fetchSummary: Summarise estimation output.
In StatisticsNZ/demest: Bayesian Demographic Estimation and Forecasting
View source: R/query-functions.R
fetchSummary R Documentation
Summarise estimation output.
Description
Summarise the results from estimateModel,
estimateCounts or estimateAccount.
fetchSummary can also be called on results from prediction
functions, though doing so is generally less enlightening.
Usage
fetchSummary(filename, nSample = 25)
Arguments
filename
The filename used by the estimation or prediction
function.
nSample
The number of elements to sample from a batch of
parameters. Defaults to 25.
Value
An object of class SummaryResults.
Metropolis-Hastings updates
Depending on the model, one or more batches of parameters is updated
using Metropolis-Hasting updates. jump is the standard
deviation of the propoposal density. acceptance is the
proportion of proposals that are accepted. autocorrelation
is the correlation between successive iterations, after thinning.
Autocorrelations are calculated separately, parameter by parameter,
and then averaged. If a batch contains more than nSample parameters,
then nSample parameters are chosen at random, and autocorrelations
calculated only for that sample.
The Metropolis-Hastings statistics can be extracted from the summary
object using function metropolis.
Parameters
'Rhat's, or 'potential scale reduction factors' are calculated
using function gelman.diag
from package coda. However, like Gelman et al (2014: 284-286),
but unlike the original gelman.diag function,
each chain is split into two,
and the first and second halves compared,
to capture variation within each chain,
in addition to variation across chains.
To save time, fetchSummary calculates Rhats for a sample of
at most nSample elements from each batch of parameters.
The number of elements sampled is shown in the column labeled n.
Rhats typically vary across different parameters within a batch.
To give an idea of the distribution, fetchSummary shows
the median and maximum Rhats. The best way to get detailed information
about convergence is to use fetchMCMC followed by
plot.
The sampling of the Rhats introduces an element
of randomness: if fetchSample is called twice on the same
filename the results for Rhat will differ.
When the number of chains (nChain), the number of iterations
(nSim), or the proportion of proposals accepted is small,
estimated Rhats can be unstable, and must be interpreted with
caution.
Est. in the output from fetchSummary is short for
"Point estimates". The Est. columns provide summaries
of the distribution of the estimates. fetchSummary
calculates point estimates (posterior medians) for every element
in a batch of parameters, and then shows the distribution of
these point estimates. If a batch has only one parameter, then
only the point estimate for that parameter is shown.
If a batch has more than one parameter, then the minimum
point estimate, median point estimate, and maximum point estimate
for that batch are shown.
To save time, fetchSummary calculates the point estimates
from a maximum of 100 draws from the posterior sample. The values
may therefore differ slightly from the more accurate values
obtained by calling fetch and then collapseIterations
(assuming the full sample has more than 100 draws.)
If greater control over the calculation of Rhat is desired, parameter
estimates can be extracted using fetchMCMC, and
results calculated using gelman.diag.
References
Geyer, C. J. (2011) Introduction to Markov Chain Monte Carlo.
Brooks, S., Gelman, A., Jones, G., Meng, X-L (eds.) Handbook of Markov
Chain Monte Carlo. CRC Press.
Gelman, A., Shirley, K. (2011) Inference from simulations and monitoring
convergence. Brooks, S., Gelman, A., Jones, G., Meng, X-L (eds.)
Handbook of Markov Chain Monte Carlo. CRC Press.
Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B., 2014.
Bayesian Data Analysis. Third Edition. Boca Raton, FL, USA:
Chapman & Hall/CRC.
See Also
Individual components of the summary can be extracted
using functions metropolis, parameters,
and gelmanDiag. Parameter estimates from estimation
or prediction are obtained using function fetch.
Examples
deaths <- demdata::VADeaths2
popn <- demdata::VAPopn
deaths <- round(deaths)
deaths <- Counts(deaths)
popn <- Counts(popn)
filename <- tempfile()
model <- Model(y ~ Poisson(mean ~ age + sex),
jump = 0.5)
estimateModel(model = model,
y = deaths,
exposure = popn,
filename = filename,
nBurnin = 50,
nSim = 50,
nChain = 2,
parallel = FALSE)
fetchSummary(filename)
## keep summary object and extract parts from it
summary.est <- fetchSummary(filename)
gelmanDiag(summary.est)
metropolis(summary.est)
parameters(summary.est)
StatisticsNZ/demest documentation built on Nov. 2, 2023, 7:56 p.m.
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View source: R/query-functions.R
| fetchSummary | R Documentation |
Summarise estimation output.
Description
Summarise the results from estimateModel,
estimateCounts or estimateAccount.
fetchSummary can also be called on results from prediction
functions, though doing so is generally less enlightening.
Usage
fetchSummary(filename, nSample = 25)
Arguments
filename |
The filename used by the estimation or prediction function. |
nSample |
The number of elements to sample from a batch of parameters. Defaults to 25. |
Value
An object of class SummaryResults.
Metropolis-Hastings updates
Depending on the model, one or more batches of parameters is updated
using Metropolis-Hasting updates. jump is the standard
deviation of the propoposal density. acceptance is the
proportion of proposals that are accepted. autocorrelation
is the correlation between successive iterations, after thinning.
Autocorrelations are calculated separately, parameter by parameter,
and then averaged. If a batch contains more than nSample parameters,
then nSample parameters are chosen at random, and autocorrelations
calculated only for that sample.
The Metropolis-Hastings statistics can be extracted from the summary
object using function metropolis.
Parameters
'Rhat's, or 'potential scale reduction factors' are calculated
using function gelman.diag
from package coda. However, like Gelman et al (2014: 284-286),
but unlike the original gelman.diag function,
each chain is split into two,
and the first and second halves compared,
to capture variation within each chain,
in addition to variation across chains.
To save time, fetchSummary calculates Rhats for a sample of
at most nSample elements from each batch of parameters.
The number of elements sampled is shown in the column labeled n.
Rhats typically vary across different parameters within a batch.
To give an idea of the distribution, fetchSummary shows
the median and maximum Rhats. The best way to get detailed information
about convergence is to use fetchMCMC followed by
plot.
The sampling of the Rhats introduces an element
of randomness: if fetchSample is called twice on the same
filename the results for Rhat will differ.
When the number of chains (nChain), the number of iterations
(nSim), or the proportion of proposals accepted is small,
estimated Rhats can be unstable, and must be interpreted with
caution.
Est. in the output from fetchSummary is short for
"Point estimates". The Est. columns provide summaries
of the distribution of the estimates. fetchSummary
calculates point estimates (posterior medians) for every element
in a batch of parameters, and then shows the distribution of
these point estimates. If a batch has only one parameter, then
only the point estimate for that parameter is shown.
If a batch has more than one parameter, then the minimum
point estimate, median point estimate, and maximum point estimate
for that batch are shown.
To save time, fetchSummary calculates the point estimates
from a maximum of 100 draws from the posterior sample. The values
may therefore differ slightly from the more accurate values
obtained by calling fetch and then collapseIterations
(assuming the full sample has more than 100 draws.)
If greater control over the calculation of Rhat is desired, parameter
estimates can be extracted using fetchMCMC, and
results calculated using gelman.diag.
References
Geyer, C. J. (2011) Introduction to Markov Chain Monte Carlo. Brooks, S., Gelman, A., Jones, G., Meng, X-L (eds.) Handbook of Markov Chain Monte Carlo. CRC Press.
Gelman, A., Shirley, K. (2011) Inference from simulations and monitoring convergence. Brooks, S., Gelman, A., Jones, G., Meng, X-L (eds.) Handbook of Markov Chain Monte Carlo. CRC Press.
Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B., 2014. Bayesian Data Analysis. Third Edition. Boca Raton, FL, USA: Chapman & Hall/CRC.
See Also
Individual components of the summary can be extracted
using functions metropolis, parameters,
and gelmanDiag. Parameter estimates from estimation
or prediction are obtained using function fetch.
Examples
deaths <- demdata::VADeaths2
popn <- demdata::VAPopn
deaths <- round(deaths)
deaths <- Counts(deaths)
popn <- Counts(popn)
filename <- tempfile()
model <- Model(y ~ Poisson(mean ~ age + sex),
jump = 0.5)
estimateModel(model = model,
y = deaths,
exposure = popn,
filename = filename,
nBurnin = 50,
nSim = 50,
nChain = 2,
parallel = FALSE)
fetchSummary(filename)
## keep summary object and extract parts from it
summary.est <- fetchSummary(filename)
gelmanDiag(summary.est)
metropolis(summary.est)
parameters(summary.est)
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