~ EE܍ E PB E 9E~eE܍4 }EUE܍ UE
E 7E 댍E \E E;E ~ E]} ~7E܍ UE
E E]E E܍ Eh@ s }uMEp@ ɋE܍ E4\$E܉D$E܍ E\$E܉D$$@ v Ep@ ɋE܍ E4\$E܉D$E܍ E\$ E܉D$D$@ oB $p k}u.E܉D$E܍ E\$E܉D$$@ E܉D$E܍ E\$ E܉D$D$@ oB $ E dE E;E ~ E E܍ E PB E 9E~eE܍4 }EUE܍ UE
E 7E 댍E \E ]]E;E ~@E܍ EE]E܍ ME E]E E E;E ~ Ex@ s }uME܍ Ep@ u\$E܉D$E܍ E\$E܉D$$@ % E܍ Ep@ u\$E܉D$E܍ E\$ E܉D$D$@ oB $ k}u.E܉D$E܍ E\$E܉D$$@ 虧 E܉D$E܍ E\$ E܉D$D$@ oB $貧 E;E Ex@ shE\$ E܍ ME \$E܍ ME u\$ ED$D$ @ E$, BE\$E܍ ME \$ ED$D$/@ E$ E EPB E 9E~ EPB E 9E~ E܍ UE
E uz }uHE܍ UE
E \$ ED$E܉D$$B@ ҥ E܍ UE
E \$ED$ E܉D$D$B@ oB $ѥ E E E;ED$k.@ E$袥 }u $M@ @ D$k.@ oB ${ E E E Eu `B E$膤 ED$E D$PB E D$D$ PB E D$D$E$要E D$D$ E$yED$E D$ E D$D$ E$}l [^_]ÉError on individual =%d agev[m][i]=%f m=%d
t& (@h㈵>UWVS E ]E]E@]EH]Eٽvvf
ft٭t|٭vE٭tx٭vxD$ |D$E$D$$ {EE `B E=`B E E4 E E;E~_ E E U4E;
~4 E E E;E$~F|Eȋx9E~&E̍ MEȍ E ʍE E E;E(~ E =`B E E;`B ~ E aB jB El
hE aB 4 }0E
jB E 7lhuzE E H} EPEȋE;ET~ Eȍ U<E̍4 <
Eȍ M8E̍ 0@ 7]EEst EEsb Eȍ M E̍ zt,Eȍ U E̍ x@P$d$
Eȍ M E̍ zt.Eȍ U E̍ xP$d$
Eȍ M E̍ ٽvvf
ft٭tp٭vp;||DEȍ M E̍ ٭tp٭vpx9;EȉD$Eȍ M E̍ \$ẺD$$ @ ڟ Eȍ M E̍ < Eȍ M E̍ ;E$ Eȍ M E̍ 4 }Eȍ M E̍ ٽvvf
ft٭tp٭vpd47`Eȍ M E̍ 4 }Eȍ M E̍ ٭tp٭vp 7E̍ `uB d` Eȍ M E̍ Ux
pEȍ M E̍ Ux
HE̍ `uB E E |E̋x9E~ E ]E;E$~*Eč ME̍ E]E E E;E$~pE;x^E8@ sJE̍ UEč
E4 <Eč ME̍ u 7E 농E "E E xD$|D$ E$D$D$ E$vĬ [^_]ÐWarning! None valid information for:%ld line=%d (skipped) and may be others, see log file
t& Warning! None valid information for:%ld line=%d (skipped)
t& Error! Negative delay (%d to death) between waves %d and %d of individual %ld at line %d who is aged %.1f with statuses from %d to %d
We assumed that the date of interview was correct (and not the date of death) and postponed the death %d month(s) (one stepm) after the interview.
You MUST fix the contradiction between dates.
Error! Negative delay (%d) between waves %d and %d of individual %ld at line %d who is aged %.1f with statuses from %d to %d
Delay (in months) between two waves Min=%d Max=%d Mean=%f
v @p@ (@ ?UStE ]E PdB uB dB E E;E,~e E E$EE ME ;E0~VE ME < ~%E E UE E
E;E(|E 놋E ME ;E0~%E E UE E
E UE
} ut`B } u0ED$E dB D$$@ B E }u2ED$ E dB D$D$ @ oB $S E E E;E,~ E E UE;
| }4 &E M E EEHE ME ;E0} E E @ w E E @ ɋE ME M E @ @ $O }Ef
fEm]mƃ} u E } `B EEHE ME D$$E ME ME D$ E ME M E \$ED$E dB D$EEHE D$ E ME D$ED$$`@ v E E4D$$ @ \ EEHE ME D$(E ME ME D$$E ME M E \$ ED$E dB D$EEHE D$E ME D$ ED$D$`@ oB $脔 E4D$D$ @ oB $h E E; uB |E uB E;PdB EPdB EE]5 EEHE M E @ ɋE ME M E @ @ $ϒ }Ef
fEm]mƍE E; uB |
E uB E;PdB EPdB } M `B EEHE ME D$$E ME ME D$ E ME M E \$ED$E dB D$EEHE D$ E ME D$ED$$@ ۑ EEHE ME D$(E ME ME D$$E ME M E \$ ED$E dB D$EEHE D$E ME D$ ED$D$@ oB $ EE]ȋUM4MЋM9EEE܋E܉U4E)ЉEԋE@U4E)ЉE=bB } uFE U E E܉
E ME 4 E U E E@
E UE E؉
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E UE Eԉ
AE U E E@
E UE E؉
E M E < uAE M E E UE E؉
E E EE dB dB \$ uB D$PdB D$$`@ 蔎 dB \$ uB D$ PdB D$D$`@ oB $賎 t[]UStE E E `B E E;E|ED E E }~ E `pB E E dB `B 9E~P E E;E~aE E
jB E }Ef
fEm]mEDE;E~EEE E E;E~#E| tE `pB E E E E `pB E;
~xE E;E~]E| t2E E U E E
E E `pB E;
~E 뙍E nE E E;E|ED E E @uB 9E~!E EEEDE E E E;E~7E| t&E; lB E aB E
E E 뿋EH`B t[]# Health expectancies
# Age %1d-%1d (SE) Problem %d lower than %d
%d| %3.0f %9.4f (%.4f) t& @`@ (@ ? @UWVS E D$E D$EED$ D$ EED$$ dEPB D$$ _EPB D$ D$ EED$$ bEEED$ D$ EED$$ bED$@ oB $Ċ D$@ oB $诊 E E;E~BE E;E~*ED$ ED$D$@ oB $j E ̍E D$k.@ oB $G E0;E} ED$E0D$$ɶ@ Չ E0EUE$Ћ$9$$E @ ]E ]E Es
Ee@ E@ $ }Ef
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hE UE
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HE UE
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E7 @ LH E ]E E E EE9E~ E EH9E~ E܍ UE
ED @E܍ UE4 <
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E4 <E܍ UE
E 7E 똍E yE ZE }Ef
fEEE9E~[E EE9E~?E UE
Em]mE E 볍E Em]mED$$@ 謁 B $茁 E}Ef
fEm]mED$D$@ oB $赁 oB $H E EH9E~ E EH9E~ E8D$ PB D$ D$ PB D$D$ EED$ D$ E܍ ED$E$lEԍ ED$ EED$ D$ PB D$D$ EED$ D$ ED$E$E EE9E~ E EE9E~ E UE
E}Ef
fEm]mE< 8E UE
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E}Ef
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E}Ef
fEm]mE $| \$E U E
E}Ef
fEm]mE \$D$@ oB $&} E 5E D$k.@ oB $| EED$D$ ED$D$ E$UEED$D$ ED$D$ E$UEED$D$ PB D$D$ ED$D$ E$ YPB D$D$ EED$D$ ED$D$ E$XED$D$ PB ED$D$ PB ED$D$ E$XE]$k.@ z{ D$k.@ oB ${ PB D$D$ E$PPB D$D$ EED$D$ E$dTEED$D$ EED$D$ E$3TE D$E D$EED$D$ EED$D$ E$W [^_]-populbased-mobilav- -populbased-nomobil- -stablbased- & Error in movingaverage mobilav=%d
prmorprev %-d & ' Computing total mortality p.j=w1*p1j+w2*p2j+..: result on file '%s'
# probabilities of dying before estepm=%d months for people of exact age and weighted probabilities w1*p1j+w2*p2j+... stand dev in()
# Age cov=%-d p.%-d SE w%1d p%-d%-d
# Routine varevsij
<li><h4> Computing probabilities of dying over estepm months as a weighted average (i.e global mortality independent of initial healh state)</h4></li>
<br>%s <br>
# Variance and covariance of health expectancies e.j
# (weighted average of eij where weights are the stable prevalence in health states i
Cov(e%1d, e%1d) %3d %d %11.3e %11.3e %11.3e %11.3e %.0f %.4f & '
set noparametric;set nolabel; set ter png small;set size 0.65, 0.65
set log y; set nolog x;set xlabel "Age"; set ylabel "Force of mortality (year-1)"; t& '
plot "%s" u 1:($3) not w l 1
replot "%s" u 1:(($3+1.96*$4)) t "95%% interval" w l 2 t&
replot "%s" u 1:(($3-1.96*$4)) not w l 2
<br> File (multiple files are possible if covariates are present): <A href="%s">%s</a>
varmuptjgr
<br> Probability is computed over estepm=%d months. <br> <img src="%s%s.png"> <br>
set out "%s%s.png";replot;
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f٭]٭UMЋ9EED$D$ PB E D$ D$ PB E D$$ HtE D$D$ PB D$ D$ ED$$ GEE D$ D$ ED$$ FEE D$ D$ ED$$ EEDžP P;PB ~S
E E;PB ~ EUE EݝE;Pu!P E݅ݝx݅ݝx݅x E jEHD$(E8D$$E4D$ E D$ E D$ED$ED$݅h\$ED$t$mEHD$$E\$ E8D$E4D$݅h\$ ED$E D$E<$ob}X@ }\ E E;E ~" E U<E4 <
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f٭٭ ppB E
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EH 7E lE E;E ~ E E;E~ E E ME E;E ~ E UEt
pE UEl
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E tE X\d` E aE ,E E;PB ~ ETEPE EݝHE;Pu!P E݅H$ݝ@݅Hݝ@݅@TP E jEHD$(E8D$$E4D$ E D$ E D$ED$ED$݅h\$ED$t$iEHD$$E\$ E8D$E4D$݅h\$ ED$E D$E<$]}X@ }\ E E;E ~" E U<E4 <
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EH 7E lE E;E ~ E E;E~ E E ME E;E ~ E UE<
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P4 <E UP
E 7P 댍E kE ME @EPB E 9E~kDžP P;PB ~JE UP< 4
P ME >P 릍E 놋EE P$d$@ ݝXE E;E ~ E E;E ~fE U E
݅hٽf
f٭٭ E 됍E rE E;E~ E E;E~ ED$ PB D$ D$ PB D$D$ E D$ D$ E ED$Ẻ$_`E ED$ E D$ D$ PB D$D$ E D$ D$ ẺD$Eȉ$
`E E;E ~ E E;E ~ E U E
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f٭٭ E U E
݅h٭٭4 <E MȋE ܍X܍X7 E E E E ED$ PB D$ D$ PB D$D$ PB E D$ E @D$ED$Eĉ$^ED$ PB E D$ E @D$PB D$D$ PB E D$ E @D$EĉD$E$8^E @EPB E 9E~aE @EPB E 9E~DE xE< 4
E ME >E 뭍E 됋EHD$(E8D$$E4D$ E D$ E D$ED$ED$݅h\$ED$t$^EHD$$E\$ E8D$E4D$݅h\$ ED$E D$E<$yS}X: }\ E ٽf
fE;E ~ E U<E< 4
݅h٭٭ ppB E
EH >E E ٽf
fE;E ~ E U<E< 4
݅h٭٭ TE
EH >E ٽf
fE @EPB E 9E~ E E | E;E ~ E |E|E U<E 4
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݅hٽf
f٭٭ \$D$@ PfB $Y E sE UD$k.@ PfB $~Y E D$D$ ED$D$ E$O2E D$D$ ED$D$ E$&2E D$D$ PB D$D$ ED$D$ E$5PB D$D$ E D$D$ ED$D$ E$\5ED$D$ PB E D$D$ PB E D$D$ t$5݅hݝhPB E D$E @D$E$.-PB E D$E @D$|$ -PB E D$E @D$ PB D$D$ E$0PB D$D$ PB E D$E @D$E$0D$ @ tB $W D$`@ tB $W $4D$D$@ tB $lW $t4D$D$ @ tB $EW $M4D$D$@@ tB $ W $&4Í$4\$ D$D$@ gB $V (D$D$@ E$4LD$T$ ELD$D$ @ gB $V (D$D$@ E$64LD$ T$D$U@ tB $XV PB D$D$ E$++E D$D$ E D$D$ Eȉ$
/PB D$D$ E D$D$ Ẻ$.PB E D$E @D$ PB E D$E @D$E$.PB D$D$ PB E D$E @D$Eĉ$.PB E D$E @D$ PB E D$E @D$x$I.}\ t>D$ D$ D$ D$ D$ D$ T$1nB $>T tB $T gB $T < [^_]É' # Standard deviation of stable prevalences
%1d-%1d %.5f (%.5f) t& @`@ (@ ? @UWVS E$]E,]E@]D$@ hB $5T D$@ hB $ T E E;E ~*EȉD$ EȉD$D$
@ hB $S E D$k.@ hB $S PB D$$ e(EPB D$ D$ E D$$ X+EЋE D$ D$ E D$$ 3+EE UE LЋL9LLE(@ ]E]EEs Ee0@ E 8@ $+R }Ef
fEm]mE 0@ sE UMLЋL9LLEE D$ D$ PB D$$ B*EE D$$ 'EE D$$ &EE E;PB ~ E E;PB ~zEEU|Eȍ EݝpE;Eu E E݅pݝh݅pݝh݅hE| E yEHD$$E\$ E8D$E4D$E\$ ED$E D$E<$FE E;E ~4Eȍ< uEȍ M<Eȍ >E E E;PB ~ EdE`Eȍ EݝXE;Eu E E݅X$ݝP݅XݝP݅Pd` E pEHD$$E\$ E8D$E4D$E\$ ED$E D$E<$EE E;E ~4Eȍ< uEȍ M<Eȍ >E E E;E ~\E UEȍ4 <
Eȍ MEȍ E$@@ E E4 7E 뚍E RPB D$ D$ E D$$ :'EE E;E ~\E E;PB ~AEč UE< 4
E MEč >E 벍E E E;E ~AEȍ ] E}Ef
fEm]mE E 뵋ED$ PB D$ D$ PB D$D$ E D$ D$ ED$EЉ$MED$ E D$ D$ PB D$D$ E D$ D$ EЉD$Ẻ$kME E;E ~\Eȍ M E}Ef
fEm]mE< 4Eȍ M̋Eȍ >E E\$D$@ hB $M E E;E ~ Eȍ ] E}Ef
fEm]mE $eL \$Eȍ M<Eȍ \$D$@ hB $ M E oD$k.@ hB $L E D$D$ E$!E D$D$ E$!E D$D$ PB D$D$ E$%PB D$D$ E D$D$ E$\%E]#PB D$D$ E$&!PB D$D$ E D$D$ Ẻ$%E D$D$ E D$D$ EЉ$$ [^_]prob probcov probcor Computing standard deviation of one-step probabilities: result on file '%s'
Computing matrix of variance covariance of one-step probabilities: result on file '%s'
& and correlation matrix of one-step probabilities: result on file '%s'
#One-step probabilities and stand. devi in ()
#One-step probabilities and covariance matrix
#One-step probabilities and correlation matrix
p%1d-%1d (SE) p%1d-%1d
# Routine varprob v
<li><h4> Computing and drawing one step probabilities with their confidence intervals</h4></li>
<li><h4> <a href="%s">Matrix of variance-covariance of pairs of step probabilities (drawings)</a></h4></li>
<h4>Matrix of variance-covariance of pairs of step probabilities</h4>
file %s<br>
v '
Ellipsoids of confidence centered on point (p<inf>ij</inf>, p<inf>kl</inf>) are estimatedand drawn. It helps understanding how is the covariance between two incidences. They are expressed in year<sup>-1</sup> in order to be less dependent of stepm.<br>
<br> Contour plot corresponding to x'cov<sup>-1</sup>x = 4 (where x is the column vector (pij,pkl)) are drawn. It can be understood this way: if pij and pkl where uncorrelated the (2x2) matrix of covariance would have been (1/(var pij), 0 , 0, 1/(var pkl)), and the confidence interval would be 2 standard deviations wide on each axis. <br> Now, if both incidences are correlated (usual case) we diagonalised the inverse of the covariance matrix and made the appropriate rotation to look at the uncorrelated principal directions.<br>To be simple, these graphs help to understand the significativity of each parameter in relation to a second other one.<br>
**********
#
V%d=%d
<hr size="2" color="#EC5E5E">********** Variable **********
<hr size="2" color="#EC5E5E">
%d %11.3e (%11.3e) %11.3e
%d %d-%d %11.3e & %d %d%d-%d%d mu %.4e %.4e Var %.4e %.4e cor %.3f cov %.4e Eig %.3e %.3e 1stv %.3f %.3f tang %.3f
Others in log...
' %d %d%d-%d%d mu %.4e %.4e Var %.4e %.4e cor %.3f cov %.4e Eig %.3e %.3e 1stv %.3f %.3f tan %.3f
set parametric;unset label v
set log y;set log x; set xlabel "p%1d%1d (year-1)";set ylabel "p%1d%1d (year-1)" & '
set ter png small
set size 0.65,0.65 varpijgr
<br>Ellipsoids of confidence cov(p%1d%1d,p%1d%1d) expressed in year<sup>-1</sup> :<a href="%s%d%1d%1d-%1d%1d.png">%s%d%1d%1d-%1d%1d.png</A>,
<br><img src="%s%d%1d%1d-%1d%1d.png">
<br> Correlation at age %d (%.3f),
set out "%s%d%1d%1d-%1d%1d.png"
set label "%d" at %11.3e,%11.3e center
# Age %d, p%1d%1d - p%1d%1d
plot [-pi:pi] %11.3e+ %.3f*(%11.3e*%11.3e*cos(t)+%11.3e*%11.3e*sin(t)), %11.3e +%.3f*(%11.3e*%11.3e*cos(t)+%11.3e*%11.3e*sin(t)) not %d (%.3f), & '
replot %11.3e+ %.3f*(%11.3e*%11.3e*cos(t)+%11.3e*%11.3e*sin(t)), %11.3e +%.3f*(%11.3e*%11.3e*cos(t)+%11.3e*%11.3e*sin(t)) not
set out "%s%d%1d%1d-%1d%1d.png";replot; & @ (@ @UWVS E ]E$]E E E pA ݝD$@ $> D$PsB $> D$'q@ $= pdB =pdB u5D$$)q@ 9> D$D$)q@ oB $j> D$@ $t> D$PsB $= D$'q@ $x= uB =uB u5D$$)q@ = D$D$)q@ oB $= D$@ $= D$PsB $y= D$'q@ $< fB =fB u5D$$)q@ /= D$D$)q@ oB $`= D$$ @ < D$D$ @ oB $+= D$$`@ < D$D$`@ oB $< D$$@ < D$D$@ oB $< D$ @ pdB $< D$@ pdB $< D$`@ uB $< D$@ uB $m< D$@ fB $X< D$@ uB $C< E E;E~ E PB E9E~pEЉD$ EԉD$D$@ pdB $; EЉD$ EԉD$D$@ uB $; EЉD$ EԉD$D$@ fB $; E 끍E cPB D$$ 4$PB D$ D$ PB EED$$ ,PB EED$ D$ PB EED$$ (Eٽf
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~ E =`B D$H@ pdB $8 E E;`B ~ E aB 4 }4E
jB E 7D$ E aB D$D$_@ pdB $G8 E nD$@ pdB $(8 D$H@ uB $8 E E;`B ~ E aB 4 }4E
jB E 7D$ E aB D$D$_@ uB $7 E nD$@ uB $e7 D$H@ tB $P7 E E;`B ~ E aB 4 }4E
jB E 7D$ E aB D$D$@ tB $6 E nD$@ tB $6 D$ @ PjB $6 E E;`B ~ E aB 4 }4E
jB E 7D$ E aB D$D$_@ gB $5 E nD$`@ PjB $5 D$H@ fB $5 E E;`B ~ E aB 4 }4E
jB E 7D$ E aB D$D$_@ fB $;5 E nD$g@ fB $ 5 Eݝ݅Es| ݅ݝE E;`B ~qUE E04 }4E jB E E0 7ݜE E E; `B ~?E aB E aB ݄܍ݜE E E;dB ~ E dB E sB 4 }4E, jB E sB 47E sB 4 }4E, jB E sB 73
P$d$ݜE PB EED$ D$ PB D$$
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PB EED$$ PB EED$$ Dž ;PB ~ E E;PB ~ E$Eԍ EݝE;u! E݅ݝ݅ݝ݅ E gED$$D$ @uB D$D$ sB $,E E E;E~\E PB E9E~?E E< Eԍ
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Eԍ Eԍ $pA E4 7E 낍 E PB EE9E~tDž ;PB ~PEЍ < 4
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fE;E~ E PB E9E~gE E ݅٭٭< 4
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݅٭٭< 4Eԍ (EЍ >E {E T݅٭٭D$D$@ pdB $+ ݅ٽf
f٭٭D$D$@ uB $* ݅ٽf
f٭٭D$D$@ fB $x* E PB EE9E~ Eԍ Eԍ
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f٭٭ \$D$@ pdB $) E E PB EE9E~ Eԍ ݅ٽf
f٭٭ \$D$@ uB $( Eԍ ݅ٽf
f٭٭ \$D$@ fB $( E E E E;E~ E PB E9E~ EԉEԍE ED$ED$ ݅ٽf
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f٭٭ $$ ܽ\$D$@ fB $% E 1E IE +݅ݝoE E E;E~5 E PB E9E~ E;Eu UJPB EEEE E;E~ E PB E9E~ E;Eu UJPB EEĉEԋE;Ev Eݝ݅Es ݅ٽf
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݅٭٭ PB xA PB xA ݝ@Eԍ EЍ
݅٭٭ PB xA PB xA ]Eԍ ݅٭٭ PB xA ]EЍ ݅٭٭ PB xA ]݅H܍@ $! Eݝ8݅H܅@ݝ݅H܅@݅H܅@݅H܍@EMA $%! ݅pA ݝx݅H܅@ݝ݅H܅@݅H܅@݅H܍@EMA $ ݅pA ݝp݅Hܥx݅Hܥxuu ${ ݝX݅xܥHu܍Xݝ`݅`ݝh݅XݝP݅`ܵXݝ0} E ݅0\$h݅`\$`݅X\$X݅p\$P݅x\$HE\$@݅8\$8݅@\$0݅H\$(E\$ E\$ED$ED$EĉD$ EȉD$݅ٽf
f٭٭D$$@ ݅0\$l݅`\$d݅X\$\݅p\$T݅x\$LE\$D݅8\$<݅@\$4݅H\$,E\$$E\$ ED$ED$EĉD$EȉD$ ݅ٽf
f٭٭D$D$@@ oB $b } E D$@ tB $< ED$ED$EĉD$ EȉD$D$@ tB $ D$ A tB $ D$F A E$AD$F A E$,ED$DED$@EĉD$<EȉD$8ED$4\$0ED$,ED$(EĉD$$EȉD$ ED$ T$ED$ED$EĉD$ EȉD$D$` A PjB $M D$F A E$ED$ ED$EĉD$EȉD$ED$ T$D$ A PjB $ ݅8\$ ݅ٽf
f٭٭D$D$@A PjB $ D$F A E$ED$ ED$EĉD$EȉD$ED$ T$D$A tB $R E\$E\$ ݅ٽf
f٭٭D$D$A tB $ ED$ED$EĉD$EȉD$ ݅ٽf
f٭٭D$D$A tB $ ݅p $ ݝ݅x $ ݝ݅p $t ݝx݅x $` ݅\$`݅P\$X݅\$P݅`\$H݅\$@E\$8݅x\$0݅h\$(\$ ݅X\$݅\$E\$D$ A tB $ E ݅8\$ ݅ٽf
f٭٭D$D$A PjB $Z ED$ED$EĉD$EȉD$ ݅ٽf
f٭٭D$D$A tB $ E\$E\$ ݅ٽf
f٭٭D$D$A tB $ ݅p $ ݝp݅x $ ݝh݅p $| ݝ`݅x $h ݅p\$`݅P\$X݅h\$P݅`\$H݅\$@E\$8݅`\$0݅h\$(\$ ݅X\$݅\$E\$D$A tB $ ݅ݝD$F A E$ED$ ED$EĉD$EȉD$ED$ T$D$@A tB $a E E ?E !E E E E Eٽf
f٭٭D$E٭٭D$PB ED$D$ ED$D$ $;Eٽf
f٭٭D$E٭٭D$ PB U¡PB ED$D$ $ PB D$D$ $$pdB $& uB $ fB $ tB $_ PjB $R [^_]Ð<ul><li><h4>Result files (first order: no variance)</h4>
- Observed prevalence in each state (during the period defined between %.lf/%.lf/%.lf and %.lf/%.lf/%.lf): <a href="%s">%s</a> <br>
pij - Estimated transition probabilities over %d (stepm) months: <a href="%s">%s</a><br>
pl t& - Stable prevalence in each health state: <a href="%s">%s</a> <br>
e - Life expectancies by age and initial health status (estepm=%2d months): <a href="%s">%s</a> <br>
</li>
<ul><li><b>Graphs</b></li><p> <hr size="2" color="#EC5E5E">************ Results for covariates ************
<hr size="2" color="#EC5E5E"> pe <br>- Pij or Conditional probabilities to be observed in state j being in state i, %d (stepm) months before: %s%d1.png<br> <img src="%s%d1.png">
<br>- Pij or Conditional probabilities to be observed in state j being in state i %d (stepm) months before but expressed in per year i.e. quasi incidences if stepm is small and probabilities too: %s%d2.png<br> <img src="%s%d2.png"> <br>- Stable prevalence in each health state : p%s%d%d.png<br> <img src="%s%d%d.png"> exp
<br>- Health life expectancies by age and initial health state (%d): %s%d%d.png <br> <img src="%s%d%d.png"> </ul> '
<br><li><h4> Result files (second order: variances)</h4>
- Parameter file with estimated parameters and covariance matrix: <a href="%s">%s</a> <br>
' - Variance of one-step probabilities: <a href="%s">%s</a> <br>
- Variance-covariance of one-step probabilities: <a href="%s">%s</a> <br>
- Correlation matrix of one-step probabilities: <a href="%s">%s</a> <br>
v - Variances and covariances of life expectancies by age and initial health status (estepm=%d months): <a href="%s">%s</a><br>
t - Health expectancies with their variances (no covariance): <a href="%s">%s</a> <br>
vpl t& - Standard deviation of stable prevalences: <a href="%s">%s</a> <br>
<ul><li><b>Graphs</b></li><p> <br>- Observed (cross-sectional) and period (incidence based) prevalence (with 95%% confidence interval) in state (%d): %s%d%d.png <br><img src="%s%d%d.png">
<br>- Total life expectancy by age and health expectancies in states (1) and (2): %s%d.png<br><img src="%s%d.png"> UWVS E4]EH]EP]EX]E`]Eh]Ep]D$@ E$D$@ E$\$<D$8E\$0E\$(E\$ E\$E\$E\$D$ +A gB $7
D$+A E$D$+A E$m\$D$ E D$D$ ,A gB $ D$X,A E$6D$X,A E$!\$ D$D$`,A gB $ D$,A E$D$,A E$\$D$ EDD$D$,A gB $Z D$@-A gB $E `B 0jB =`B 0jB `pB E E E;0jB ~ E E `pB E;
~ E =`B D$`-A gB $ E E;`B ~ E aB 4 =0gB E
jB E 7D$ E aB D$D$@ gB $ E kD$-A gB $ D$-A $lB JD$-A $lB 4ED$\$ED$T$ E D$D$ .A gB $
D$-A $lB D$-A $lB ED$\$ED$T$ E D$D$.A gB $D
E E;PB |lD$@ $lB zD$@ $lB dED$ ED$\$ED$ED$ T$D$/A gB $ E E E;PB ~sD$/A $lB D$/A $lB ED$ ED$ \$ED$ED$T$ ED$D$ 0A gB $D E 뀍E >E D$m0A gB $ E<D$ E<D$D$0A gB $ D$@ E$<D$@ E$'\$ D$D$ 1A gB $ D$@ E$D$@ E$\$ D$D$1A gB $g D$@ E$D$@ E$\$ D$D$1A gB $" D$+2A E$mD$+2A E$X\$D$ EDD$D$@2A gB $ D$2A E$!D$2A E$ \$ D$D$2A gB $ D$73A E$D$73A E$\$ D$D$@3A gB $L gB $ D$3A gB $* `B 0jB =`B 0jB `pB E E E;0jB ~ E E `pB E;
~ E =`B D$`-A gB $ E E;`B ~ E aB 4 =0gB E
jB E 7D$ E aB D$D$@ gB $ E kD$-A gB $ E E;PB ~sD$+2A $lB D$+2A $lB ED$ ED$ \$ED$ED$T$ ED$D$3A gB $e E D$,A $lB D$,A $lB ED$\$ED$ T$D$`4A gB $ E #E D$m0A gB $ gB $q Č [^_]cd "%s"
set out "%s%d%d.png"
#set out "v%s%d%d.png"
set xlabel "Age"
set ylabel "Probability"
set ter png small
set size 0.65,0.65
plot [%.f:%.f] "%s" every :::%d::%d u 1:2 "%%lf %%lf (%%lf) %%*lf (%%*lf) v " t"Stable prevalence" w l 0,"%s" every :::%d::%d u 1:($2+1.96*$3) "%%lf " t"95%% CI" w l 1,"%s" every :::%d::%d u 1:($2-1.96*$3) "%%lf " t"" w l 1,"%s" every :::%d::%d u 1:($%d) t"Observed prevalence " w l 2
set out "%s%d.png"
set ylabel "Years"
set ter png small
set size 0.65,0.65
plot [%.f:%.f] "%s" every :::%d::%d u 1:2 "%%lf " t"TLE" w l , " t"LE in state (%d)" w l , "%s" every :::%d::%d u 1:($2-$3*2) "%%lf " t"" w l 0, v ' "%s" every :::%d::%d u 1:($2+$3*2) "%%lf " t"" w l 0 t& ' set ter png small
set size 0.65,0.65
plot [%.f:%.f] "%s" every :::%d::%d u 1:%d t "e%d1" w l v ,"%s" every :::%d::%d u 1:%d t "e%d%d" w l set xlabel "Age"
set ylabel "Probability"
set ter png small
set size 0.65,0.65
unset log y
plot [%.f:%.f] "%s" u ($1==%d ? ($3):1/0):($%d/($%d +$%d )) t"prev(%d,%d)" w l ,"%s" u ($1==%d ? ($3):1/0):($%d/($%d )) t"prev(%d,%d)" w l
p%d=%f
set ylabel "Quasi-incidence per year"
set title "Probability"
set ter png small
set size 0.65,0.65
set log y
plot [%.f:%.f] %f*exp(p%d+p%d*x exp(p%d+p%d*x +p%d*%d*x +p%d*%d )/(1 +exp(p%d+p%d*x ) ) t "p%d%d" , & @ (@UWVS E]E]E ]ЋE(D$D$=A tB $ `B \$CA $ ٽvvf
ft٭t٭vE D$8$O D$73A $9 Dž ;PB ~ Dž ;~ D$+2A E $*D$D$ T$D$=A tB $ D$D$ E D$D$ >A tB $m D$73A E$HD$ HD$ T$E\$E\$D$@>A tB $ Dž ;PB ~D;uD$>A tB $ D$>A tB $ D$73A E$HD$HD$ T$D$>A tB $y Dž ;PB ~D;uD$>A tB $< D$>A tB $% D$73A E$fHD$HD$ T$D$@?A tB $ Dž ;PB ~D;uD$>A tB $ D$>A tB $ D$@ E$D$HD$HD$ T$D$?A tB $% & Dž ;~ D$,A E $9D$ T$D$?A tB $ E\$E\$D$?A tB $ Dž PB @9~ D$2A E$HD$HD$ T$D$@@A tB $$ Dž PB @9~D;uD$>A tB $ D$>A tB $ 묃uD$a@A tB $ HD$D$p@A tB $ D$2A E$HD$HD$ T$D$@A tB $B Dž PB @9~D;uD$>A tB $ D$>A tB $ D$@A tB $ D$2A E$HD$HD$ T$D$@A tB $ Dž PB @9~D;uD$>A tB $N D$>A tB $7 묡PB @9uD$ AA tB $
D$@A tB $ ] Dž ;~ Dž ;PB ~b PB D$/A E $D$D$ T$D$=A tB $B D$,A E$D$(D$$HD$ HD$ T$E\$E\$D$ AA tB $ Dž ;PB |xD$,A E$
@D$ D$D$HD$HD$ T$D$AA tB $W x aDž ;~X Dž ;PB ~. Dž D$@ E $7D$D$ T$D$=A tB $ D$+A E$@D$$@D$ D$ T$E\$E\$D$AA tB $H Dž ;PB |0@D$D$QBA tB $ @D$ D$D$VBA tB $ PB PB |D$+A E$|@D$|@D$D$ T$D$BA tB $e Dž ;PB |KPB PB ||@D$D$QBA tB $
륋@D$ @D$D$BA tB $ Dž Dž ;PB ~ Dž PB PB 9~ ;twDž ;@uB ~] E,\$ D$D$BA tB $ D$k.@ tB $ 듍 V ,Džx x~ Dž ;~ D$-A E $xD$D$ T$D$=A tB $\ xuD$BA tB $> D$CA tB $' E\$E\$D$@CA tB $ Dž Dž ;PB ~ Dž PB PB 9~ ; xu>@D$D$PB CA \$D$CA tB $X *@D$ D$D$CA tB $, Dž Dž ;@uB ~U aB ; ; `B aB 4 =0gB jB aB 7D$ HD$D$CA tB $C |aB 50gB jB
D$ HD$D$CA tB $ D$CA tB $ Dž ;PB ~ H@uB @D$ H@uB D$D$CA tB $8 Dž Dž ;@uB ~t aB ; ; `B aB 4 =0gB jB aB 7D$ H@uB HD$D$CA tB $A aB 50gB jB
D$ H@uB HD$D$CA tB $ yD$CA tB $ D$ D$D$CA tB $R ¡PB PB 9tD$CA tB $ @uB < 8x tB $o ļ [^_]UWVSTE ]E]E `B EЃ=`B E } t} t} t } t } u E E EE]EEs E E;PB ~ E E;E~ E}Ef
fEm]mE U E܍
Eԍ< 4Em]mE UE܍
Eԍ >E nE ME]%E E;E~ UJ P$d$E]UJ P$d$EEs E E;PB ~ E E;E~t E}Ef
fEm]mE U E܍
Eԍ< 4Em]mE UE܍
Eԍ >E UJ 9E~\ Em]mE U E܍
EE<Em]mE U E܍
EE4Em]mE+E؍ UE܍
Eԍ ME Em]mE U E܍
EE<Em]mE U E܍
EE4Em]mEE؍ UE܍
Eԍ ME E Em]mE U E܍
Eԍ4 <Em]mE U E܍
Eԍ E 7E E ^E]Eȃ EE ET[^_]f & Problem with forecast resultfile: %s
Computing forecasting: result on file '%s'
# Mean day of interviews %.lf/%.lf/%.lf (%.2f) between %.2f and %.2f
#****** Routine prevforecast **
#****** V%d=%d, hpijx=probability over h years, hp.jx is weighted by observed prev ******
t& ' # Covariate valuofcovar yearproj age p%d%d p.%d & '
# Forecasting at date %.lf/%.lf/%.lf %d %d %.f %.f %.3f v @`@ (@ >@ ?UWVS E ]E]E ]E$]E,]E4]E<]EH]EP]E`]\A ݝXE\D$LEXD$HE\$@E\$8`fB D$4pB D$0`pB D$,0gB D$(aB D$$0uB D$ PB D$ nB D$`dB D$E\$ E\$ppB $@D$wZA x$> ED$x$ D$'q@ x$C bB =bB u5xD$$ZA xD$D$ZA oB $ xD$$ZA J xD$D$ZA oB ${ }l uEl `pB }D D$ D$ D$ D$ D$ $ 蒽EDD$D$E\$ E\$ppB $+t/EDD$D$ @ oB $ EDD$$ @ l PB \A ٽvvf
ft٭tp٭vpP$d$\A ٭t]٭v=PB E oB ;PB } PB D$oB D$$ɶ@ oB EE=PB EHD$ `B $ ݝ@݅Hݝ HD$݅@\A $ ݝ8݅Hݝ(HD$݅8\A $ ݝ@݅Hݝ0݅0ztݝ0݅(ztݝ0Ell=`B
Džl E\$0E\$( `B \$ ݅ \$݅(\$݅0\$D$ [A bB $ D$`[A bB $ nB E nB ;l~ Džx El `pB x;
~b E D$[A bB $^ E E;El~ E aB 4 =0gB E
jB E 7D$ E aB D$D$[A bB $ E nD$[A bB $ D$ \A bB $ E PB PB 9E~mDžt t;PB ~0ED$ tD$D$%\A bB $B t ED$D$,\A bB $ E E EEes D$k.@ bB $ EE\$E\$E\$D$@\A bB $ Eݝ`E݅`se ݅Xܥ`\A PB \A $ ٽvvf
ft٭t]٭vUMdЋd9ddEED$D$ PB PB D$ D$ PB PB D$$ LpfB gB nB oB ED$(oB D$$gB D$ PB D$ PB D$EhD$ED$݅`\$ED$$Džp p;E~ pEP$d$\A PB Ezt D$k.@ bB $ E E;El~ E aB 4 =0gB E
jB E 7D$ E aB D$D$g\A bB $v E npEP$d$\A PB ܅`\$EE\$D$n\A bB $! E PB PB 9E~h ݝPDžt t;PB ~ }D t E
p4 <݅`ٽvvf
ft٭tp٭vp t
x 7 ݅PݝP t E
p4 <݅`ٽvvf
ft٭tp٭vp ppB t
x 7 ݅PݝPpEP$d$\A PB EztLt E
p \$D$w\A bB $ t pEP$d$\A PB Ezt ݅P\$D$w\A bB $ E p 1ED$D$ PB PB D$D$ PB PB D$D$ $݅`ݝ`UEx ~nB W}D t>D$ D$ D$ D$ D$ D$ $kbB $ [^_]pop r Problem with population file : %s
%d %lf
P.%d [Population]
# Forecasting at date %.lf/%.lf/%.lf
%3.f %15.2f @`@ (@ v@ ?UWVS E ]E]E ]E$]E,]E4]E<]EH]EP]E`]D$ D$ D$ D$ D$ $ 8D$ D$ D$ D$ D$ $ 賱4iA ݝXEiA EEiA % `B iA ݝ`PB D$L `B D$HE\$@E\$8`fB D$4pB D$0`pB D$,0gB D$(aB D$$0uB D$ PB D$ nB D$`dB D$E\$ E\$ppB $3D$iA $ ED$$
D$'q@ $ qB = qB u5D$$ZA D$D$ZA oB $ D$$ZA D$D$ZA oB $ =`B u`B `pB }D D$ D$ D$ D$ D$ $ ί0EDD$0D$E\$ E\$ppB $gt/EDD$D$ @ oB $ EDD$$ @ PB iA ٽf
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t 몋t0uB Džt t;0uB |Ft l Dt @ t nB E nB ;El~2 Džx `B `pB x;
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f٭٭* ))P$d$iA E]݅`٭٭* ))P$d$iA EEs ݅XeiA PB iA $ ٽf
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f٭٭9puVpEP$d$iA PB E\$D$iA qB $ ٽf
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x ݅PݝPE E٭٭ 4t
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x ܵPUP$d$܅`EiA PB E٭٭ D 7t oUP$d$܅`٭٭9p E E;PB ~ Eٽf
f٭٭ 4E
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f٭٭* ))P$d$iA E]݅`٭٭* ))P$d$iA EEs ݅XeiA PB iA $| ٽf
f٭]٭UMЋ9EED$D$ PB PB D$ D$ PB PB D$$ /<pfB gB nB oB ED$(oB D$$gB D$ PB D$ PB D$EhD$ED$E\$ED$<$Džp p;E~ EiA ܅`ٽf
f٭٭9pu>pEP$d$iA PB E\$D$iA bB $ E PB PB 9E~? ݝPݝHDžt ٽf
ft;PB ~ t <E
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x > ݅PݝPt VUP$d$܅`٭٭9pu ݅P\$D$iA bB $] E p ED$D$ PB PB D$D$ PB PB D$D$ <$蟢E]ٽf
f E Ix nB }D t>D$ D$ D$ D$ D$ D$ 0$}XuZD$ D$ l$踚D$ D$ D$D$ D$ @$D$ D$ D$ D$ D$ D$ 8$tD$ D$ D$ D$ D$ D$ 4$6 qB $ [^_]a Problem with file: %s
UD$6~A E $ E} u8E D$$8~A E D$D$8~A oB $ E E$ E E# Parameters nlstate*nlstate*ncov a12*1 + b12 * age + ...
%1d%1d 0. # Scales (for hessian or gradient estimation)
# Covariance matrix
#%1d%1d%d %1d%1d%d Cov(%s%1d%1d,%s%1d%1d) Var(%s%1d%1d) U $~A D$~A E$ E E;E ~ Džt E EE 9E~ E;Eu t ED$ED$$~A 6 ED$ ED$D$~A E$e E E;E~&$A D$A E$5 E $k.@ D$k.@ E$ E @E $ A D$ A E$ EE HE EpE E;E ~ Džt E EE 9E~ E;Eu t ED$ ED$D$~A E$\ ED$ED$$~A B $ E E;E~&$A D$A E$ E Ѝh $k.@ D$k.@ E$ E (E $OA d B $D D$OA E$ Džl l~( Džt E E;E ~ E EE 9E~ E;Eu E E;E~ t E`EE luKED$ ED$ED$$dA 蔿 ED$ED$ ED$D$dA E$輿 IED$ ED$ED$$nA I ED$ED$ ED$D$nA E$q Džx E E;E ~ E EE 9E~ E;Eux Dž| |;E~^ x x;t7 |`EE x;t luxED$ED$ED$ED$ ED$E؉D$$wA J ED$ ED$ED$ED$ED$ E؉D$D$wA E$] $A D$A E$9 sluKED$ ED$E؉D$$A 载 ED$ED$ E؉D$D$A E$ $A 臽 D$A E$Ľ | E aE C$k.@ G D$k.@ E$脽 h E BE E l Í& (@ .@ Uh]]]E E 0uB H9E~3Eԍ `uB E]E]E]؍E E 0uB H9E~ Eԍ tB <ȋEԍ pqB <!ȅ E E0]ȋE hA Eԍ uB hA ɋPB P$d$ $x ]E hA Eԍ jB hA ɋPB P$d$ $( mM]Eԍ tB < ȋEԍ pqB <!ȅ8 E E0]E hA Eԍ tB hA ɋPB P$d$ $聻 ]E hA Eԍ jB hA ɋPB P$d$ $1 mE]E hA $ EE hA Eԍ tB hA ɋPB P$d$]hA $豹 E]Eԍ pqB <ȋEԍ uB pA !ȅt Eԍ `uB E E]E ExA MuÐ<ul><li><h4>Result files </h4>
Force of mortality. Parameters of the Gompertz fit (with confidence interval in brackets):<br> mu(age) =%lf*exp(%lf*(age-%d)) per year<br><br> p[%d] = %lf [%f ; %f]<br>
<br><br><img src="graphmort.png"> UWVS<D$ A gB $萸 PB D$E, \$E, \$D$A gB $Z E }~ E4 },E M0E $6 7]E4 },E M0E $ 7E\$ \$E E,\$ ED$D$A gB $蘷 E ;D$A gB $y D$m0A gB $d gB $ <[^_]set out "graphmort.png"
t& set xlabel "Age"
set ylabel "Force of mortality (per year)"
set ter png small
set log y
set size 0.65,0.65
& ' plot [%d:100] %lf*exp(%lf*(x-%d)) U E]E]E ]E(D$D$=A tB $W E D$H$b D$73A $L D$aA tB $ D$A tB $ D$A tB $ D$ދA tB $ص PB D$ E, \$E, \$ PB D$D$ A tB $虵 b c d c
%s
%s
Enter the parameter file name: %s pathimach=%s, pathtot=%s,
path=%s,
optionfile=%s
optionfilext=%s
optionfilefiname=%s
mkdir Problem creating directory or it already exists %s%s, err=%d
.log Problem with logfile %s
Log filename:%s
Enter the parameter file name:
pathimach=%s
pathtot=%s
path=%s
optionfile=%s
optionfilext=%s
optionfilefiname=%s
Local time (at start):%s Local time (at start): %s .txt Problem with optionfile %s
o Problem with Output resultfile: %s
title=%s datafile=%s lastobs=%d firstpass=%d lastpass=%d
ftol=%lf stepm=%d ncovcol=%d nlstate=%d ndeath=%d maxwav=%d mle=%d weight=%d model=%s
title=%s datafile=%s lastobs=%d firstpass=%d lastpass=%d
ftol=%e stepm=%d ncovcol=%d nlstate=%d ndeath=%d maxwav=%d mle=%d weight=%d
model=%s
You choose mle=-1, look at file %s for a template of covariance matrix
You choose mle=-3, look at file %s for a template of covariance matrix
Error in line parameters number %d, %1d%1d instead of %1d%1d
%lf %le %le %.5le Problem writing new parameter file: %s
#%s
Problem with datafile: %s
age age*age Error. Non available option model=%s Error! Date of death (month %2d and year %4d) of individual %ld on line %d was unknown, you must set an arbitrary year of death or he/she is skipped and results are biased
Error! Month of death of individual %ld on line %d was unknown %2d, you should set it otherwise the information on the death is skipped and results are biased.
Error! Month of death of individual %ld on line %d was unknown %f, you should set it otherwise the information on the death is skipped and results are biased.
Warning negative age at death: %ld line:%d
Error: on wave %d of individual %d status %d > (nlstate+ndeath)=(%d+%d)=%d
Total number of individuals= %d, Agemin = %.2f, Agemax= %.2f
-mort .gp Problem with file %s
# %s
# %s
set missing 'NaNq'
.htm Problem with %s
-cov.htm <body>
<title>IMaCh Cov %s</title>
<font size="2">%s <br> %s</font> <hr size="2" color="#EC5E5E">
Title=%s <br>Datafile=%s Firstpass=%d Lastpass=%d Stepm=%d Weight=%d Model=%s<br>
' <body>
<title>IMaCh %s</title>
<font size="2">%s <br> %s</font> <hr size="2" color="#EC5E5E">
Title=%s <br>Datafile=%s Firstpass=%d Lastpass=%d Stepm=%d Weight=%d Model=%s<br>
<hr size="2" color="#EC5E5E"> <ul><li><h4>Parameter files</h4>
- Copy of the parameter file: <a href="o%s">o%s</a><br>
- Log file of the run: <a href="%s">%s</a><br>
- Gnuplot file name: <a href="%s">%s</a><br>
- Date and time at start: %s</ul>
<br>Total number of observations=%d <br>
Youngest age at first (selected) pass %.2f, oldest age %.2f<br>
Interval (in months) between two waves: Min=%d Max=%d Mean=%.2lf<br>
pow-mort
Covariance matrix
%f
iter=%d MLE=%f Eq=%lf*exp(%lf*(age-%d))
%f [%f ; %f]
' First Likeli=%12.6f ipmx=%ld sw=%12.6f %d %8.5f
Second Likeli=%12.6f ipmx=%ld sw=%12.6f title=%s datafile=%s lastobs=%d firstpass=%d lastpass=%d
ftol=%e stepm=%d ncovcol=%d nlstate=%d ndeath=%d maxwav=%d mle= 0 weight=%d
model=%s
%d%d %1d%1d %.5e # Covariance matrix
# 121 Var(a12)
# 122 Cov(b12,a12) Var(b12)
# ...
# 232 Cov(b23,a12) Cov(b23,b12) ... Var (b23)
& agemin=%lf agemax=%lf bage=%lf fage=%lf estepm=%d
' # agemin agemax for life expectancy, bage fage (if mle==0 ie no data nor Max likelihood).
t& agemin=%.0f agemax=%.0f bage=%.0f fage=%.0f estepm=%d
' begin-prev-date=%lf/%lf/%lf end-prev-date=%lf/%lf/%lf mov_average=%d
begin-prev-date=%.lf/%.lf/%.lf end-prev-date=%.lf/%.lf/%.lf mov_average=%d
pop_based=%d
prevforecast=%d starting-proj-date=%lf/%lf/%lf final-proj-date=%lf/%lf/%lf mobil_average=%d
v prevforecast=%d starting-proj-date=%.lf/%.lf/%.lf final-proj-date=%.lf/%.lf/%.lf mobil_average=%d
Problem with stable prevalence resultfile: %s
Computing stable prevalence: result on file '%s'
#Stable prevalence
#Age %d-%d %.5f t& Problem with Pij resultfile: %s
Computing pij: result on file '%s'
#****** h Pij x Probability to be in state j at age x+h being in i at x
#****** ' # Cov Agex agex+h hpijx with i,j= %d %3.f %3.f Problem with total LE resultfile: %s
Computing Total LEs with variances: file '%s'
Problem with Health Exp. resultfile: %s
Computing Health Expectancies: result on file '%s'
t& ' Problem with variance resultfile: %s
Computing Variance-covariance of DFLEs: file '%s'
#Total LEs with variances: e.. (std) e.%d (std) %4.0f %7.3f (%7.3f) Problem with variance of stable prevalence resultfile: %s
t& Computing Variance-covariance of stable prevalence: file '%s'
End of Imach with %d errors and/or %d warnings
End of Imach with %d errors and/or warnings %d
End of Imach
See log file on %s
& ' Local time at start %s
Localtime at end %s Local time at start %s
Local time at end %s
Total time used %s
Total time was %d Sec.
t& <br>Local time at start %s<br>Local time at end %s<br> " gnuplot Starting graphs with: %s Problem with gnuplot
Wait...
Type e to edit output files, g to graph again and q for exiting: Starting browser with: %s
Type q for exiting: t& @x D@x @@|= @ (@ UUUUUU? @ @`@?-C 6? v@ ?UWVS @@ 莆 E N Ed E E 8A ݝ8A ݝ@A ݝ@A ݝ Dž Dž
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