Description Usage Arguments Details Value Author(s) References Examples
saABC
fits parameter estimators based on simulated data to be
used as summary statistics within ABC. Fitting is by linear
regression. Some simple diagnostics are provided for assistance.
1 
theta 
A n x d matrix or data frame of simulated parameter values.

X 
A n x p matrix or data frame of simulated data and/or associated
transformations. 
plot 
When 
The semiautomatic ABC method of Fearnhead and Prangle (2012) is as follows:
1) Simulate parameter vectors theta_i and corresponding data sets x_i for i=1,2,...,N.
2) Use the simulations to fit an estimator of each parameter as a linear combination of f(x), where f(x) is a vector of transformations of x (including a constant term).
3) Run ABC using these simulations.
The saABC
function automates step 2 of this process. The user
must supply simulated parameter values theta
and corresponding
f(x) values x
(n.b. excluding the constant term). The function
returns weights for the linear combinations which can easily be used
for step 3. In particular, fitted weights are returned as a matrix
of weights for the columns of x
and a vector of constants. The
vector can usually be discarded, as it is not needed to find
differences between summary statistics.
The function also returns BIC values for each parameter so that the user can judge the quality of the fits, and compare different choices of f(x). Diagnostic plots of supplied parameter values against fitted values are also optionally provided. These are useful for exploratory purposes when there are a small number of parameters, but provide less protection from overfitting than BIC values.
B0 
Vector of constant terms from fitted regressions. 
B 
Matrix of weights from fitted regressions. 
BICs 
Vector of BIC values for each fitted regression. 
Dennis Prangle
Blum, M. G. B, Nunes, M. A., Prangle, D. and Sisson, S. A. (2013) A
comparative review of dimension reduction methods in approximate
Bayesian computation. Stat. Sci. 28, Issue 2, 189–208.
Fearnhead, P. and Prangle, D. (2012) Constructing summary statistics for approximate Bayesian computation:
semiautomatic approximate Bayesian
computation. J. R. Stat. Soc. B 74, Part 3, 1–28.
Nunes, M. A. and Prangle, D. (2016) abctools: an R package for tuning
approximate Bayesian computation analyses. The R Journal
7, Issue 2, 189–205.
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Loading required package: abc
Loading required package: abc.data
Loading required package: nnet
Loading required package: quantreg
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
Loading required package: MASS
Loading required package: locfit
locfit 1.59.1 20130322
Loading required package: abind
Loading required package: parallel
Loading required package: plyr
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Attaching package: 'survival'
The following object is masked from 'package:quantreg':
untangle.specials
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'Hmisc'
The following objects are masked from 'package:plyr':
is.discrete, summarize
The following object is masked from 'package:quantreg':
latex
The following objects are masked from 'package:base':
format.pval, round.POSIXt, trunc.POSIXt, units
**********************************************
abctools: A package with tools for ABC inference
 Written by Matt Nunes and Dennis Prangle
Current package version: 1.1.1 ( 20170419 )
**********************************************
abctools 1.1.1 loaded
Mean Variance
421.8430 446.0156
Mean Variance
401.38215 64.52758
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