abc.star: abc.star

n.of.x	<- 60
n.of.y	<- 60

# Example 1: calibrate critical region and power of ABC accept/reject step (default)
# this requires to specify alpha, mx.pw (calibration parameters), 
# n.of.x, n.of.y (summary parameters), 
# tau.u.ub (for numerical optimization)
# note: this is the default calibration because it specifies all ABC parameters
# for sensible calibration parameters, and only requires minimal information on 
# the observed summary values. If a set of observed summary values is available,
# use the calibrations in Example 5.

vartest.calibrate(n.of.x=n.of.x, n.of.y=n.of.y, tau.u.ub=3, what='MXPW', mx.pw=0.9, alpha=0.01, plot=TRUE, verbose=FALSE)

# Example 2: calculate ABC false positive rate for given ABC tolerance
# this requires to specify c.l, c.u, tau.l, tau.u (ad-hoc ABC parameters), n.of.x, n.of.y (summary parameters)
# note: can be useful to compute the ABC false positive rate for uncalibrated ABC routines

vartest.calibrate(n.of.x=n.of.x, n.of.y=n.of.y, c.l=2/3, c.u=3/2, tau.l=1/2, tau.u=2, what='ALPHA', alpha=0.01, plot=TRUE, verbose=FALSE)

# Example 3: calibrate critical region for given ABC false positive rate and equivalence region
# this requires to specify alpha (calibration parameters), tau.l, tau.u (ad-hoc ABC parameter), n.of.x, n.of.y (summary parameters)
# note: this is just an intermediate calibration and may result in unsuitable power properties

vartest.calibrate(n.of.x=n.of.x, n.of.y=n.of.y, tau.l=1/2, tau.u=2, what='CR', alpha=0.01, plot=TRUE, verbose=FALSE)

# Example 4: calibrate critical region and location of maximum power of ABC accept/reject step
# this requires to specify alpha (calibration parameters), tau.u (ad-hoc ABC parameter), n.of.x, n.of.y (summary parameters)
# note: this is just an intermediate calibration and may result in unsuitable power properties

vartest.calibrate(n.of.x=n.of.x, n.of.y=n.of.y, tau.u=2, what='MXPW_AT_EQU', alpha=0.01, plot=TRUE, verbose=FALSE)

# Example 5: calibrate critical region, power of ABC accept/reject step, and #simulated data points
# this requires to specify alpha, mx.pw (calibration parameters), and n.of.x, s.of.x (summary parameters)
# note: the advantage here is that the KL divergence is also minimised, but we also
# need to have a set of observed summary values

xmean 		<- 1.2
xsigma 		<- 1.42
obs 		<- rnorm(n.of.x, xmean, xsigma)
obs 		<- (obs - mean(obs))/sd(obs) * xsigma + xmean
s.of.x		<- sd(obs)
n.of.y		<- NA	#this is now a calibration output
vartest.calibrate(n.of.x=n.of.x, s.of.x=s.of.x, what='KL', mx.pw=0.9, alpha=0.01, plot=TRUE, verbose=FALSE)