predict.sns: Sample-based prediction using "sns" Objects

In sns: Stochastic Newton Sampler (SNS)

Sample-based prediction using "sns" Objects

Description

Method for sample-based prediction using the output of sns.run.

Usage

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

Arguments

object	Object of class "sns" (output of sns.run) or "predict.sns" (output of predict.sns).
fpred	Prediction function, accepting a single value for the state vector and producing a vector of outputs.
nburnin	Number of burn-in iterations discarded for sample-based prediction.
end	Last iteration used in sample-based prediction.
thin	One out of thin iterations within the specified range are used for sample-based prediction.
quantiles	Values for which sample-based quantiles are calculated.
ess.method	Method used for calculating effective sample size. Default is to call effectiveSize from package coda.
x	An object of class "summary.predict.sns".
...	Arguments passed to/from other functions.

Value

predict.sns produces a matrix with number of rows equal to the length of prediction vector produces by fpred. Its numnber of columns is equal to the number of samples used within the user-specified range, and after thinning (if any). summary.predict.sns produces sample-based prediction mean, standard deviation, quantiles, and effective sample size.

Note

See package vignette for more details on SNS theory, software, examples, and performance.

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

Examples

## Not run: 

# using RegressionFactory for generating log-likelihood and derivatives
library("RegressionFactory")

loglike.poisson <- function(beta, X, y) {
  regfac.expand.1par(beta, X = X, y = y,
    fbase1 = fbase1.poisson.log)
}

# simulating data
K <- 5
N <- 1000
X <- matrix(runif(N * K, -0.5, +0.5), ncol = K)
beta <- runif(K, -0.5, +0.5)
y <- rpois(N, exp(X %*% beta))

beta.init <- rep(0.0, K)
beta.smp <- sns.run(beta.init, loglike.poisson,
  niter = 1000, nnr = 20, mh.diag = TRUE, X = X, y = y)

# prediction function for mean response
predmean.poisson <- function(beta, Xnew) exp(Xnew %*% beta)
ymean.new <- predict(beta.smp, predmean.poisson,
                     nburnin = 100, Xnew = X)
summary(ymean.new)

# (stochastic) prediction function for response
predsmp.poisson <- function(beta, Xnew)
  rpois(nrow(Xnew), exp(Xnew %*% beta))
ysmp.new <- predict(beta.smp, predsmp.poisson
                    , nburnin = 100, Xnew = X)
summary(ysmp.new)


## End(Not run)

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