MLDS (version 0.1-5)

boot.mlds: Resampling of an Estimated Difference Scale

Description

Using the fitted responses (probabilities) to the difference scale, new responses are generated which permit new bootstrap replications of estimated scales to be generated. The mean scale is useful for evaluating bias and the standard deviation for estimating standard errors of the scale values.

Usage

boot.mlds(x, nsim, ...)

Arguments

x

an object of class `mlds'.

nsim

an integer, the number of simulations.

...

Additional options passed along to the function mlds.

Value

• A list of 4 elements:
• boot.samp
  A $p$ x $N$ matrix of the bootstrap samples of the scale, where p is the number of scale values and N is the number of simulations.
• bt.mean
  A vector of length $p$ giving the mean of the bootstrap scales.
• bt.sd
  A vector of length $p$ giving the standard deviation of the boostrap scales.
• N
  The number of bootstrap simulations.

Details

Either the scale values (from glm method) or the scale values and $\sigma$ (from optim method) permit the fitted probabilities to be estimated. These are used to generate new responses to the quadruples using rbinom. The new responses are then used with mlds to estimate a bootstrapped scale. This is repeated $N$ times and stored in the output with the mean and standard deviation of the bootstrapped scales.

References

Maloney, L. T. and Yang, J. N. (2003). Maximum likelihood difference scaling. Journal of Vision, 3(8):5, 573--585, http://journalofvision.org/3/8/5/, doi:10.1167/3.8.5.

See Also

mlds

Examples

data(kk1)
	kk1.mlds <- mlds(kk1)
	#nsim should be near 10,000 for stability,
	# but this will take a little time
	boot.mlds(kk1.mlds, 100)