[Pomp-commits] r451 - pkg/tests
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue May 3 17:42:19 CEST 2011
Author: kingaa
Date: 2011-05-03 17:42:18 +0200 (Tue, 03 May 2011)
New Revision: 451
Added:
pkg/tests/filtfail.R
pkg/tests/filtfail.Rout.save
pkg/tests/sir-icfit.R
pkg/tests/sir-icfit.Rout.save
Log:
- two new tests: 'sir-icfit' and 'filtfail'
Added: pkg/tests/filtfail.R
===================================================================
--- pkg/tests/filtfail.R (rev 0)
+++ pkg/tests/filtfail.R 2011-05-03 15:42:18 UTC (rev 451)
@@ -0,0 +1,90 @@
+library(pomp)
+
+set.seed(834454394L)
+
+### the following example tests to make sure that states are updated properly
+### upon filtering failures
+
+"time,admissions,discharges,patients,cases
+0,4,2,8,
+1,0,1,10,2
+2,2,0,9,1
+3,1,4,11,2
+4,6,8,8,8
+5,4,1,6,0
+6,2,3,9,1
+7,4,2,8,1
+8,1,2,10,1
+9,3,2,9,1
+10,2,3,10,1
+11,3,2,9,2
+12,1,3,10,0
+13,1,3,8,2
+14,2,3,6,1
+15,1,4,5,2
+16,6,2,2,2
+17,2,1,6,2
+18,4,0,7,1
+19,0,0,11,0
+20,1,4,11,
+" -> csvtext
+
+tc <- textConnection(csvtext)
+records <- read.csv(tc)
+close(tc)
+
+po <- pomp(
+ data=subset(records[c("time","cases")],!is.na(cases)),
+ times="time",
+ t0=records$time[1],
+ rprocess=euler.sim(
+ step.fun=function(x, t, params, delta.t, covars, ...) {
+ with(
+ as.list(c(x,params,covars)),
+ {
+ if (S+I!=patients) {
+ print(c(t,S,I,patients))
+ stop("assumption violation")
+ }
+ ## number of infected (resp. susceptible) admissions
+ iadm <- rbinom(n=1,size=admissions,prob=p)
+ sadm <- admissions-iadm
+ ## number of infected (resp. susceptible) discharges
+ idis <- rhyper(nn=1,m=I,n=S,k=discharges)
+ sdis <- discharges-idis
+ ## number of in-hospital infections
+ infections <- rbinom(n=1,size=S+sadm-sdis,prob=1-exp(-beta*(I+iadm-idis)))
+ c(
+ I=unname(I+infections-idis+iadm),
+ S=unname(S-infections-sdis+sadm)
+ )
+ }
+ )
+ },
+ delta.t=1
+ ),
+ rmeasure=function(x, t, params, covars, ...){
+ with(
+ as.list(c(x,params,covars)),
+ rbinom(1,size=I,prob=rho)
+ )
+ },
+ dmeasure=function(y, x, t, params, log, covars, ...){
+ with(
+ as.list(c(y,x,params,covars)),
+ dbinom(cases,size=I,prob=rho,log=log)
+ )
+ },
+ covar=records,
+ tcovar="time"
+ )
+
+simpo <- simulate(po,params=c(p=0.05,rho=0.5,beta=0.1,S.0=6,I.0=2))
+
+pf <- pfilter(
+ po,
+ params=c(p=0.05,rho=0.5,beta=0.01,S.0=6,I.0=2),
+ Np=10,
+ max.fail=20,
+ verbose=TRUE
+ )
Added: pkg/tests/filtfail.Rout.save
===================================================================
--- pkg/tests/filtfail.Rout.save (rev 0)
+++ pkg/tests/filtfail.Rout.save 2011-05-03 15:42:18 UTC (rev 451)
@@ -0,0 +1,120 @@
+
+R version 2.12.1 (2010-12-16)
+Copyright (C) 2010 The R Foundation for Statistical Computing
+ISBN 3-900051-07-0
+Platform: x86_64-unknown-linux-gnu (64-bit)
+
+R is free software and comes with ABSOLUTELY NO WARRANTY.
+You are welcome to redistribute it under certain conditions.
+Type 'license()' or 'licence()' for distribution details.
+
+R is a collaborative project with many contributors.
+Type 'contributors()' for more information and
+'citation()' on how to cite R or R packages in publications.
+
+Type 'demo()' for some demos, 'help()' for on-line help, or
+'help.start()' for an HTML browser interface to help.
+Type 'q()' to quit R.
+
+> library(pomp)
+Loading required package: mvtnorm
+Loading required package: subplex
+Loading required package: deSolve
+>
+> set.seed(834454394L)
+>
+> ### the following example tests to make sure that states are updated properly
+> ### upon filtering failures
+>
+> "time,admissions,discharges,patients,cases
++ 0,4,2,8,
++ 1,0,1,10,2
++ 2,2,0,9,1
++ 3,1,4,11,2
++ 4,6,8,8,8
++ 5,4,1,6,0
++ 6,2,3,9,1
++ 7,4,2,8,1
++ 8,1,2,10,1
++ 9,3,2,9,1
++ 10,2,3,10,1
++ 11,3,2,9,2
++ 12,1,3,10,0
++ 13,1,3,8,2
++ 14,2,3,6,1
++ 15,1,4,5,2
++ 16,6,2,2,2
++ 17,2,1,6,2
++ 18,4,0,7,1
++ 19,0,0,11,0
++ 20,1,4,11,
++ " -> csvtext
+>
+> tc <- textConnection(csvtext)
+> records <- read.csv(tc)
+> close(tc)
+>
+> po <- pomp(
++ data=subset(records[c("time","cases")],!is.na(cases)),
++ times="time",
++ t0=records$time[1],
++ rprocess=euler.sim(
++ step.fun=function(x, t, params, delta.t, covars, ...) {
++ with(
++ as.list(c(x,params,covars)),
++ {
++ if (S+I!=patients) {
++ print(c(t,S,I,patients))
++ stop("assumption violation")
++ }
++ ## number of infected (resp. susceptible) admissions
++ iadm <- rbinom(n=1,size=admissions,prob=p)
++ sadm <- admissions-iadm
++ ## number of infected (resp. susceptible) discharges
++ idis <- rhyper(nn=1,m=I,n=S,k=discharges)
++ sdis <- discharges-idis
++ ## number of in-hospital infections
++ infections <- rbinom(n=1,size=S+sadm-sdis,prob=1-exp(-beta*(I+iadm-idis)))
++ c(
++ I=unname(I+infections-idis+iadm),
++ S=unname(S-infections-sdis+sadm)
++ )
++ }
++ )
++ },
++ delta.t=1
++ ),
++ rmeasure=function(x, t, params, covars, ...){
++ with(
++ as.list(c(x,params,covars)),
++ rbinom(1,size=I,prob=rho)
++ )
++ },
++ dmeasure=function(y, x, t, params, log, covars, ...){
++ with(
++ as.list(c(y,x,params,covars)),
++ dbinom(cases,size=I,prob=rho,log=log)
++ )
++ },
++ covar=records,
++ tcovar="time"
++ )
+>
+> simpo <- simulate(po,params=c(p=0.05,rho=0.5,beta=0.1,S.0=6,I.0=2))
+>
+> pf <- pfilter(
++ po,
++ params=c(p=0.05,rho=0.5,beta=0.01,S.0=6,I.0=2),
++ Np=10,
++ max.fail=20,
++ verbose=TRUE
++ )
+filtering failure at time t = 4
+pfilter timestep 4 of 19 finished
+pfilter timestep 9 of 19 finished
+pfilter timestep 14 of 19 finished
+filtering failure at time t = 16
+filtering failure at time t = 17
+filtering failure at time t = 18
+pfilter timestep 19 of 19 finished
+>
Added: pkg/tests/sir-icfit.R
===================================================================
--- pkg/tests/sir-icfit.R (rev 0)
+++ pkg/tests/sir-icfit.R 2011-05-03 15:42:18 UTC (rev 451)
@@ -0,0 +1,110 @@
+library(pomp)
+
+set.seed(343435488L)
+
+pdf(file="sir-icfit.pdf")
+
+data(euler.sir)
+po <- window(euler.sir,end=0.25)
+guess <- coef(po)
+ics <- c("S.0","I.0","R.0")
+guess[ics[-3]] <- guess[ics[-3]]+c(0.5,-0.3)
+
+plist <- list(
+ probe.marginal("reports",ref=obs(po),order=3,diff=1,transform=sqrt),
+ probe.acf("reports",lags=c(0,1,2,3,4,5),transform=sqrt),
+ median=probe.median("reports")
+ )
+
+summary(pm.true <- probe(po,probes=plist,nsim=100,seed=1066L))
+
+summary(pm.guess <- probe(po,params=guess,probes=plist,nsim=100,seed=1066L))
+
+pm.fit <- probe.match(
+ po,
+ start=guess,
+ probes=plist,
+ est=ics[-1],
+ method="Nelder-Mead",
+ trace=3,
+ reltol=1e-5,
+ parscale=c(0.1,0.1),
+ nsim=100,
+ seed=1066L
+ )
+
+summary(pm.fit)
+
+comp.table <- cbind(true=exp(coef(po,ics)),guess=exp(guess[ics]),fit=exp(coef(pm.fit,ics)))
+comp.table <- apply(comp.table,2,function(x)x/sum(x))
+comp.table <- rbind(
+ comp.table,
+ synth.loglik=c(
+ summary(pm.true)$synth.loglik,
+ summary(pm.guess)$synth.loglik,
+ summary(pm.fit)$synth.loglik
+ )
+ )
+comp.table
+
+x <- sapply(
+ list(true=pm.true,guess=pm.guess,fit=pm.fit),
+ function (x) trajectory(x,times=time(x),t0=timezero(x))["cases",1,]
+ )
+
+plot(range(time(po)),range(c(states(po,"cases"),x)),bty='l',xlab="time",ylab="cases",type='n')
+points(time(po),states(po,"cases"))
+matlines(time(po),x,lty=1,col=c("red","blue","green"))
+legend("topright",lty=1,bty='n',col=c("red","blue","green"),legend=colnames(x))
+
+data(euler.sir)
+po <- window(euler.sir,end=0.25)
+guess <- coef(po)
+ics <- c("S.0","I.0","R.0")
+guess[ics[-3]] <- guess[ics[-3]]+c(0.1,-0.2)
+
+summary(tm.true <- traj.match(po,eval.only=TRUE))
+
+summary(tm.guess <- traj.match(po,start=guess,eval.only=TRUE))
+
+tm.fit <- traj.match(
+ po,
+ start=guess,
+ est=ics[-1],
+ method="sannbox",
+ maxit=300,
+ trace=2,
+ parscale=c(0.1,0.1)
+ )
+
+tm.fit <- traj.match(
+ tm.fit,
+ est=ics[-1],
+ method="Nelder-Mead",
+ trace=3,
+ reltol=1e-8,
+ parscale=c(0.1,0.1)
+ )
+
+summary(tm.fit)
+
+comp.table <- cbind(true=exp(coef(po,ics)),guess=exp(guess[ics]),fit=exp(coef(tm.fit,ics)))
+comp.table <- apply(comp.table,2,function(x)x/sum(x))
+comp.table <- rbind(
+ comp.table,
+ loglik=sapply(list(tm.true,tm.guess,tm.fit),logLik)
+ )
+comp.table
+
+x <- sapply(
+ list(true=tm.true,guess=tm.guess,fit=tm.fit),
+ function (x) trajectory(x,times=time(x),t0=timezero(x))["cases",1,]
+ )
+
+plot(range(time(po)),range(c(states(po,"cases"),x)),bty='l',xlab="time",ylab="cases",type='n')
+points(time(po),states(po,"cases"))
+matlines(time(po),x,lty=1,col=c("red","blue","green"))
+legend("topright",lty=c(NA,1,1,1),pch=c(1,NA,NA,NA),bty='n',col=c("black","red","blue","green"),legend=c("actual",colnames(x)))
+
+dev.off()
+
Added: pkg/tests/sir-icfit.Rout.save
===================================================================
--- pkg/tests/sir-icfit.Rout.save (rev 0)
+++ pkg/tests/sir-icfit.Rout.save 2011-05-03 15:42:18 UTC (rev 451)
@@ -0,0 +1,668 @@
+
+R version 2.11.1 (2010-05-31)
+Copyright (C) 2010 The R Foundation for Statistical Computing
+ISBN 3-900051-07-0
+
+R is free software and comes with ABSOLUTELY NO WARRANTY.
+You are welcome to redistribute it under certain conditions.
+Type 'license()' or 'licence()' for distribution details.
+
+R is a collaborative project with many contributors.
+Type 'contributors()' for more information and
+'citation()' on how to cite R or R packages in publications.
+
+Type 'demo()' for some demos, 'help()' for on-line help, or
+'help.start()' for an HTML browser interface to help.
+Type 'q()' to quit R.
+
+> library(pomp)
+Loading required package: mvtnorm
+Loading required package: subplex
+Loading required package: deSolve
+>
+> set.seed(343435488L)
+>
+> pdf(file="sir-icfit.pdf")
+>
+> data(euler.sir)
+> po <- window(euler.sir,end=0.25)
+> guess <- coef(po)
+> ics <- c("S.0","I.0","R.0")
+> guess[ics[-3]] <- guess[ics[-3]]+c(0.5,-0.3)
+>
+> plist <- list(
++ probe.marginal("reports",ref=obs(po),order=3,diff=1,transform=sqrt),
++ probe.acf("reports",lags=c(0,1,2,3,4,5),transform=sqrt),
++ median=probe.median("reports")
++ )
+>
+> summary(pm.true <- probe(po,probes=plist,nsim=100,seed=1066L))
+$coef
+ gamma mu iota nbasis degree period
+ 3.25809654 -3.91202301 -4.60517019 3.00000000 3.00000000 1.00000000
+ beta1 beta2 beta3 beta.sd pop rho
+ 7.09007684 7.49554194 6.39692966 -6.90775528 14.55744790 -0.51082562
+ S.0 I.0 R.0
+-3.83198030 -6.90775528 -0.02292750
+
+$nsim
+[1] 100
+
+$quantiles
+ marg.1 marg.2 marg.3 acf.0.reports acf.1.reports
+ 0.93 0.97 0.08 0.89 0.87
+acf.2.reports acf.3.reports acf.4.reports acf.5.reports median
+ 0.81 0.78 0.23 0.36 0.35
+
+$pvals
+ marg.1 marg.2 marg.3 acf.0.reports acf.1.reports
+ 0.15841584 0.07920792 0.17821782 0.23762376 0.27722772
+acf.2.reports acf.3.reports acf.4.reports acf.5.reports median
+ 0.39603960 0.45544554 0.47524752 0.73267327 0.71287129
+
+$synth.loglik
+[1] -4.012907
+
+>
+> summary(pm.guess <- probe(po,params=guess,probes=plist,nsim=100,seed=1066L))
+$coef
+ gamma mu iota nbasis degree period
+ 3.25809654 -3.91202301 -4.60517019 3.00000000 3.00000000 1.00000000
+ beta1 beta2 beta3 beta.sd pop rho
+ 7.09007684 7.49554194 6.39692966 -6.90775528 14.55744790 -0.51082562
+ S.0 I.0 R.0
+-3.33198030 -7.20775528 -0.02292750
+
+$nsim
+[1] 100
+
+$quantiles
+ marg.1 marg.2 marg.3 acf.0.reports acf.1.reports
+ 0 1 0 0 0
+acf.2.reports acf.3.reports acf.4.reports acf.5.reports median
+ 0 0 0 1 0
+
+$pvals
+ marg.1 marg.2 marg.3 acf.0.reports acf.1.reports
+ 0.01980198 0.01980198 0.01980198 0.01980198 0.01980198
+acf.2.reports acf.3.reports acf.4.reports acf.5.reports median
+ 0.01980198 0.01980198 0.01980198 0.01980198 0.01980198
+
+$synth.loglik
+[1] -1247.474
+
+>
+> pm.fit <- probe.match(
++ po,
++ start=guess,
++ probes=plist,
++ est=ics[-1],
++ method="Nelder-Mead",
++ trace=3,
++ reltol=1e-5,
++ parscale=c(0.1,0.1),
++ nsim=100,
++ seed=1066L
++ )
+ Nelder-Mead direct search function minimizer
+function value for initial parameters = 1247.473783
+ Scaled convergence tolerance is 0.0124747
+Stepsize computed as 7.207755
+BUILD 3 1882.104083 1247.473783
+HI-REDUCTION 5 1273.599371 321.643854
+HI-REDUCTION 7 1247.473783 50.803164
+LO-REDUCTION 9 321.643854 30.294611
+HI-REDUCTION 11 83.364824 30.294611
+HI-REDUCTION 13 51.401288 30.294611
+HI-REDUCTION 15 50.803164 30.294611
+EXTENSION 17 45.048892 12.640485
+REFLECTION 19 30.294611 5.652463
+HI-REDUCTION 21 14.282091 5.652463
+HI-REDUCTION 23 12.640485 5.652463
+REFLECTION 25 8.321974 4.949657
+HI-REDUCTION 27 6.048930 4.949657
+LO-REDUCTION 29 5.652463 4.949657
+SHRINK 33 5.636242 4.949657
+LO-REDUCTION 35 5.525022 4.306266
+HI-REDUCTION 37 4.949657 3.956195
+SHRINK 41 6.425156 3.956195
+LO-REDUCTION 43 4.572072 3.956195
+LO-REDUCTION 45 4.137456 3.956195
+SHRINK 49 6.284694 3.956195
+LO-REDUCTION 51 4.866083 3.956195
+SHRINK 55 5.731239 3.956195
+LO-REDUCTION 57 4.807376 3.956195
+SHRINK 61 5.951373 3.956195
+LO-REDUCTION 63 5.871587 3.956195
+REFLECTION 65 5.226899 3.953204
+LO-REDUCTION 67 4.191270 3.953204
+SHRINK 71 5.553088 3.805070
+HI-REDUCTION 73 5.039328 3.805070
+LO-REDUCTION 75 4.677420 3.805070
+SHRINK 79 5.039328 3.805070
+SHRINK 83 5.553088 3.805070
+LO-REDUCTION 85 5.039328 3.805070
+SHRINK 89 5.553088 3.805070
+Exiting from Nelder Mead minimizer
+ 91 function evaluations used
+>
+> summary(pm.fit)
+$coef
+ gamma mu iota nbasis degree period beta1
+ 3.2580965 -3.9120230 -4.6051702 3.0000000 3.0000000 1.0000000 7.0900768
+ beta2 beta3 beta.sd pop rho S.0 I.0
+ 7.4955419 6.3969297 -6.9077553 14.5574479 -0.5108256 -3.3319803 -6.4597344
+ R.0
+ 0.4722391
+
+$nsim
+[1] 100
+
+$quantiles
+ marg.1 marg.2 marg.3 acf.0.reports acf.1.reports
+ 0.93 0.98 0.03 0.63 0.57
+acf.2.reports acf.3.reports acf.4.reports acf.5.reports median
+ 0.55 0.53 0.10 0.45 0.57
+
+$pvals
+ marg.1 marg.2 marg.3 acf.0.reports acf.1.reports
+ 0.15841584 0.05940594 0.07920792 0.75247525 0.87128713
+acf.2.reports acf.3.reports acf.4.reports acf.5.reports median
+ 0.91089109 0.95049505 0.21782178 0.91089109 0.87128713
+
+$synth.loglik
+[1] -3.805070
+
+$est
+[1] "I.0" "R.0"
+
+$weights
+[1] 1
+
+$value
+[1] 3.805070
+
+$eval
+[1] 91 NA
+
+$convergence
+[1] 0
+
+>
+> comp.table <- cbind(true=exp(coef(po,ics)),guess=exp(guess[ics]),fit=exp(coef(pm.fit,ics)))
+> comp.table <- apply(comp.table,2,function(x)x/sum(x))
+> comp.table <- rbind(
++ comp.table,
++ synth.loglik=c(
++ summary(pm.true)$synth.loglik,
++ summary(pm.guess)$synth.loglik,
++ summary(pm.fit)$synth.loglik
++ )
++ )
+> comp.table
+ true guess fit
+S.0 0.02166667 3.523616e-02 0.0217703602
+I.0 0.00100000 7.307367e-04 0.0009538922
+R.0 0.97733333 9.640331e-01 0.9772757476
+synth.loglik -4.01290746 -1.247474e+03 -3.8050695934
+>
+> x <- sapply(
++ list(true=pm.true,guess=pm.guess,fit=pm.fit),
++ function (x) trajectory(x,times=time(x),t0=timezero(x))["cases",1,]
++ )
+>
+> plot(range(time(po)),range(c(states(po,"cases"),x)),bty='l',xlab="time",ylab="cases",type='n')
+> points(time(po),states(po,"cases"))
+> matlines(time(po),x,lty=1,col=c("red","blue","green"))
+> legend("topright",lty=1,bty='n',col=c("red","blue","green"),legend=colnames(x))
+>
+> data(euler.sir)
+> po <- window(euler.sir,end=0.25)
+> guess <- coef(po)
+> ics <- c("S.0","I.0","R.0")
+> guess[ics[-3]] <- guess[ics[-3]]+c(0.1,-0.2)
+>
+> summary(tm.true <- traj.match(po,eval.only=TRUE))
+$params
+ gamma mu iota nbasis degree period
+ 3.25809654 -3.91202301 -4.60517019 3.00000000 3.00000000 1.00000000
+ beta1 beta2 beta3 beta.sd pop rho
+ 7.09007684 7.49554194 6.39692966 -6.90775528 14.55744790 -0.51082562
+ S.0 I.0 R.0
+-3.83198030 -6.90775528 -0.02292750
+
+$loglik
+[1] -82.95589
+
+$eval
+[1] 1 0
+
+$convergence
+[1] NA
+
+$msg
+[1] "no optimization performed"
+
+>
+> summary(tm.guess <- traj.match(po,start=guess,eval.only=TRUE))
+$params
+ gamma mu iota nbasis degree period
+ 3.25809654 -3.91202301 -4.60517019 3.00000000 3.00000000 1.00000000
+ beta1 beta2 beta3 beta.sd pop rho
+ 7.09007684 7.49554194 6.39692966 -6.90775528 14.55744790 -0.51082562
+ S.0 I.0 R.0
+-3.73198030 -7.10775528 -0.02292750
+
+$loglik
+[1] -557.4875
+
+$eval
+[1] 1 0
+
+$convergence
+[1] NA
+
+$msg
+[1] "no optimization performed"
+
+>
+> tm.fit <- traj.match(
++ po,
++ start=guess,
++ est=ics[-1],
++ method="sannbox",
++ maxit=300,
++ trace=2,
++ parscale=c(0.1,0.1)
++ )
+initial evaluation: 557.4875
+iter 1 val= 202.5355 , accept= TRUE
+iter 2 val= 202.5355 , accept= FALSE
+iter 3 val= 202.5355 , accept= FALSE
+iter 4 val= 202.5355 , accept= FALSE
+iter 5 val= 202.5355 , accept= FALSE
+iter 6 val= 202.5355 , accept= FALSE
+iter 7 val= 202.5355 , accept= FALSE
+iter 8 val= 202.5355 , accept= FALSE
+iter 9 val= 202.5355 , accept= FALSE
+iter 10 val= 202.5355 , accept= FALSE
+iter 11 val= 202.5355 , accept= FALSE
+iter 12 val= 202.5355 , accept= FALSE
+iter 13 val= 202.5355 , accept= FALSE
+iter 14 val= 202.5355 , accept= FALSE
+iter 15 val= 202.5355 , accept= FALSE
+iter 16 val= 202.5355 , accept= FALSE
+iter 17 val= 202.5355 , accept= FALSE
+iter 18 val= 202.5355 , accept= FALSE
+iter 19 val= 202.5355 , accept= FALSE
+iter 20 val= 162.2567 , accept= TRUE
+iter 21 val= 162.2567 , accept= FALSE
+iter 22 val= 162.2567 , accept= FALSE
+iter 23 val= 162.2567 , accept= FALSE
+iter 24 val= 162.2567 , accept= FALSE
+iter 25 val= 162.2567 , accept= FALSE
+iter 26 val= 162.2567 , accept= FALSE
+iter 27 val= 141.8041 , accept= TRUE
+iter 28 val= 141.8041 , accept= FALSE
+iter 29 val= 130.7353 , accept= TRUE
+iter 30 val= 130.7353 , accept= FALSE
+iter 31 val= 130.7353 , accept= FALSE
+iter 32 val= 130.7353 , accept= FALSE
+iter 33 val= 130.7353 , accept= FALSE
+iter 34 val= 130.7353 , accept= FALSE
+iter 35 val= 130.7353 , accept= FALSE
+iter 36 val= 130.7353 , accept= FALSE
+iter 37 val= 130.7353 , accept= FALSE
+iter 38 val= 130.7353 , accept= FALSE
+iter 39 val= 130.7353 , accept= FALSE
+iter 40 val= 130.7353 , accept= FALSE
+iter 41 val= 130.7353 , accept= FALSE
+iter 42 val= 130.7353 , accept= FALSE
+iter 43 val= 130.7353 , accept= FALSE
+iter 44 val= 130.7353 , accept= FALSE
+iter 45 val= 130.7353 , accept= FALSE
+iter 46 val= 130.7353 , accept= FALSE
+iter 47 val= 123.9158 , accept= TRUE
+iter 48 val= 123.1513 , accept= TRUE
+iter 49 val= 123.1513 , accept= FALSE
+iter 50 val= 123.1513 , accept= FALSE
+iter 51 val= 123.1513 , accept= FALSE
+iter 52 val= 123.1513 , accept= FALSE
+iter 53 val= 123.1513 , accept= FALSE
+iter 54 val= 123.1513 , accept= FALSE
+iter 55 val= 123.1513 , accept= FALSE
+iter 56 val= 123.1513 , accept= FALSE
+iter 57 val= 123.1513 , accept= FALSE
+iter 58 val= 113.3106 , accept= TRUE
+iter 59 val= 113.3106 , accept= FALSE
+iter 60 val= 113.3106 , accept= FALSE
+iter 61 val= 113.3106 , accept= FALSE
+iter 62 val= 109.4542 , accept= TRUE
+iter 63 val= 109.4542 , accept= FALSE
+iter 64 val= 107.3697 , accept= TRUE
+iter 65 val= 107.3697 , accept= FALSE
+iter 66 val= 107.3697 , accept= FALSE
+iter 67 val= 107.3697 , accept= FALSE
+iter 68 val= 105.1804 , accept= TRUE
+iter 69 val= 105.1804 , accept= FALSE
+iter 70 val= 105.1804 , accept= FALSE
+iter 71 val= 105.1804 , accept= FALSE
+iter 72 val= 105.1804 , accept= FALSE
+iter 73 val= 105.1804 , accept= FALSE
+iter 74 val= 105.1804 , accept= FALSE
+iter 75 val= 105.1804 , accept= FALSE
+iter 76 val= 105.1804 , accept= FALSE
+iter 77 val= 105.1804 , accept= FALSE
+iter 78 val= 105.1804 , accept= FALSE
+iter 79 val= 102.0452 , accept= TRUE
+iter 80 val= 102.0452 , accept= FALSE
+iter 81 val= 102.0452 , accept= FALSE
+iter 82 val= 102.0452 , accept= FALSE
+iter 83 val= 102.0452 , accept= FALSE
+iter 84 val= 102.0452 , accept= FALSE
+iter 85 val= 96.87843 , accept= TRUE
+iter 86 val= 96.87843 , accept= FALSE
+iter 87 val= 96.87843 , accept= FALSE
+iter 88 val= 96.56392 , accept= TRUE
+iter 89 val= 96.56392 , accept= FALSE
+iter 90 val= 96.56392 , accept= FALSE
+iter 91 val= 96.56392 , accept= FALSE
+iter 92 val= 96.56392 , accept= FALSE
+iter 93 val= 96.56392 , accept= FALSE
+iter 94 val= 96.56392 , accept= FALSE
+iter 95 val= 96.56392 , accept= FALSE
+iter 96 val= 93.35854 , accept= TRUE
+iter 97 val= 93.35854 , accept= FALSE
+iter 98 val= 93.35854 , accept= FALSE
+iter 99 val= 93.35854 , accept= FALSE
+iter 100 val= 93.35854 , accept= FALSE
+iter 101 val= 93.35854 , accept= FALSE
+iter 102 val= 93.35854 , accept= FALSE
+iter 103 val= 89.3218 , accept= TRUE
+iter 104 val= 88.1193 , accept= TRUE
+iter 105 val= 88.1193 , accept= FALSE
+iter 106 val= 88.1193 , accept= FALSE
+iter 107 val= 88.1193 , accept= FALSE
+iter 108 val= 88.1193 , accept= FALSE
+iter 109 val= 88.1193 , accept= FALSE
+iter 110 val= 88.1193 , accept= FALSE
+iter 111 val= 88.1193 , accept= FALSE
+iter 112 val= 88.1193 , accept= FALSE
+iter 113 val= 88.1193 , accept= FALSE
+iter 114 val= 88.1193 , accept= FALSE
+iter 115 val= 88.1193 , accept= FALSE
+iter 116 val= 88.1193 , accept= FALSE
+iter 117 val= 88.1193 , accept= FALSE
+iter 118 val= 88.1193 , accept= FALSE
+iter 119 val= 88.1193 , accept= FALSE
+iter 120 val= 88.1193 , accept= FALSE
+iter 121 val= 88.1193 , accept= FALSE
+iter 122 val= 88.1193 , accept= FALSE
+iter 123 val= 88.1193 , accept= FALSE
+iter 124 val= 88.1193 , accept= FALSE
+iter 125 val= 88.1193 , accept= FALSE
+iter 126 val= 88.1193 , accept= FALSE
+iter 127 val= 88.1193 , accept= FALSE
+iter 128 val= 88.1193 , accept= FALSE
+iter 129 val= 86.8662 , accept= TRUE
+iter 130 val= 86.8662 , accept= FALSE
+iter 131 val= 86.8662 , accept= FALSE
+iter 132 val= 86.8662 , accept= FALSE
+iter 133 val= 86.8662 , accept= FALSE
+iter 134 val= 86.8662 , accept= FALSE
+iter 135 val= 86.8662 , accept= FALSE
+iter 136 val= 86.8662 , accept= FALSE
+iter 137 val= 86.8662 , accept= FALSE
+iter 138 val= 86.8662 , accept= FALSE
+iter 139 val= 86.8662 , accept= FALSE
+iter 140 val= 86.8662 , accept= FALSE
+iter 141 val= 81.4736 , accept= TRUE
+iter 142 val= 81.4736 , accept= FALSE
+iter 143 val= 81.4736 , accept= FALSE
+iter 144 val= 81.4736 , accept= FALSE
+iter 145 val= 81.4736 , accept= FALSE
+iter 146 val= 81.4736 , accept= FALSE
+iter 147 val= 81.4736 , accept= FALSE
+iter 148 val= 81.4736 , accept= FALSE
+iter 149 val= 81.4736 , accept= FALSE
+iter 150 val= 81.4736 , accept= FALSE
+iter 151 val= 81.4736 , accept= FALSE
+iter 152 val= 81.4736 , accept= FALSE
+iter 153 val= 81.4736 , accept= FALSE
+iter 154 val= 81.4736 , accept= FALSE
+iter 155 val= 81.4736 , accept= FALSE
+iter 156 val= 80.6933 , accept= TRUE
+iter 157 val= 80.6933 , accept= FALSE
+iter 158 val= 75.75244 , accept= TRUE
+iter 159 val= 75.75244 , accept= FALSE
+iter 160 val= 75.75244 , accept= FALSE
+iter 161 val= 71.66279 , accept= TRUE
+iter 162 val= 71.66279 , accept= FALSE
+iter 163 val= 71.66279 , accept= FALSE
+iter 164 val= 71.66279 , accept= FALSE
+iter 165 val= 71.66279 , accept= FALSE
+iter 166 val= 71.66279 , accept= FALSE
+iter 167 val= 72.1831 , accept= TRUE
+iter 168 val= 72.1831 , accept= FALSE
+iter 169 val= 72.1831 , accept= FALSE
+iter 170 val= 72.1831 , accept= FALSE
+iter 171 val= 72.1831 , accept= FALSE
+iter 172 val= 72.1831 , accept= FALSE
+iter 173 val= 72.1831 , accept= FALSE
+iter 174 val= 72.1831 , accept= FALSE
+iter 175 val= 72.1831 , accept= FALSE
+iter 176 val= 72.1831 , accept= FALSE
+iter 177 val= 72.1831 , accept= FALSE
+iter 178 val= 72.1831 , accept= FALSE
+iter 179 val= 68.91 , accept= TRUE
+iter 180 val= 68.91 , accept= FALSE
+iter 181 val= 68.91 , accept= FALSE
+iter 182 val= 68.91 , accept= FALSE
+iter 183 val= 68.91 , accept= FALSE
+iter 184 val= 68.91 , accept= FALSE
+iter 185 val= 68.91 , accept= FALSE
+iter 186 val= 68.91 , accept= FALSE
+iter 187 val= 68.91 , accept= FALSE
+iter 188 val= 68.91 , accept= FALSE
+iter 189 val= 68.91 , accept= FALSE
+iter 190 val= 68.91 , accept= FALSE
+iter 191 val= 68.91 , accept= FALSE
+iter 192 val= 68.91 , accept= FALSE
+iter 193 val= 68.91 , accept= FALSE
+iter 194 val= 68.91 , accept= FALSE
+iter 195 val= 68.91 , accept= FALSE
+iter 196 val= 68.91 , accept= FALSE
+iter 197 val= 68.91 , accept= FALSE
+iter 198 val= 68.91 , accept= FALSE
+iter 199 val= 68.91 , accept= FALSE
+iter 200 val= 68.91 , accept= FALSE
+iter 201 val= 68.91 , accept= FALSE
+iter 202 val= 69.15251 , accept= TRUE
+iter 203 val= 69.15251 , accept= FALSE
+iter 204 val= 69.15251 , accept= FALSE
+iter 205 val= 69.15251 , accept= FALSE
+iter 206 val= 69.15251 , accept= FALSE
+iter 207 val= 69.15251 , accept= FALSE
+iter 208 val= 69.15251 , accept= FALSE
+iter 209 val= 69.15251 , accept= FALSE
+iter 210 val= 69.15251 , accept= FALSE
+iter 211 val= 69.15251 , accept= FALSE
+iter 212 val= 69.15251 , accept= FALSE
+iter 213 val= 69.15251 , accept= FALSE
+iter 214 val= 69.15251 , accept= FALSE
+iter 215 val= 69.15251 , accept= FALSE
+iter 216 val= 69.15251 , accept= FALSE
+iter 217 val= 69.15251 , accept= FALSE
+iter 218 val= 69.15251 , accept= FALSE
+iter 219 val= 69.15251 , accept= FALSE
+iter 220 val= 69.15251 , accept= FALSE
+iter 221 val= 69.15251 , accept= FALSE
+iter 222 val= 69.15251 , accept= FALSE
+iter 223 val= 69.15251 , accept= FALSE
+iter 224 val= 69.15251 , accept= FALSE
+iter 225 val= 69.15251 , accept= FALSE
+iter 226 val= 69.21927 , accept= TRUE
+iter 227 val= 69.21927 , accept= FALSE
+iter 228 val= 69.21927 , accept= FALSE
+iter 229 val= 69.21927 , accept= FALSE
+iter 230 val= 69.21927 , accept= FALSE
+iter 231 val= 68.9241 , accept= TRUE
+iter 232 val= 68.9241 , accept= FALSE
+iter 233 val= 68.7367 , accept= TRUE
+iter 234 val= 68.7367 , accept= FALSE
+iter 235 val= 68.7367 , accept= FALSE
+iter 236 val= 68.7367 , accept= FALSE
+iter 237 val= 68.7367 , accept= FALSE
+iter 238 val= 68.7367 , accept= FALSE
+iter 239 val= 68.7367 , accept= FALSE
+iter 240 val= 68.7367 , accept= FALSE
+iter 241 val= 68.7367 , accept= FALSE
+iter 242 val= 68.7367 , accept= FALSE
+iter 243 val= 68.7367 , accept= FALSE
+iter 244 val= 68.7367 , accept= FALSE
+iter 245 val= 68.7367 , accept= FALSE
+iter 246 val= 68.7367 , accept= FALSE
+iter 247 val= 68.7367 , accept= FALSE
+iter 248 val= 68.7367 , accept= FALSE
+iter 249 val= 68.7367 , accept= FALSE
+iter 250 val= 68.7367 , accept= FALSE
+iter 251 val= 68.7367 , accept= FALSE
+iter 252 val= 68.7367 , accept= FALSE
+iter 253 val= 68.7367 , accept= FALSE
+iter 254 val= 68.7367 , accept= FALSE
+iter 255 val= 68.7367 , accept= FALSE
+iter 256 val= 68.7367 , accept= FALSE
+iter 257 val= 68.7367 , accept= FALSE
+iter 258 val= 68.7367 , accept= FALSE
+iter 259 val= 68.7367 , accept= FALSE
+iter 260 val= 68.7367 , accept= FALSE
+iter 261 val= 68.7367 , accept= FALSE
+iter 262 val= 68.7367 , accept= FALSE
+iter 263 val= 68.7367 , accept= FALSE
+iter 264 val= 68.7367 , accept= FALSE
+iter 265 val= 68.7367 , accept= FALSE
+iter 266 val= 68.7367 , accept= FALSE
+iter 267 val= 68.7367 , accept= FALSE
+iter 268 val= 68.7367 , accept= FALSE
+iter 269 val= 68.7367 , accept= FALSE
+iter 270 val= 68.7367 , accept= FALSE
+iter 271 val= 68.7367 , accept= FALSE
+iter 272 val= 68.7367 , accept= FALSE
+iter 273 val= 68.7367 , accept= FALSE
+iter 274 val= 68.91344 , accept= TRUE
+iter 275 val= 68.91344 , accept= FALSE
+iter 276 val= 68.91344 , accept= FALSE
+iter 277 val= 68.91344 , accept= FALSE
+iter 278 val= 68.91344 , accept= FALSE
+iter 279 val= 68.60169 , accept= TRUE
+iter 280 val= 68.60169 , accept= FALSE
+iter 281 val= 68.60169 , accept= FALSE
+iter 282 val= 68.60169 , accept= FALSE
+iter 283 val= 68.60169 , accept= FALSE
+iter 284 val= 68.60169 , accept= FALSE
+iter 285 val= 68.60169 , accept= FALSE
+iter 286 val= 68.60169 , accept= FALSE
+iter 287 val= 68.60169 , accept= FALSE
+iter 288 val= 68.60169 , accept= FALSE
+iter 289 val= 68.60169 , accept= FALSE
+iter 290 val= 68.60169 , accept= FALSE
+iter 291 val= 68.60169 , accept= FALSE
+iter 292 val= 68.60169 , accept= FALSE
+iter 293 val= 68.60169 , accept= FALSE
+iter 294 val= 68.60169 , accept= FALSE
+iter 295 val= 68.60169 , accept= FALSE
+iter 296 val= 68.60169 , accept= FALSE
+iter 297 val= 68.60169 , accept= FALSE
+iter 298 val= 68.60169 , accept= FALSE
+iter 299 val= 68.60169 , accept= FALSE
+iter 300 val= 68.60169 , accept= FALSE
+best val= 68.60169
+>
+> tm.fit <- traj.match(
++ tm.fit,
++ est=ics[-1],
++ method="Nelder-Mead",
++ trace=3,
++ reltol=1e-8,
++ parscale=c(0.1,0.1)
++ )
+ Nelder-Mead direct search function minimizer
+function value for initial parameters = 68.601691
+ Scaled convergence tolerance is 6.86017e-07
+Stepsize computed as 6.872543
+BUILD 3 99999999999999996863366107917975552.000000 68.601691
+LO-REDUCTION 5 17849.224347 68.601691
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/pomp -r 451
More information about the pomp-commits
mailing list