Nothing
require("dse")
Sys.info()
DSEversion()
fuzz <- 1e-6
digits <- 18
all.ok <- TRUE
test.rng <- list(kind="Wichmann-Hill",seed=c(979,1479,1542),normal.kind="Box-Muller")
###################################################
# test with input and having output dim < state dim.
###################################################
if(is.R()) data("eg1.DSE.data.diff", package="dse")
model <- TSmodel(toSSChol(estVARXls(eg1.DSE.data.diff)))
model0 <- model
model0$G <- NULL
simdata0 <- simulate(model0, rng=test.rng)
z <- smoother(model0, simdata0, compiled=TRUE)
zz <- smoother(model0, simdata0, compiled=FALSE)
#tfplot(simdata0$state, state(z, smooth=TRUE), state(zz, smooth=TRUE), graphs.per.page=3)
# using simulated data gives a true state for comparison.
simdata <- simulate(model, input= inputData(eg1.DSE.data.diff), rng=test.rng)
z <- smoother(model, simdata, compiled=TRUE)
zz <- smoother(model, simdata, compiled=FALSE)
error <- max(abs((state(zz, smooth=TRUE) - zz$smooth$state)))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
tfplot(state(zz), simdata$state, graphs.per.page=3)
#tfplot(state(z, smoother=TRUE) - state(zz, smoother=TRUE), graphs.per.page=3)
tfplot(state(z, smoother=TRUE), state(zz, smoother=TRUE), graphs.per.page=3)
# plot smoother agains true state
#tfplot(state(z, smoother=TRUE), simdata$state, graphs.per.page=3)
# plot smoother agains true state
#tfplot(state(zz, smoother=TRUE), simdata$state, graphs.per.page=3)
#tfplot(state(z, smoother=TRUE), simdata$state, graphs.per.page=3)
#tfplot(simdata$state, state(z, smoother=TRUE), state(zz, smoother=TRUE), graphs.per.page=3)
#tfplot(simdata$state, state(zz, filter=TRUE), state(zz, smoother=TRUE), graphs.per.page=3)
# compare fortran and S versions
error <- max(abs((state(z, smoother=TRUE) - state(zz, smoother=TRUE))))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs(z$smooth$track - zz$smooth$track))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs(z$filter$track - zz$filter$track))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
######################################
# test output dim exceeds state dim.
######################################
Hloadings <- t(matrix(c(
8.8, 5.2,
23.8, -12.6,
5.2, -2.0,
36.8, 16.9,
-2.8, 31.0,
2.6, 47.6), 2,6))
ss.ar1 <- SS(F=array(c(.5, .4, .3, .2),c(2,2)),
H=Hloadings,
Q=array(c(1.0, 2.0),c(2,2)),
R=diag(1,6)
)
simdata2 <- simulate(ss.ar1, rng=test.rng)
z <- smoother(ss.ar1, simdata2, compiled = TRUE)
zz <- smoother(ss.ar1, simdata2, compiled = FALSE)
#tfplot(state(zz, smooth=TRUE), state(z, smooth=TRUE), simdata2$state, graphs.per.page=3)
#tfplot(state(zz, smooth=TRUE) - state(z, smooth=TRUE), graphs.per.page=3)
error <- max(abs((state(zz, filter=TRUE) - zz$filter$state)))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs((state(zz, filter=TRUE) - state(z, filter=TRUE))))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs((state(zz, smooth=TRUE) - zz$smooth$state)))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs((state(z, smoother=TRUE) - state(zz, smoother=TRUE))))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs((z$smooth$track) - zz$smooth$track))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
tfplot(simdata2$state, state(zz, smoother=TRUE), state(zz, filter=TRUE))
tfplot(simdata2$state, state(z, smoother=TRUE), state(z, filter=TRUE))
tfplot(simdata2$state, state(z, smoother=TRUE), state(zz, smoother=TRUE))
######################################
# test "big k" (which is numerically sensitive).
######################################
# Starting P0 ("big k") symmetric with off diagonal element smaller than diag.
P0 <- matrix(1e6,4,4)
diag(P0 )<- 1e7
mod4 <- SS(F=t(matrix(c(
0.8, 0.04, 0.2, 0,
0.2, 0.5, 0, -0.3,
1, 0, 0, -0.2,
0, 1, 0, 0 ), c(4,4))),
H=cbind(Hloadings, matrix(0,6,2)),
Q=diag(c(1, 1, 0, 0),4),
R=diag(1,6),
z0=c(10, 20, 30,40),
P0=P0
)
z <- simulate(SS(F=t(matrix(c(
0.8, 0.04, 0.2, 0,
0.2, 0.5, 0, -0.3,
1, 0, 0, -0.2,
0, 1, 0, 0 ), c(4,4))),
H=cbind(Hloadings, matrix(0,6,2)),
Q=diag(c(1, 1, 0, 0),4),
R=diag(1,6),
z0=c(10, 20, 30,40),
P0=diag(c(10, 10, 10, 10)) ),
rng=test.rng)
state.sim <- z$state # for comparison below
y.sim <- outputData(z) # simulated indicators
error <- max(abs(l(mod4, TSdata(output=y.sim), return.state=TRUE)$filter$state -
l(mod4, TSdata(output=y.sim), return.state=TRUE,
compile=FALSE)$filter$state))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
zz <- smoother(l(mod4, TSdata(output=y.sim)))
zzz <- smoother(l(mod4, TSdata(output=y.sim)), compiled=FALSE)
tfplot(state.sim, state(zz))
tfplot(state.sim, state(zzz))
tfplot(state.sim, state(zz, smoother=TRUE))
tfplot(state.sim, state(zzz, smoother=TRUE))
error <- max(abs(state(zz, filter=TRUE) - state(zzz, filter=TRUE)))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs(state(zz, smoother=TRUE) - state(zzz, smoother=TRUE)))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs(zz$filter$track - zzz$filter$track))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
error <- max(abs(zz$smooth$track - zzz$smooth$track))
if ( fuzz < error)
{print(error, digits=18)
all.ok <- FALSE
}
if (! all.ok) stop("some tests FAILED")
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