MZ@ !L!This program cannot be run in DOS mode. $PEL7@B8 84>0P@ $.text34``.dataP8@.bss`.idata$:@U($PBED$PBED$PBD$ ED$0]Ít&UPBtFPBBuQt@0$PBD$X0Bt@P$PBD$:0%0PB]É'@T$$0B뙍v'U]E1ۉu1==sg=t؋u]]$ D$/t"t$ &'л$ D$S/㐾$D$7/tz$$D$ /t딉=t=t=U$@]{1I.PB$PBT$D$艒j.$B1U$tB1]ÉU$tBh1]ÉUE$B]Ív'UE$B]ÐU]US4}E$0EEEEEUEUE*P!NbEUD$ D$ $T$wEU]EUD$@BD$ $T$L]EUD$@BD$ $T$C} tE=(`Bu n/(`B] B)‰ЉU |BB]U(E$ /E}u EGD$\E$.E}u7D$E $u EED$E$.fEE$.E}u EED$E$M.UE)ЉD$ED$E $.UE)Љ‹E E $3.ED$.E$ .EEED$E$-E$-EE$-@EUE)ЉD$ED$E$-UE)Љ‹EEEUEEE $-EEE;E~.EU‹EE EE 8\u EE/EUE EEEEE$-EEE;E~EE:EuEE܋EUEEEEEE$,H9Ev+EE:EtEE@:EuE@EE‹E$,EEE;E|EU‹EEE܋EEEE;E~)E@9E|UE)E PEEEERREUR ... %s UD$@B@$+ED$D$@B@$+$c+allocation failure in vectorUUE )$+E}u $=@iEE)ЃUE E$*allocation failure in ivectorUUE )$*E}u $@EE)ЃUE E$H*UUE )$:*E}u $@EE)ЃUE E$)Ðt&allocation failure 1 in matrix()allocation failure 2 in matrix()UWVSUE )@EUE)@EE$])E}u $@EEE)EuEE$)EE<u $@SEMEEE<uEME)Љ>E@EE;E ~+EMEEPEEˋE[^_]UE UE $"(E E$(UWVSUE )@EUE)@EE$'E}u $@9EEE)EuEE$'EE<u $@EMEEE<uEME)Љ>E@EE;E ~+EMEEPEEˋE[^_]UE UE $&E E$&Ðallocation failure 3 in matrix()UWVSUE )@EUE)@EUE)@EE$1&E܃}u $@E܃EE)Eu܋EE$%EE܃<u $@'EM܋EE܋E<u܋EM܋E)Љ>E@EE;E ~+EM܋EE܍PEEˋE U܋E4 EEE$%3EM܋E<u $@:E U܋E<4 EM܋E>E U܋E4< E U܋E E)Љ7E@EE;E~EE U܋E4 E U܋E PEE뱋E@EE;E ~E U܋E4 EE܍PE EE E@EE;E~EE U܋E4 E U܋E PEE뱍EQE܃[^_]USE UE E$"E UE $"E E$"[]/UD$lB$@tB"D$@$@tB|"ED$$@tBi"@tBUD$lB$@tB"D$@$@tB4"E D$$@tB!"ED$$@tB"@tBUD$lB$@tBM"D$@$@tB!E D$$@tB!ED$$@tB!ED$$@tB!@tBUWVSE‹E ]ED$$_/@ B $ ED$D$_/@oB$ oB$X E$D$E D$ ED$ED$E$E E EwE E ]ЋEEEE E EE MsmED$D$E$ ED$D$E$ED$D$E$ED$D$Eĉ$9E8u $/@EE;E~E܍ ]E܍EE܍E$ E܍4}E܍ME܍E$7E܍MċE܍EEgE$E$]EEwXE EE]E E eX`BuzX`B X`BE] E]EeX`BuzX`B X`BME]EE]E]EwE$D$E D$ ED$ED$E$wEE;E~nE܍ U E<4 E܍M E>E܍ U E E܍EE눋E>Č[^_]ÍvI@(@UWVS,E]E$]]08@ݝ@EPBE 9E~xEPBE 9E~XEЍ UE8 4E;Eu ݝ(ݝ(݅(84E뙍Eyݝ`PB88@EݝHEܥ@݅HsE T݅HݝhEE;`B~{Uȉ$EȍaB4=0gBE, jBEȍaB 7$ݜhExEE; `B~?EȍaB EȍaB݄h܍hݜhEEE;dB~EȍdB EȍsB4=0gBE, jBEȍsB 47EȍsB4=0gBE, jBEȍsB  73 P$d$ ݜhEE D$ED$ @uBD$XD$ sB$‹ED$ PBE D$D$PBE D$D$PBE D$ D$T$T$EEE TE]EE;E ~]]EE;E ~a]EE;PB~0EԍTEE E]EËEԍ UE̍4< EԍTE̍ e7E8`BEԍME̍H`B8`BH`Bw8`Bݝ H`Bݝ݅]E8`BEԍME̍H`B8`BH`Bw8`Bݝ H`Bݝ݅]EEe]E8`BEH`B8`BH`Bw8`Bݝ H`Bݝ݅]EEEw EE룍E농EE[^_](@:0yE>UWVSݝ@EE;PB~EݜxE=bBEDžݝ(E;0uB~EE;`B~@u؋E؍aB jBEݜHEEEpqBH9E~EPBPB9E~EPBPB9E~E gBE E;Eu ݝݝ݅E oBE E;Eu ݝݝ݅E9EEEԍnBE E; |oBpiBEԍ 0oBE nBE EPBP$d$G@ݝHEE; `B~dE̍aB4E̍aBaB jBE܍HݜHE돡PBD$ED$ @uBD$8D$ sB$rD$ PBPBD$D$PBPBD$D$PBPBD$ D$gBD$piB$4gBoBpiBgBEBEԍ 0oBE `dBEE0oBHE `dBEEԍ oBEPBݝ;PB~\ 44<  oB7$$ݝ l݅4  oB݅ $Lݝ E`uB݅(ݝ(Eԍ 0oBE `dBE4Eԍ 0oBE `dBE E`uB܍ ݄xݜxEEY=bBEDžݝ(E;0uB~EE;`B~@u؋E؍aB jBEݜHEEEpqBH9E~.EPBPB9E~EPBPB9E~E gBE E;Eu ݝݝ݅E oBE E;Eu ݝݝ݅E9EEEԍnBE E; ~oBpiBEԍ 0oBE nBE EPBP$d$G@ݝHEE; `B~dE̍aB4E̍aBaB jBE܍HݜHE돡PBD$ED$ @uBD$8D$ sB$D$ PBPBD$D$PBPBD$D$PBPBD$ D$gBD$piB$4gBoBpiBgBEBEԍ 0oBE `dBEE0oBHE `dBEEԍ oBEPBݝ oBG@wn݅4  oB݅ $ݝ>݅4 $rݝ݅ݝ E`uB݅(ݝ(Eԍ 0oBE `dBE4Eԍ 0oBE `dBE E`uB܍ ݄xݜxEE@=bBEDžݝ(E;0uB~EE;`B~@u؋E؍aB jBEݜHEEEpqBH9E~SEPBPB9E~EPBPB9E~E gBE E;Eu ݝݝ݅E oBE E;Eu ݝݝ݅E9EEEԍnBE E; |oBpiBEԍ 0oBE nBE EPBP$d$G@ݝHEE; `B~dE̍aB4E̍aBaB jBE܍HݜHE돡PBD$ED$ @uBD$8D$ sB$D$ PBPBD$D$PBPBD$D$PBPBD$ D$gBD$piB$(4gBoBpiBgBEBEԍ 0oBE `dBEE0oBHE `dBEEԍ oBEPBݝ oBG@w݅ݝ4$݅ݝ oB$܍݅ݝ>݅4 $gݝ݅ݝ E`uB݅(ݝ(Eԍ 0oBE `dBE4Eԍ 0oBE `dBE E`uB܍ ݄xݜxEE=bBEDžݝ(E;0uB~ EE;`B~@u؋E؍aB jBEݜHEEEpqBH9E~EPBPB9E~EPBPB9E~E gBE E;Eu ݝݝ݅E oBE| xE;Eu ݝpݝp݅p|xE9EEEԍnBE E; |oBpiBEԍ 0oBE nBE EPBP$d$G@ݝHEE; `B~dE̍aB4E̍aBaB jBE܍HݜHE돡PBD$ED$ @uBD$8D$ sB$D$ PBPBD$D$PBPBD$D$PBPBD$ D$gBD$piB$4gBoBpiBgBEBEԍ 0oBE `dBEE0oBHE `dBE;PB~_ 44<  oB7$$*ݝ Eԍ 0oBE `dBE44E0oBHE `dBE7$ݝ E`uB݅(ݝ(Eԍ 0oBE `dBE4Eԍ 0oBE `dBE E`uB܍ ݄xݜxEEPEDžݝ(E;0uB~3EE;`B~@u؋E؍aB jBEݜHEEEpqBH9E~EPBPB9E~EPBPB9E~E gBEl hE;Eu ݝ`ݝ`݅`lhE oBE\ XE;Eu ݝPݝP݅P\XE9EEEԍnBE E; |oBpiBEԍ 0oBE nBE EPBP$d$G@ݝHEE; `B~dE̍aB4E̍aBaB jBE܍HݜHE돡PBD$ED$ @uBD$8D$ sB$D$ PBPBD$D$PBPBD$D$PBPBD$ D$gBD$piB$T4gBoBpiBgBEBEԍ 0oBE `dBEE0oBHE `dBEEԍ 0oBE `dBE44E0oBHE `dBE7$)ݝ E`uB݅(ݝ(Eԍ 0oBE `dBE4Eԍ 0oBE `dBE E`uB܍ ݄xݜxE2EE]E;PB~EE܄x]EۅMܵ(]E[^_]Ív%9d %6d %1d %1d %1d %1d %3d %10.6f %6.4f %10.6f %10.6f %10.6f %10.6f %10.6f (@:0yE>UWVS|ݝ@EE;PB~EݜxEElB uBE;0uB~ EE;`B~@u؋E؍aB jBEݜHEEEpqBH9E~( EPBPB9E~EPBPB9E~E gBE E;Eu ݝݝ݅E oBE E;Eu ݝݝ݅E9EEEԍnBE E; |oBpiBEԍ 0oBE nBE EPBP$d$pe@ݝHEE; `B~dE̍aB4E̍aBaB jBE܍HݜHE돡PBD$ED$ @uBD$8D$ sB$D$ PBPBD$D$PBPBD$D$PBPBD$ D$gBD$piB$4Eԍ 0oBE `dBEE0oBHE `dBEgBoBpiBgBEEԍ 0oBE `dBEE0oBHE `dBEEԍ oBEPBݝ;PB~h=bB_ 44<  oB7$$Zݝ(=bBuq݅4  oB݅ $ݝ(=bB oBxe@wn݅4  oB݅ $+ݝ>݅4 $ݝ݅ݝ(=bB oBxe@w݅ݝ4$O݅ݝ oB$܍݅ݝ>݅4 $ݝ݅ݝ(s=bBu64$zݝ(44$Dݝ(lBE`uB uB uBEԍ 0oBE `dBE4Eԍ 0oBE `dBE E`uB܍(݄xݜx=puB݅($[ oB\$D4\$File of contributions to the likelihood: %s
USE8D$#q@$rBD$PsB$rBD$'q@$rBhB=hBu1D$rB$)q@D$rBD$)q@oB$D$`q@hB$D$`r@hB$EE;E~#ED$D$r@hB$ED$r@hB$E $E(ЋE$E8tQhB$$rBm$rB_\$ D$D$r@gB$,gB$[]Powell pow# Powell # iter -2*LL p%1d%1d #Number of iterations = %d, -2 Log likelihood = %.12f & #Number of iterations = %d, -2 Log likelihood = %.12f &#Number of iterations = %d, -2 Log likelihood = %.12f UXE]ED$ D$ED$$认EEE;E~[EE;E~CE UEEԋ MЋE;Eu]]EȋEԋUE볍E$t@D$t@oB$ZD$t@$ hBfD$PsB$ hBD$'q@$ hBnPqB=PqBu1D$ hB$)q@D$ hBD$)q@oB$D$t@PqB$EE;E~OEPBE9E~2E;Et#ED$ ED$D$t@PqB$xE뿍ED$k.@PqB$UE$D$ED$ED$E\$ ED$ED$E $PqB$SE $E$\$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@iD$ x@oB$EE;E~EȉD$$_/@-B $ EȉD$D$_/@oB$QoB$ED$E D$ED$EȉD$ E\$E $EȍM̋EȍEREE;E~EE;E~E;EEĉD$EȉD$$Mx@QB $1EĉD$ EȉD$D$Mx@oB$noB$ED$E D$EĉD$ EȉD$ED$E $EȍM̋EčEč ŰEȍ<4 EȍM̋Eč>EE$k.@lD$k.@oB$$`x@KD$`x@oB$ED$ D$ED$$(EED$ D$ED$$EED$$ӛE܋ED$$E뵍E띍EЉD$ ED$ED$E$EE;E~EE;E~EȍEE݋EčEE܉D$ ED$ED$E$ EE;E~4Eȍ UEč EȍEEEH$x@kD$x@oB$EE;E~EE;E~jEȍM̋Eč\$$x@EȍM̋Eč\$D$x@oB$E$k.@D$k.@oB$EMEE;E~YEE;E~AEȍ UEč<4 EȍMEč>E뵍E띍EЉD$ 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-C6?$@@UE ]EE@ݝ@ݝݝ@ݝDž|Džx E$EݝEE;E ~!MEEݜ͸EEE;E~E\$@$7ݝp݅ݝDž||;x|ۅ|܍pEݝMEE܅ݜ͸$Eܥ]؋MEEܥݜ͸$Eܥ]EEܵܵ@ݝ݅ܵ@Ew"݅ܵ@Ewx|݅ܵEw݅ܵEw@x|E@ٽnnf fl٭l]٭n:݅ܵEw݅ܵEw ݅ݝ|@EEE݅݅Ív@@UVSpEEE$E]Dž~~EE;E~!MEEݜͨEՋuE ]EE ۅ ݜuE ]EE ۅ ݜ$Ee]uE ]EE ۅ ݜuE ]EE ۅ ݜ$Ee]؋uE ]EE ۅ ݜuE ]EE ۅ ݜ$Ee]ЋuE ]EE ۅ ݜuE ]EE ۅ ݜ$Ee]EeeE@EE 4ۅɋEE 4ۅ@]tEp[^]ÐSingular matrix in routine ludcmp#B ;UWVSLE D$$.EEEE;E ~]EE;E ~@EME]EEwE]؍EEzt $@6EEuE`EE;E ~cEE;E|EME]EE;E|IE UE4< EME7 E]ȍE뭋EMEEEO]؋EEE;E ~EME]EE;E|IE UE4< EME7 E]ȍE뭋EMEEEEEUEs E]؋EEEE;EEE;E ~EME]ЋE UE<4 EME>EMEEEoUEEMEEE UE EMEzt#EME@E;E twEME4]ЋE@EE;E ~DE UE4< EMEM7E벍EE D$D$E$1L[^_]UWVSEEE;E ~EEEEE]؋EMEE}tMEEEH9E~TE UE EE E]؍EEuzEEEEEE%E E}EE]؋E@EE;E ~UWVSl]]ED$$豊EED$ E D$ED$$褍ED$@$ED$$rD$'q@$E}uAD$$ @-D$D$ @oB$^$BED$E D$PBED$ D$PBED$$膎EE`BE=`BEE,EEE;E~oEE؍ U,E; ~DEEPBE9E~jEPBE9E~ME EE9E~3E UE܍ EEE뤍EEE;E~@E EE9E~&EMEE͍E]]EE;E ~E=`BEE;`B~EȍE$  jBE EȍE$4}(EЍ jBEȍ7uzEEL}`BEE;PB~E U4E4< EM0E`@7]EMEzt)EUE E@P$d$ EMEzt+EUE EP$d$ EME<6EME;EEME4}EMEٽf f٭۝٭47EME4}EME٭۝٭ 7E`uBE;PB EMEuEEHE<4EMEٽf f٭۝٭<>EMEuEEHE<4EME٭۝٭ >E`uBEMEuEEHE E xEMEuEEHE E HE`uBEMEwIEUE EP$d$ wEE]E]EE=`BD$H@E$裭EE;`B~|EȍE$4}(EЍ jBEȍ7D$ EȍE$D$D$_@E$EwD$g@E$EE;E~(ED$ ED$D$t@E$άED$k.@E$贬E EE9E~-E9EuD$@oB$~5}uE$@ED$D$@oB$GEE;E~EE܍EPBE9E~eE܍4}EUE܍ UE E 7E댍E\EE;E~E]}~7E܍ UE EE]EE܍Eh@s}uMEp@ɋE܍E4\$E܉D$E܍E\$E܉D$$@vEp@ɋE܍E4\$E܉D$E܍E\$ E܉D$D$@oB$pk}u.E܉D$E܍E\$E܉D$$΋@E܉D$E܍E\$ E܉D$D$΋@oB$EdEE;E~EE܍EPBE9E~eE܍4}EUE܍ UE E 7E댍E\E]]E;E~@E܍EE]E܍MEE]EEE;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;EEx@shE\$E܍ME\$E܍MEu\$ ED$D$@E$,BE\$E܍ME\$ ED$D$/@E$EEPBE9E~EPBE9E~E܍ UE Euz}uHE܍ UE E\$ ED$E܉D$$B@ҥE܍ UE E\$ED$ E܉D$D$B@oB$ѥE EE;ED$k.@E$袥}u $M@@D$k.@oB${EEEEu `BE$膤ED$E D$PBED$D$ PBED$D$E$要ED$D$E$yED$E D$ ED$D$E$}l[^_]ÉError on individual =%d agev[m][i]=%f m=%d t&(@h㈵>UWVSE ]E]E@]EH]Eٽvvf ft٭t۝|٭vE٭t۝x٭vxD$ |D$E$D$${EE`BE=`BEE4EE;E~_EE U4E; ~4EEE;E$~F|Eȋx9E~&E̍MEȍEʍEEE;E(~E=`BEE;`B~EaB  jBEl hEaB4}0E jBE7lhuzEEH}EPEȋE;ET~Eȍ U

Computing probabilities of dying over estepm months as a weighted average (i.e global mortality independent of initial healh state)


%s
# 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
File (multiple files are possible if covariates are present): %s varmuptjgr
Probability is computed over estepm=%d months.

set out "%s%s.png";replot; @`@(@?@UWVS<E$]E,]E@]Ѓ}Xu6}\tD$@($t.D$@($tD$@($t}\D$D$D$ D$D$$MTE\D$TD$E\$ E\$ppB$tt/E\D$D$@oB$tE\D$$@sD$$@$tEHD$D$.@L$bsLD$$zs(D$$bsD$PsB$LsD$'q@$rnB=nBu5D$$)q@sD$D$)q@oB$3sD$$@@rD$D$@@oB$rELD$D$@nB$rEHD$D$&@nB$rE@EPBE9E~eED$D$4@nB$rEE;E~1ED$ED$ ED$D$>@nB$YrEōED$k.@nB$6rD$L@ tB$!rD$`@gB$ r(D$D$@gB$qPBED$ E@D$PBED$E@$IxD$ @PfB$qD$@PfB$qEE;E~BEE;E~*ED$ ED$D$@PfB$LqE̍ED$k.@PfB$)qPBD$$EEPBD$ D$ED$$HE̋ED$ D$ED$$HEȡPBD$ D$PBED$E@$`HEġPBED$ E@D$PBED$E@$1HEPBED$ E@D$PBD$$HEPBED$E@$DEPBED$E@$D|PBD$ D$PBED$E@$GEEL;E }E D$ELD$$ɶ@goELEUE Ћ9Ex@ݝ`Eݝh݅hEs݅`ܥh@E @$fnٽf f٭]٭UMЋ9EED$D$PBED$ D$PBED$$HtED$D$PBD$ D$ED$$GEED$ D$ED$$FEED$ D$ED$$EEDžPP;PB~S EE;PB~EUEEݝE;Pu!PE݅ݝx݅ݝx݅xEjEHD$(E8D$$E4D$ E D$ED$ED$ED$݅h\$ED$t$mEHD$$E\$E8D$E4D$݅h\$ ED$ED$E<$ob}X@}\EE;E~"E UP릍E놋EE P$d$@ݝXEE;E~EE;E~fE U E ݅hٽf f٭۝٭E됍ErEE;E~EE;E~ED$ PBD$D$PBD$D$ED$ D$EED$Ẻ$_`EED$ ED$D$PBD$D$ED$ D$ẺD$Eȉ$ `EE;E~ EE;E~E U E ݅hٽf f٭۝٭E U E ݅h٭۝٭4<EMȋE܍X܍X7EEE EED$ PBD$D$PBD$D$PBED$ E@D$ED$Eĉ$^ED$ PBED$E@D$PBD$D$PBED$ E@D$EĉD$E$8^E@EPBE9E~aE@EPBE9E~DE xE<4 EME>E뭍E됋EHD$(E8D$$E4D$ E D$ED$ED$ED$݅h\$ED$t$^EHD$$E\$E8D$E4D$݅h\$ ED$ED$E<$yS}X:}\Eٽf fE;E~E UEEٽf fE;E~E UEٽf fE@EPBE9E~EE|E;E~E |E|E UD$D$D$D$ D$D$T$1nB$>T tB$TgB$T<[^_]É'# Standard deviation of stable prevalences %1d-%1d %.5f (%.5f)t&@`@(@?@UWVSE$]E,]E@]D$@hB$5TD$@hB$ TEE;E~*EȉD$ EȉD$D$ @hB$SED$k.@hB$SPBD$$e(EPBD$ D$ED$$X+EЋED$ D$ED$$3+EE UE LЋL9LLE(@]E]EEsEe0@E 8@$+R}Ef fEm]mE 0@sEUMLЋL9LLEED$ D$PBD$$B*EED$$'EED$$&EEE;PB~EE;PB~zEEU|EȍEݝpE;EuEE݅pݝh݅pݝh݅hE|EyEHD$$E\$E8D$E4D$E\$ ED$ED$E<$FEE;E~4Eȍ<uEȍMEEE;PB~EdE`EȍEݝXE;EuEE݅X$ݝP݅XݝP݅Pd`EpEHD$$E\$E8D$E4D$E\$ ED$ED$E<$EEE;E~4Eȍ<uEȍMEEE;E~\E UEȍ4< EȍMEȍE$@@EE47E뚍ERPBD$ D$ED$$:'EEE;E~\EE;PB~AEč UE<4 EMEč>E벍EEE;E~AEȍ ] E}Ef fEm]mE E뵋ED$ PBD$D$PBD$D$ED$ D$ED$EЉ$MED$ ED$D$PBD$D$ED$ D$EЉD$Ẻ$kMEE;E~\EȍM E}Ef fEm]mE<4EȍM̋Eȍ>EE\$D$@hB$MEE;E~Eȍ ] E}Ef fEm]mE $eL\$EȍM

Computing and drawing one step probabilities with their confidence intervals

  • Matrix of variance-covariance of pairs of step probabilities (drawings)

    • Matrix of variance-covariance of pairs of step probabilities

    file %s
    v' Ellipsoids of confidence centered on point (pij, pkl) are estimatedand drawn. It helps understanding how is the covariance between two incidences. They are expressed in year-1 in order to be less dependent of stepm.

    Contour plot corresponding to x'cov-1x = 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.
    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.
    To be simple, these graphs help to understand the significativity of each parameter in relation to a second other one.
    ********** # V%d=%d
    ********** Variable **********
    %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 labelv 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 0.65,0.65varpijgr
    Ellipsoids of confidence cov(p%1d%1d,p%1d%1d) expressed in year-1 :%s%d%1d%1d-%1d%1d.png,

    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;&@(@@UWVSE]E$]EEEpAݝD$@$>D$PsB$>D$'q@$=pdB=pdBu5D$$)q@9>D$D$)q@oB$j>D$@$t>D$PsB$=D$'q@$x=uB=uBu5D$$)q@=D$D$)q@oB$=D$@$=D$PsB$y=D$'q@$<fB=fBu5D$$)q@/=D$D$)q@oB$`=D$$@<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$C<EE;E~EPBE9E~pEЉD$ EԉD$D$@pdB$;EЉD$ EԉD$D$@uB$;EЉD$ EԉD$D$@fB$;E끍EcPBD$$4$PBD$ D$PBEED$$,PBEED$ D$PBEED$$(Eٽf f٭۝٭D$ E٭۝٭D$PBEED$$iEٽf f٭۝٭D$E٭۝٭D$PBEED$ D$PBEED$$ED$@ tB$9D$@gB$9D$k.@gB$9D$iBD$@gB$9D$iBD$@PjB$9D$`@PjB$z9D$`@PjB$e9ݝ`BE=`BEE8EEE;E~ EE U8E; ~E=`BD$H@pdB$8EE;`B~EaB4}4E jBE7D$ EaBD$D$_@pdB$G8EnD$@pdB$(8D$H@uB$8EE;`B~EaB4}4E jBE7D$ EaBD$D$_@uB$7EnD$@uB$e7D$H@ tB$P7EE;`B~EaB4}4E jBE7D$ EaBD$D$@ tB$6EnD$@ tB$6D$ @PjB$6EE;`B~EaB4}4E jBE7D$ EaBD$D$_@gB$5EnD$`@PjB$5D$H@fB$5EE;`B~EaB4}4E jBE7D$ EaBD$D$_@fB$;5EnD$g@fB$5Eݝ݅Es|݅ݝEE;`B~qUEE04}4E jBEE0 7ݜEEE; `B~?EaB EaB݄܍ݜEEE;dB~EdBEsB4}4E, jBEsB 47EsB4}4E, jBEsB  73 P$d$ݜEPBEED$ D$PBD$$ PBD$ D$PBEED$$W PBEED$$ PBEED$$Dž;PB~EE;PB~E$EԍEݝE;u!E݅ݝ݅ݝ݅EgED$$D$ @uBD$D$ sB$,EEE;E~\EPBE9E~?EE< Eԍ sBEЍ>E벍EEE;PB~E$EԍEݝE;u!E݅$ݝ݅ݝ݅EgED$$D$ @uBD$D$ sB$+EEE;E~\EPBE9E~?EE<Eԍ sBEЍ>E벍EEPBEE9E~k Eԍ4< Eԍ Eԍ$pAE47E낍EPBEE9E~tDž;PB~PEЍ <4 EЍ>렍EyE D$ PBD$D$PBD$D$PBEED$ D$D$,$r-D$ PBEED$D$PBD$D$PBEED$ D$,D$($-PBU¡PBE‰D$D$ $PBU¡PBE‰D$D$$WPBD$D$ PBU¡PBE‰D$D$$"PBD$D$ PBU¡PBE‰D$D$$ED$ED$ @uBD$D$ sB$;(EEٽf fE;E~EPBE9E~gEE ݅٭۝٭<4 Eԍ sBEЍ>E늍ElEPBEE9E~EPBEE9E~rEԍ EЍ ݅٭۝٭<4Eԍ(EЍ>E{ET݅٭۝٭D$D$@pdB$+݅ٽf f٭۝٭D$D$@uB$*݅ٽf f٭۝٭D$D$@fB$x*EPBEE9E~Eԍ Eԍ ݅ٽf f٭۝٭$)\$Eԍ ݅ٽf f٭۝٭ \$D$@pdB$)EEPBEE9E~Eԍ ݅ٽf f٭۝٭ \$D$@uB$(Eԍ ݅ٽf f٭۝٭ \$D$@fB$(EEEE;E~EPBE9E~EԉEԍEED$ED$ ݅ٽf f٭۝٭D$D$@uB$'ED$ED$ ݅ٽf f٭۝٭D$D$@fB$'EE;E~Eԍ EЍ ݅ٽf f٭۝٭\$D$@uB$&Eԍ EЍ ݅ٽf f٭۝٭4<Eԍ Eԍ ݅٭۝٭$g%7ݝEЍ EЍ ݅ٽf f٭۝٭$$ܽ\$D$@fB$%E1EIE+݅ݝoEEE;E~5 EPBE9E~ E;Eu UJPBEEEEE;E~ EPBE9E~ E;Eu UJPBEEĉEԋE;Ev Eݝ݅Es ݅ٽf f٭۝٭gfff)‰)ȅ Eԍ Eԍ ݅٭۝٭PBxAPBxAݝHEЍ EЍ ݅٭۝٭PBxAPBxAݝ@Eԍ EЍ ݅٭۝٭PBxAPBxA]Eԍ݅٭۝٭PBxA]EЍ݅٭۝٭PBxA]݅H܍@$!Eݝ8݅H܅@ݝ݅H܅@݅H܅@݅H܍@EMA$%!݅pAݝx݅H܅@ݝ݅H܅@݅H܅@݅H܍@EMA$ ݅pAݝp݅Hܥx݅Hܥxuu${ ݝX݅xܥHu܍Xݝ`݅`ݝh݅XݝP݅`ܵXݝ0}E݅0\$h݅`\$`݅X\$X݅p\$P݅x\$HE\$@݅8\$8݅@\$0݅H\$(E\$ E\$ED$ED$EĉD$ EȉD$݅ٽf f٭۝٭D$$@݅0\$l݅`\$d݅X\$\݅p\$T݅x\$LE\$D݅8\$<݅@\$4݅H\$,E\$$E\$ED$ED$EĉD$EȉD$ ݅ٽf f٭۝٭D$D$@@oB$b}ED$@ tB$<ED$ED$EĉD$ EȉD$D$@ tB$ D$ A tB$D$FAE$AD$FAE$,‹ED$DED$@EĉD$

  • Result files (first order: no variance)

    - Observed prevalence in each state (during the period defined between %.lf/%.lf/%.lf and %.lf/%.lf/%.lf): %s
    pij - Estimated transition probabilities over %d (stepm) months: %s
    plt& - Stable prevalence in each health state: %s
    e - Life expectancies by age and initial health status (estepm=%2d months): %s
    • Graphs

      • ************ Results for covariates ************
      pe
      - Pij or Conditional probabilities to be observed in state j being in state i, %d (stepm) months before: %s%d1.png

      - 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

      - Stable prevalence in each health state : p%s%d%d.png
      exp
      - Health life expectancies by age and initial health state (%d): %s%d%d.png
    '

  • Result files (second order: variances)

    - Parameter file with estimated parameters and covariance matrix: %s
    ' - Variance of one-step probabilities: %s
    - Variance-covariance of one-step probabilities: %s
    - Correlation matrix of one-step probabilities: %s
    v - Variances and covariances of life expectancies by age and initial health status (estepm=%d months): %s
    t - Health expectancies with their variances (no covariance): %s
    vplt& - Standard deviation of stable prevalences: %s
    • Graphs


      • - Observed (cross-sectional) and period (incidence based) prevalence (with 95%% confidence interval) in state (%d): %s%d%d.png

      - Total life expectancy by age and health expectancies in states (1) and (2): %s%d.png
      UWVSE4]EH]EP]EX]E`]Eh]Ep]D$@E$D$@E$\$ED$m0AgB$ EA tB$mD$73AE$‹HD$ HD$T$E\$E\$D$@>A tB$Dž;PB~D;uD$>A tB$D$>A tB$D$73AE$‹HD$HD$ T$D$>A tB$yDž;PB~D;uD$>A tB$<D$>A tB$%D$73AE$f‹HD$HD$ T$D$@?A tB$Dž;PB~D;uD$>A tB$D$>A tB$D$@E$‹D$HD$HD$ T$D$?A tB$%&Dž;~D$,AE $9‹D$ T$D$?A tB$E\$E\$D$?A tB$DžPB@9~D$2AE$‹HD$HD$ T$D$@@A tB$$DžPB@9~D;uD$>A tB$D$>A tB$묃uD$a@A tB$ HD$D$p@A tB$D$2AE$‹HD$HD$ T$D$@A tB$BDžPB@9~D;uD$>A tB$D$>A tB$D$@A tB$D$2AE$‹HD$HD$ T$D$@A tB$DžPB@9~D;uD$>A tB$ND$>A tB$7묡PB@9uD$ AA tB$ D$@A tB$]Dž;~Dž;PB~bPBD$/AE $‹D$D$ T$D$=A tB$BD$,AE$‹D$(D$$HD$ HD$T$E\$E\$D$ AA tB$Dž;PB|xD$,AE$ ‹@D$D$D$HD$HD$ T$D$AA tB$WxaDž;~XDž;PB~.DžD$@E $7‹D$D$ T$D$=A tB$D$+AE$‹@D$$@D$ D$T$E\$E\$D$AA tB$HDž;PB|0@D$D$QBA tB$@D$ D$D$VBA tB$PBPB|D$+AE$‹|@D$|@D$D$ T$D$BA tB$eDž;PB|KPBPB||@D$D$QBA tB$ 륋@D$ @D$D$BA tB$DžDž;PB~DžPBPB9~;twDž;@uB~]E,\$ D$D$BA tB$D$k.@ tB$듍V,Džxx~Dž;~D$-AE $‹xD$D$ T$D$=A tB$\xuD$BA tB$>D$CA tB$'E\$E\$D$@CA tB$DžDž;PB~DžPBPB9~;xu>@D$D$PBCA\$D$CA tB$X*@D$ D$D$CA tB$,DžDž;@uB~UaB; ; `BaB4=0gB jBaB 7D$ HD$D$CA tB$C|aB50gB jB D$ HD$D$CA tB$D$CA tB$Dž;PB~H@uB@D$ H@uBD$D$CA tB$8DžDž;@uB~taB; ; `BaB4=0gB jBaB 7D$ H@uBHD$D$CA tB$AaB50gB jB D$ H@uBHD$D$CA tB$yD$CA tB$D$ D$D$CA tB$R¡PBPB9tD$CA tB$@uB<8x tB$oļ[^_]UWVSTE ]E]E`BEЃ=`BE} t} t} t } t} u EE EE]EEsEE;PB~EE;E~E}Ef fEm]mE UE܍ Eԍ<4Em]mE UE܍ Eԍ>EnEME]%EE;E~UJP$d$E]UJP$d$EEsEE;PB~EE;E~tE}Ef fEm]mE UE܍ Eԍ<4Em]mE UE܍ Eԍ>EUJ9E~\Em]mE UE܍ EE<Em]mE UE܍ EE4Em]mE+E؍ UE܍ EԍMEEm]mE UE܍ EE<Em]mE UE܍ EE4Em]mEE؍ UE܍ EԍMEEEm]mE UE܍ Eԍ4<Em]mE UE܍ EԍE7EE^E]EȃEEET[^_]f&Problem with forecast resultfile: %s Computing forecasting: result on file '%s' # Mean day of interviews %.lf/%.lf/%.lf (%.2f) between %.2f and %.2f #****** Routine prevforecast ** #****** V%d=%d, hpijx=probability over h years, hp.jx is weighted by observed prev ****** t&'# Covariate valuofcovar yearproj age p%d%d p.%d&' # Forecasting at date %.lf/%.lf/%.lf %d %d %.f %.f %.3fv@`@(@>@?UWVSE ]E]E]E$]E,]E4]E<]EH]EP]E`]\AݝXE\D$LEXD$HE\$@E\$8`fBD$4pBD$0`pBD$,0gBD$(aBD$$0uBD$ PBD$nBD$`dBD$E\$ E\$ppB$@D$wZAx$>ED$x$D$'q@x$CbB=bBu5xD$$ZAxD$D$ZAoB$xD$$ZAJxD$D$ZAoB${}luEl`pB}DD$D$D$ D$D$$蒽EDD$D$E\$ E\$ppB$+t/EDD$D$@oB$EDD$$@lPB\Aٽvvf ft٭t۝p٭vpP$d$\A٭t]٭v=PB EoB;PB} PBD$oBD$$ɶ@oBEE=PBEHD$ `B$ݝ@݅Hݝ HD$݅@\A$ݝ8݅Hݝ(HD$݅8\A$ݝ@݅Hݝ0݅0ztݝ0݅(ztݝ0Ell=`B DžlE\$0E\$( `B\$ ݅ \$݅(\$݅0\$D$[AbB$D$`[AbB$nBEnB;l~DžxEl `pBx; ~bED$[AbB$^EE;El~EaB4=0gBE jBE7D$ EaBD$D$[AbB$EnD$[AbB$D$\AbB$EPBPB9E~mDžtt;PB~0ED$ tD$D$%\AbB$BtED$D$,\AbB$EEEEesD$k.@bB$EE\$E\$E\$D$@\AbB$Eݝ`E݅`se݅Xܥ`\APB\A$ٽvvf ft٭t]٭vUMdЋd9ddEED$D$PBPBD$ D$PBPBD$$LpfBgBnBoBED$(oBD$$gBD$ PBD$PBD$EhD$ED$݅`\$ED$$Džpp;E~pEP$d$\APBEztD$k.@bB$EE;El~EaB4=0gBE jBE7D$ EaBD$D$g\AbB$vEnpEP$d$\APB܅`\$EE\$D$n\AbB$!EPBPB9E~hݝPDžtt;PB~}Dt E p4<݅`ٽvvf ft٭t۝p٭vp t x7 ݅PݝPt E p4<݅`ٽvvf ft٭t۝p٭vp ppBt x7 ݅PݝPpEP$d$\APBEztLt E p\$D$w\AbB$tpEP$d$\APBEzt݅P\$D$w\AbB$Ep1ED$D$PBPBD$D$ PBPBD$D$$݅`ݝ`UEx~nBW}Dt>D$D$D$D$ D$D$$kbB$[^_]poprProblem with population file : %s %d %lf P.%d [Population] # Forecasting at date %.lf/%.lf/%.lf %3.f %15.2f@`@(@v@?UWVSE ]E]E]E$]E,]E4]E<]EH]EP]E`]D$D$D$ D$D$$8D$D$D$ D$D$$賱4iAݝXEiAEEiA% `BiAݝ`PBD$L`BD$HE\$@E\$8`fBD$4pBD$0`pBD$,0gBD$(aBD$$0uBD$ PBD$nBD$`dBD$E\$ E\$ppB$3D$iA$ED$$ D$'q@$ qB= qBu5D$$ZAD$D$ZAoB$D$$ZAD$D$ZAoB$=`Bu`B`pB}DD$D$D$ D$D$$ί0EDD$0D$E\$ E\$ppB$gt/EDD$D$@oB$ EDD$$@PBiAٽf f٭۝٭P$d$iA٭]٭=PB EiAݝXEE=PBE}XYD$iAE\$qB=qBuE\D$$ iA$ D$$2lD$$虩DD$$@Džtt@D$ tlD$D$CiAqB$||u t몋t0uBDžtt;0uB|FtlDt@tnBEnB;El~2Džx`B `pBx; ~ ED$[A qB$EE;`B~EaB4=0gBE jBE7D$ EaBD$D$@ qB$ EkD$[A qB$D$@ qB$EPBPB9E~#ED$D$KiA qB$E˃}XuD$QiA qB$E}~EE\$E\$E\$D$`iA qB$B݅`ٽf f٭۝٭*)‰)P$d$iAE]݅`٭۝٭*)‰)P$d$iAEEs݅XeiAPBiA$ٽf f٭]٭UMЋ9EED$D$PBPBD$ D$PBPBD$$2 ݅PݝPt pEP$d$iAPBE\$D$iAbB$EPBPB9E~?ݝPݝHDžtٽf ft;PB~t ݅PݝPtVUP$d$܅`٭۝٭9pu݅P\$D$iAbB$]EpED$D$PBPBD$D$ PBPBD$D$<$蟢E]ٽf fEIxnB}Dt>D$D$D$D$ D$D$0$}XuZD$D$l$踚D$D$D$D$D$@$D$D$D$D$ D$D$8$tD$D$D$D$ D$D$4$6 qB$[^_]aProblem with file: %s UD$6~AE $E}u8E D$$8~AE D$D$8~AoB$EE$EE# Parameters nlstate*nlstate*ncov a12*1 + b12 * age + ... %1d%1d 0.# Scales (for hessian or gradient estimation) # Covariance matrix #%1d%1d%d%1d%1d%d Cov(%s%1d%1d,%s%1d%1d) Var(%s%1d%1d)U$~AD$~AE$EE;E ~DžtEEE 9E~E;EutED$ED$$~A6ED$ ED$D$~AE$eEE;E~&$AD$AE$5E$k.@D$k.@E$E@E$ AD$ AE$EE HE EpEE;E ~DžtEEE 9E~E;EutED$ ED$D$~AE$\ED$ED$$~AB $EE;E~&$AD$AE$EЍh$k.@D$k.@E$E(E$OAdB $DD$OAE$Džll~(DžtEE;E ~EEE 9E~E;EuEE;E~tE`EEluKED$ ED$ED$$dA蔿ED$ED$ ED$D$dAE$輿IED$ ED$ED$$nAIED$ED$ ED$D$nAE$qDžxEE;E ~EEE 9E~E;EuxDž||;E~^xx;t7|`EEx;tluxED$ED$ED$ED$ ED$E؉D$$wAJED$ED$ED$ED$ED$ E؉D$D$wAE$]$AD$AE$9sluKED$ ED$E؉D$$A载ED$ED$ E؉D$D$AE$$A臽D$AE$Ľ|EaEC$k.@GD$k.@E$脽hEBEElÍ&(@.@Uh]]]EE0uBH9E~3Eԍ`uBE]E]E]؍EE0uBH9E~EԍtB<ȋEԍpqB<!ȅEE0]ȋEhAEԍuBhAɋPBP$d$$x]EhAEԍjBhAɋPBP$d$$(mM]EԍtB<ȋEԍpqB<!ȅ8EE0]EhAEԍtBhAɋPBP$d$$聻]EhAEԍjBhAɋPBP$d$$1mE]EhA$EEhAEԍtBhAɋPBP$d$]hA$豹E]EԍpqB<ȋEԍuBpA!ȅtEԍ`uBE E]EExAMuÐ

      • Result files

        Force of mortality. Parameters of the Gompertz fit (with confidence interval in brackets):
        mu(age) =%lf*exp(%lf*(age-%d)) per year

        p[%d] = %lf [%f ; %f]


        UWVS (nlstate+ndeath)=(%d+%d)=%d Total number of individuals= %d, Agemin = %.2f, Agemax= %.2f -mort.gpProblem with file %s # %s # %s set missing 'NaNq' .htmProblem with %s -cov.htm IMaCh Cov %s %s
        %s
        Title=%s
        Datafile=%s Firstpass=%d Lastpass=%d Stepm=%d Weight=%d Model=%s
        ' IMaCh %s %s
        %s
        Title=%s
        Datafile=%s Firstpass=%d Lastpass=%d Stepm=%d Weight=%d Model=%s

        • Parameter files

          - Copy of the parameter file: o%s
          - Log file of the run: %s
          - Gnuplot file name: %s
          - Date and time at start: %s

        Total number of observations=%d
        Youngest age at first (selected) pass %.2f, oldest age %.2f
        Interval (in months) between two waves: Min=%d Max=%d Mean=%.2lf
        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=%12.6ftitle=%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 vprevforecast=%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 %.5ft&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.fProblem 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 %sLocal time at start %s Local time at end %s Total time used %s Total time was %d Sec. t&
        Local time at start %s
        Local time at end %s
        "gnuplot Starting graphs with: %s Problem with gnuplot Wait... 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