boot.mlds: Resampling of an Estimated Difference Scale

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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boot.mlds(x, nsim, no.warn = TRUE, ...)
boot.mlbs(x, nsim, no.warn = TRUE, ...)

Arguments

x

an object of class ‘mlds’ or ‘mlbs’ depending on which function is called.

nsim

an integer, the number of simulations.

no.warn

logical indicating when TRUE (default) to suppress warnings from glm

...

Additional options passed along to the function mlds.

Details

Either the scale values (from ‘glm’ method) or the scale values and σ (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.

Value

A list of 4 elements of class ‘mlds.bt’:

boot.samp

A p + 1 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. The extra value is the bootstrapped sigma value.

bt.mean

A vector of length p + 1 giving the mean of the bootstrap scales.

bt.sd

A vector of length p + 1 giving the standard deviation of the boostrap scales.

N

The number of bootstrap simulations.

Author(s)

Kenneth Knoblauch and Laurence T. Maloney

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.

Knoblauch, K. and Maloney, L. T. (2008) MLDS: Maximum likelihood difference scaling in R. Journal of Statistical Software, 25:2, 1–26, http://www.jstatsoft.org/v25/i02.

See Also

mlds

Examples

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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)

MLDS documentation built on May 1, 2019, 6:50 p.m.