Boom (version 0.9.15)

check: Check MCMC Output

Description

Verify that MCMC output covers expected values.

Usage

CheckMcmcMatrix(draws, truth, confidence = .95,
                control.multiple.comparisons = TRUE,
                burn = 0)
CheckMcmcVector(draws, truth, confidence = .95, burn = 0)
McmcMatrixReport(draws, truth, confidence = .95, burn = 0)

Value

CheckMcmcVector and CheckMcmcMatrix return TRUE

if the check passes, and FALSE if it does not.

McmcMatrixReport returns a string that can be put in the

info field of an expect_true

expression, to give useful information about a failed test case. The return value is a textual representation of a three column matrix. Each row matches a variable in draws, and gives the lower and upper bounds for the credible interval used to check the values. The final column lists the true values that are supposed to be inside the credible intervals. The value is returned as a character string that is expected to be fed to cat() or print() so that it will render correctly in R CMD CHECK output.

Arguments

draws

The array of MCMC draws to check. This must be a matrix for CheckMcmcMatrix and a vector for CheckMcmcVector.

truth

The vector of true values that must be covered by draws in order for the check to succeed.

confidence

Specifies the probability width of the intervals used to determine whether draws covers truth. Central intervals are used, not HPD intervals.

control.multiple.comparisons

If FALSE then every interval must cover its corresponding true value. Otherwise a fraction of intervals (given by confidence) must cover.

burn

The number of MCMC iterations to discard as burn-in.

Author

Steven L. Scott

Details

CheckMcmcVector checks a vector of draws corresponding to a scalar random variable. CheckMcmcMatrix checks a matrix of draws corresponding to a vector of random variables. In either case the check is made by constructing a central confidence interval (obtained by removing half of 1 - confidence from the upper and lower tails of the distribution).

If a single variable is being checked with CheckMcmcVector then the check passes if and only if the interval covers the true value.

If multiple values are being checked with CheckMcmcMatrix then the user has control over how strict to make the check. If control.multiple.comparisons is FALSE then the check passes if and only if all intervals cover true values. Otherwise a fraction of intervals must cover. The fraction is the lower bound of the binomial confidence interval for the coverage rate under the hypothesis that the true coverage rate is confidence.

Examples

ndraws <- 100
draws <- rnorm(ndraws, 0, 1)
CheckMcmcVector(draws, 0)  ## Returns TRUE
CheckMcmcVector(draws, 17)  ## Returns FALSE

draws <- matrix(nrow = ndraws, ncol = 5)
for (i in 1:5) {
  draws[, i] <- rnorm(ndraws, i, 1)
}  
CheckMcmcMatrix(draws, truth = 1:5)  ## Returns TRUE
CheckMcmcMatrix(draws, truth = 5:1)  ## Returns FALSE