summary.sns: Summarizing "sns" Objects

In sns: Stochastic Newton Sampler (SNS)

Summarizing "sns" Objects

Description

Methods for summarizing the output of sns.run, and for printing the summary.

Usage

## S3 method for class 'sns'
summary(object, quantiles = c(0.025, 0.5, 0.975)
  , pval.ref = 0.0, nburnin = max(nrow(object)/2, attr(object, "nnr"))
  , end = nrow(object), thin = 1, ess.method = c("coda", "ise"), ...)
## S3 method for class 'summary.sns'
print(x, ...)

Arguments

object	An object of class "sns", typically the output of sns.run.
quantiles	Values for which sample-based quantiles are calculated.
pval.ref	Reference value for state space variables, used for calculating sample-based p-values.
nburnin	Number of initial iterations to discard before calculating the sample statistics. A warning is issued if this number is smaller than the initial iterations run in NR mode.
end	Last iteration to use for calculating sample statistics. Defaults to last iteration.
thin	One out of thin samples are kept for calculating sample statistics. Default is 1, using all samples within specified range.
ess.method	Method used for calculating effective sample size. Default is to call effectiveSize from package coda.
x	An object of class "summary.sns", typically the output of summary.sns.
...	Arguments passed to/from other functions.

Value

summary.sns returns a list with these elements:

K	Dimensionality of state space.
nnr	Number of NR (Newton-Raphson) iterations performed at the beginning.
nburnin	Number of burn-in iterations. These are discarded before calculating sample statistics.
end	Last iteration to use for calculating sample statistics.
thin	One out of every thin iterations within the specified range is used for calculating sample statistics.
niter	Total iterations, including NR and MCMC modes.
nsmp	Number of samples within specified range (before applying thinning).
nseq	Number of samples used for calculating sample statistics (after applying thinning).
npart	Number of subsets used in state space partitioning. If no partitioning is done, the value is 1.
accept.rate	Acceptance rate for the MH transition proposals, calculated over nsmp iterations.
reldev.mean	Mean relative deviation from quadratic approximation, defined as difference between actual log-density change and the value predicted from quadratic fit at density maximum, divided by the actual change. The location of density maximum is assumed to be the value at the end of the last NR iteration. Therefore, for this measure to be accurate, users must ensure nnr is sufficiently large to allow for convegrence of the optimization phase.
pval.ref	Same as input.
ess.method	Same as input.
smp	A list with elements mean, sd, ess, quantiles, pval representing sample-based mean, standard deviation, effective size, quantiles and sample-based p-values, based on specified range and using thinning (if specified).

Author(s)

Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani

References

Mahani A.S., Hasan A., Jiang M. & Sharabiani M.T.A. (2016). Stochastic Newton Sampler: The R Package sns. Journal of Statistical Software, Code Snippets, 74(2), 1-33. doi:10.18637/jss.v074.c02

See Also

sns.run

sns documentation built on Nov. 2, 2022, 5:15 p.m.