# expected.deviance: Expected deviance In AlexRego/edited_abc: Tools for Approximate Bayesian Computation (ABC)

## Description

Model selection criterion based on posterior predictive distributions and approximations of the expected deviance.

## Usage

 1 2 expected.deviance(target, postsumstat, kernel = "gaussian", subset=NULL, print=TRUE) 

## Arguments

 target a vector of the observed summary statistics. postsumstat a vector, matrix or data frame of summary statistics simulated a posteriori. kernel a character string specifying the kernel to be used when. Defaults to "gaussian". See density for details. subset a logical expression indicating elements or rows to keep. Missing values in postsumstat are taken as FALSE. print prints out what percent of the distances have been zero.

## Details

This function implements an approximation for the expected deviance based on simulation performed a posteriori. Thus, after the posterior distribution of parameters or the posterior model probabilities have been determined, users need to re-simulate data using the posterior. The Monte-Carlo estimate of the expected deviance is computed from the simulated data as follows: D=-\frac{2}{n}∑_{j=1}^{n}\log(K_ε(\parallel s^j-s_0\parallel)), where n is number of simulations, K is the statistical kernel, ε is the error, i.e. difference between the observed and simulated summary statistics below which simualtions were accepted in the original call to postpr, the s^j's are the summary statistics obtained from the posterior predictive simualtions, and s_0 are the observed values of the summary statistics. The expected devaince averaged over the posterior distribution to compute a deviance information criterion (DIC).

## Value

A list with the following components:

 expected.deviance The approximate expected deviance. dist The Euclidean distances for summary statistics simulated a posteriori.

## References

Francois O, Laval G (2011) Deviance information criteria for model selection in approximate Bayesian computation arXiv:0240377.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 ## Function definitions skewness <- function(x) { sk <- mean((x-mean(x))^3)/(sd(x)^3) return(sk) } kurtosis <- function(x) { k <- mean((x-mean(x))^4)/(sd(x)^4) - 3 return(k) } ## Observed summary statistics obs.sumstat <- c(2.004821, 3.110915, -0.7831861, 0.1440266) ## Model 1 (Gaussian) ## ################## ## Simulate data theta <- rnorm(10000, 2, 10) zeta <- 1/rexp(10000, 1) param <- cbind(theta, zeta) y <- matrix(rnorm(200000, rep(theta, each = 20), sd = rep(sqrt(zeta), each = 20)), nrow = 20, ncol = 10000) ## Calculate summary statistics s <- cbind(apply(y, 2, mean), apply(y, 2, sd), apply(y, 2, skewness), apply(y, 2, kurtosis)) ## ABC inference gaus <- abc(target=obs.sumstat, param = param, sumstat=s, tol=.1, hcorr = FALSE, method = "loclinear") param.post <- gaus$adj.values ## Posterior predictive simulations postpred.gaus <- matrix(rnorm(20000, rep(param.post[,1], each = 20), sd = rep(sqrt(param.post[,2]), each = 20)), nrow = 20, ncol = 1000) statpost.gaus <- cbind(apply(postpred.gaus, 2, mean),apply(postpred.gaus, 2, sd),apply(postpred.gaus, 2,skewness),apply(postpred.gaus, 2,kurtosis)) # Computation of the expected deviance expected.deviance(obs.sumstat, statpost.gaus)$expected.deviance expected.deviance(obs.sumstat, statpost.gaus, kernel = "epanechnikov")$expected.deviance ## Modele 2 (Laplace) ## ################## ## Simulate data zeta <- rexp(10000) param <- cbind(theta, zeta) y <- matrix(theta + sample(c(-1,1),200000, replace = TRUE)*rexp(200000, rep(zeta, each = 20)), nrow = 20, ncol = 10000) ## Calculate summary statistics s <- cbind( apply(y, 2, mean), apply(y, 2, sd), apply(y, 2, skewness), apply(y, 2, kurtosis)) ## ABC inference lapl <- abc(target=obs.sumstat, param = param, sumstat=s, tol=.1, hcorr = FALSE, method = "loclinear") param.post <- lapl$adj.values ## Posterior predictive simulations postpred.lapl <- matrix(param.post[,1] + sample(c(-1,1),20000, replace = TRUE)*rexp(20000, rep(param.post[,2], each = 20)), nrow = 20, ncol = 1000) statpost.lapl <- cbind(apply(postpred.lapl, 2, mean),apply(postpred.lapl, 2, sd),apply(postpred.lapl, 2,skewness),apply(postpred.lapl, 2,kurtosis)) ## Computation of the expected deviance expected.deviance(obs.sumstat, statpost.lapl)$expected.deviance expected.deviance(obs.sumstat, statpost.lapl, kernel = "epanechnikov")$expected.deviance 

AlexRego/edited_abc documentation built on May 5, 2019, 4:53 a.m.