Computes iterative approximation to mle precision estimates for nonconstant bias model using original data.

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Description

This is an internal function that computes iterative approximation to mle precision estimates for nonconstant bias model using original data.

Usage

1
precision.mle.ncb.od(x, M = 20, beta.bars = beta.bar(x), jaech.errors = FALSE)

Arguments

x

A matrix or numeric data.frame consisting of an n (no. of items) by N (no. of methods) matrix of measuremnts. N must be >= 3 and n > N.

M

Maximum no. of iterations for convergence.

beta.bars

Estimates or hypothesized values for the betas.

jaech.errors

TRUE replicates the minor error in Jaech's Fortran code to allow comparison with his examples.

Details

Provides iterative approximation to MLE precision estimates for NonConstant Bias model using Original Data. See Jaech, p. 185-186.

Value

sigma2

Estimated squared imprecisions (variances) for methods.

sigma.mu2

Estimated process variance.

Author(s)

Richard A. Bilonick

References

Jaech, J. L. (1985) Statistical Analysis of Measurement Errors. New York: Wiley.

See Also

precision.grubbs.ncb.od,precision.grubbs.cb.pd

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