bryant_paulson: The Bryant-Paulson Generalized Studentized Range Distribution

Description

Distribution function (pbryant_paulson), quantile/critical-value function (qbryant_paulson), and density (dbryant_paulson) for the Bryant-Paulson generalized studentized range, the sampling distribution of the studentized range of covariate-adjusted means in the analysis of covariance (ANCOVA) when the covariate(s) are random. These are the analysis-of-covariance analogues of ptukey / qtukey and supply the critical values needed for Tukey-Kramer-type simultaneous confidence intervals on (and tests of) contrasts of adjusted means.

Usage

pbryant_paulson(q, num_covariates, num_groups, df, lower_tail = TRUE, ...)
qbryant_paulson(prob, num_covariates, num_groups, df, lower_tail = TRUE, ...)
dbryant_paulson(q, num_covariates, num_groups, df, ...)

Value

Numeric vectors. pbryant_paulson returns cumulative (or upper-tail) probabilities, dbryant_paulson returns density values, and qbryant_paulson returns critical values (quantiles) of the generalized studentized range. Results are recycled to the length of the longest of q/prob and the parameter arguments.

Arguments

q: Vector of quantiles (values of the generalized studentized range statistic).
num_covariates: The number of random covariates, $p$ ($p \ge 0$). With num_covariates = 0 the distribution is exactly the ordinary studentized range and the functions reduce to ptukey / qtukey.
num_groups: The number of groups (treatments) being compared, $k$ ($k \ge 2$); this is the “sample size” of the range.
df: The error degrees of freedom of the ANCOVA model, $\nu$. In a one-way ANCOVA with $N$ total observations, $k$ groups, and $p$ covariates, $\nu = N - k - p$.
lower_tail: Logical; if TRUE (default) probabilities are $P(Q \le q)$, otherwise $P(Q > q)$.
...: Additional arguments (currently unused; for extensibility).
prob: Vector of probabilities. For qbryant_paulson this is the cumulative probability (e.g., 0.95 returns the upper 5% critical value).

Author

Ken Kelley [email protected]

Details

The statistic. In a balanced ANCOVA with $k$ groups and $p$ random covariates, let $\hat\theta_i$ be the adjusted group means and $\hat\sigma_{y \mid x}$ the square root of the ANCOVA error mean square (on $\nu$ degrees of freedom). The Bryant-Paulson statistic is the studentized range of the adjusted means, $$Q \;=\; \frac{\max_i \hat\theta_i - \min_i \hat\theta_i}{\hat\sigma_{y\mid x}\sqrt{K_1 - K_2}},$$ where $K_1 - K_2$ is the design constant that scales the variance of a single adjusted mean (for a one-way design with $n$ per group, $K_1 - K_2 = 1/n$). Crucially, the studentizer uses only this “between-only” standard error: the extra sampling variability induced by having to estimate the covariate adjustment from random covariates is carried by the distribution of $Q$ itself, not by a per-comparison standard-error correction. This is what distinguishes the procedure from naively applying Tukey's method to adjusted means.

The distribution. Bryant and Paulson (1976) give the exact CDF of $Q_p$ in their Equation (17), a single integral over a variable that combines the $\chi^2_\nu$ error estimate with a random covariate-shrinkage factor $\delta$ that, by their Equations (11)--(12), has a $\mathrm{Beta}((\nu+1)/2,\, p/2)$ distribution. Carrying out the error integral with the studentized-range routine ptukey reduces Equation (17) to the equivalent one-dimensional form $$P(Q_p \le q) \;=\; \int_0^1 \mathrm{ptukey}\!\left(q\sqrt{\delta};\, k,\, \nu\right)\, f_{\mathrm{Beta}}\!\left(\delta;\, \tfrac{\nu+1}{2},\, \tfrac{p}{2}\right) d\delta,$$ which this package evaluates (the reduction is exact; see the source-code comments in R/bryant_paulson.R for the one-line derivation from Bryant and Paulson's p. 634 conditioning argument). When $p = 0$ the factor $\delta$ degenerates at 1 and $Q_p$ is exactly the ordinary studentized range (Bryant and Paulson, 1976, Sec. 1), so the code short-circuits to ptukey. The integral is evaluated with integrate after the change of variables $\delta = 1 - u^2$, which removes the endpoint singularity of the Beta weight at $\delta = 1$ when $p = 1$ and makes the quadrature converge in a few subdivisions; qbryant_paulson inverts it with uniroot. Bryant and Bruvold (1980) later showed the same distribution and critical values remain valid when the covariates are not identically distributed across groups (their grouped-covariate model, Eq. 1.3), and added the Duncan multiple-range extension.

Accuracy at small df. The error integral is carried out with ptukey for $\nu \ge 7$, where it is accurate to about $10^{-9}$. For $\nu < 7$ ptukey's algorithm loses accuracy (at $\nu = 3$, $k = 20$ its probability error reaches $\approx 3\times10^{-4}$, enough to move the critical value by about 0.2, and it is larger at $\nu = 2$), so the studentized-range distribution is instead evaluated directly, without ptukey, by integrating the probability integral of the range against the $\chi^2_\nu$ error density. The small-$\nu$ path costs a fraction of a second.

Validation. The implementation reproduces Bryant and Paulson's (1976) Table 1 exactly, to the two decimal places tabled, over the whole of its range: both tail areas ($\alpha = .05$ and $\alpha = .01$), all three covariate counts ($p = 1, 2, 3$), every tabled number of groups ($k = 2, \ldots, 8, 10, 12, 16, 20$), and every tabled error degrees of freedom ($\nu = 2, \ldots, 8, 10, 12, 14, 16, 18, 20, 24, 30, 40, 60, 120$), which is 1188 critical values in all. The corners of the table are included: $q_{.01;\,1,2,2} = 19.09$, $q_{.01;\,3,20,2} = 73.01$, $q_{.01;\,3,20,3} = 33.13$, and $q_{.05;\,1,6,14} = 4.83$ (the value used in the Bryant and Bruvold, 1980 worked example). Two of the 1188 entries, $q_{.01;\,2,8,3}$ and $q_{.01;\,2,20,4}$, have exact values of 23.165013 and 19.745008, each roughly $10^{-5}$ above the 23.165 and 19.745 half-way points, so they round to 23.17 and 19.75; the 1976 table rounds them down, to 23.16 and 19.74. Both values were confirmed to fourteen significant figures by two independent high-order quadrature engines that share no code with the package implementation. A large-scale simulation of the Bryant and Paulson statistic confirms the computed values independently, and the Bryant and Bruvold (1980) Table 2 Duncan ranges are reproduced as well. See the package tests.

References

Bryant, J. L., & Paulson, A. S. (1976). An extension of Tukey's method of multiple comparisons to experimental designs with random concomitant variables. Biometrika, 63, 631--638. tools:::Rd_expr_doi("10.1093/biomet/63.3.631")

Bryant, J. L., & Bruvold, N. T. (1980). Multiple comparison procedures in the analysis of covariance. Journal of the American Statistical Association, 75(372), 874--880. tools:::Rd_expr_doi("10.2307/2287175")

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge. (See Chapter 9.)

Examples

Run this code

# Critical value from the worked example of Bryant and Bruvold (1980):
# k = 6 panels, p = 1 covariate, nu = 14 error df, alpha = .05. The quantile
# is found by inverting the distribution function with uniroot; it is 4.83,
# the entry in Table 1 of Bryant and Paulson (1976).
qbryant_paulson(0.95, num_covariates = 1, num_groups = 6, df = 14)

# The ordinary Tukey value (ignoring that the covariate is random and
# estimated) is smaller, 4.64, so it yields intervals that are too narrow:
qtukey(0.95, nmeans = 6, df = 14)

# How much too narrow: the Bryant-Paulson area beyond the Tukey value is the
# familywise error rate Tukey's method actually delivers in this design.
# With no covariate the two distributions coincide, so the area is .0499,
# the nominal .05 up to the rounding of 4.64 itself. Each additional random
# covariate carries more estimation uncertainty, stretches the distribution
# to the right, and pushes the rate up, to .063, .076, and .091.
pbryant_paulson(4.64, num_covariates = 0:3, num_groups = 6, df = 14,
                lower_tail = FALSE)

# The p = 0 entry above is exactly the ordinary studentized range:
ptukey(4.64, nmeans = 6, df = 14, lower.tail = FALSE)

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