regions_of_significance: Regions of Significance for a Covariate by Group Interaction

Description

Finds the values of a covariate at which two groups differ significantly when the within-group regression slopes are not equal, that is, when there is a covariate-by-group interaction (heterogeneity of regression). With more than two groups the calculation is carried out for every pair of groups.

Usage

regions_of_significance(
  object,
  data = NULL,
  conf_level = 0.95,
  method = c("simultaneous", "pointwise")
)

Value

A data.frame (class dmar_tbl) with one row per reported quantity per pair of groups and columns pair,

term, and a numeric value. The terms, for each pair, are

lower_bound, upper_bound: The boundaries of the region, sorted, on the scale of the covariate. NA when a boundary does not exist; when there is a single boundary it is reported as lower_bound and upper_bound is NA. Read them with region_code: the boundaries are the covariate values at which the group difference sits exactly on the critical value, and it is region_code that says on which side of them the groups differ.
region_code: How to read the boundaries. 1: two boundaries, the groups differ outside them. 2: two boundaries, the groups differ between them. 3: no boundary, the groups differ at every covariate value. 4: no boundary, the groups differ at no covariate value. 5 and 6: one boundary, the groups differ above it (5) or below it (6).
n_boundaries: How many real boundaries exist: 0, 1, or 2.
lower_bound_in_range, upper_bound_in_range: 1 when the boundary falls inside the covariate values actually observed in the two groups, 0 when it falls outside them, NA when the boundary does not exist. A boundary outside the observed range is an extrapolation of the fitted lines and should not be interpreted as a covariate value at which anything was or could be seen.
difference_intercept, difference_slope: The intercept $d_0$ and slope $d_1$ of the group difference as a function of the covariate, so that the estimated difference at covariate value $x$ is $d_0 + d_1 x$.
critical_value: The critical value used, $t_{crit}$.
df_error: Error degrees of freedom of the fitted model.
conf_level: The confidence level.

Everything that is not a number is carried on attributes rather than forced into value: method, outcome,

covariate, group, the overall observed

covariate_range, a pairs

data.frame with the group labels, the per-pair observed covariate range, and a plain-language region string for each pair, and a

geometry

data.frame holding $d_0$, $d_1$, and the three elements of their covariance matrix, which is what

plot_regions_of_significance draws.

Arguments

object: Either a fitted lm or aov of the form y ~ x * group, or a model formula of that form. When a formula is supplied, data must be supplied too and the model is fit with lm.
data: A data.frame holding the variables in the formula. Used, and required, only when object is a formula; ignored with a warning when object is already a fitted model.
conf_level: Confidence level for the boundaries. Default 0.95.
method: Character string naming the critical value. "simultaneous" (the default) uses Potthoff's (1964) simultaneous critical value $\sqrt{2 F_{1 - \alpha; 2, \nu}}$, which holds the error rate over the whole covariate continuum at once. "pointwise" uses the classic critical value $t_{1 - \alpha/2; \nu}$, which holds it at one covariate value chosen in advance.

Author

Ken Kelley [email protected]

Details

Why the procedure exists. An analysis of covariance that assumes a common within-group slope reports one adjusted mean difference, and that single number is a complete summary of the group comparison only if the slopes really are common. When the covariate interacts with the group factor the slopes are not common, the two fitted lines converge or cross, and there is no such thing as “the” treatment effect: the difference between the groups depends on where along the covariate you look. Reporting the adjusted mean difference anyway reports the difference at one covariate value, the covariate grand mean, and says nothing about the rest of the range. The question worth answering is instead where on the covariate the groups differ, and that is what this function answers.

The calculation. For two groups, write the estimated difference at covariate value $x$ as the line $$\hat D(x) = \hat d_0 + \hat d_1 x,$$ where $\hat d_0$ is the difference in intercepts and $\hat d_1$ the difference in slopes. Because $\hat D(x)$ is a linear combination of the regression coefficients, its sampling variance follows from their covariance matrix, $$\mathrm{Var}[\hat D(x)] = \mathrm{Var}(\hat d_0) + 2 x \, \mathrm{Cov}(\hat d_0, \hat d_1) + x^2 \mathrm{Var}(\hat d_1).$$ The groups differ significantly at $x$ exactly when $\hat D(x)^2 > t_{crit}^2 \, \mathrm{Var}[\hat D(x)]$. Setting the two sides equal gives a quadratic in $x$, $$(\hat d_1^2 - t_{crit}^2 \mathrm{Var}(\hat d_1)) x^2 + 2(\hat d_0 \hat d_1 - t_{crit}^2 \mathrm{Cov}(\hat d_0, \hat d_1)) x + (\hat d_0^2 - t_{crit}^2 \mathrm{Var}(\hat d_0)) = 0,$$ whose real roots are the boundaries of the region of significance.

Every geometry is possible, and the leading coefficient decides which. The coefficient on $x^2$ is positive exactly when the slope difference itself clears the critical value. When it is positive the parabola opens upward and the groups differ outside the two boundaries, the familiar picture of two lines that cross somewhere in the middle of the covariate and separate at both ends. When it is negative the parabola opens downward and the groups differ between the boundaries, a middle band of covariate values where the two lines are far enough apart relative to the precision available there. When there are no real roots the sign never changes, so the groups differ either everywhere or nowhere. All of these are reported through region_code rather than being treated as failures.

Of those, “everywhere” is a case the code enumerates but the mathematics rules out whenever the slopes genuinely differ. The estimated difference $\hat D(x)$ is then a nonconstant line in $x$, so it crosses zero at some covariate value, and at that value the squared difference is zero while the critical bound is positive. The quadratic therefore always has two real roots, and the significant set is the pair of tails outside them or the band between them, never the whole line. Note this is a statement about the covariate axis extended without limit, not about the observed data: within the range actually observed the groups may well differ everywhere, which is why the next paragraph matters.

Boundaries outside the observed data. A boundary is a root of an equation, and the equation is happy to place it far outside the covariate values that were actually observed. Such a boundary is an extrapolation of two fitted lines into a region where there are no data to support them, and it should not be read as a covariate value at which anything can be claimed. The boundary is still reported, since suppressing it would hide the shape of the result, but it is flagged in lower_bound_in_range and upper_bound_in_range and noted in the region string.

Simultaneous versus pointwise. The classic critical value is $t_{1 - \alpha/2}$, which controls the Type I error rate at a single covariate value fixed in advance. That is not what anyone actually does: the whole point of the procedure is to scan the covariate continuum and read off where the groups differ, which is a search over infinitely many tests. Potthoff (1964) gave the simultaneous critical value $\sqrt{2 F_{1 - \alpha; 2, \nu}}$, which holds the error rate over the entire covariate range at once and is therefore the default here, and the value used in Maxwell, Delaney, and Kelley (2027, Chapter 9). The simultaneous critical value is always the larger of the two, so its region of significance is always the more conservative one. With more than two groups the simultaneous guarantee applies to each pair over the covariate range, not to the family of pairs. For a Bonferroni protection across the pairs, divide the Type I error rate by the number of pairs before choosing conf_level: with three groups (three pairs) and a familywise rate of .05, pass conf_level = 1 - .05 / 3.

What the model may contain. The model must contain exactly one interaction between a numeric covariate and a grouping factor, and the grouping factor may not appear in any other term. Additional predictors that do not interact with the group are allowed and drop out of the group difference, so y ~ x * group + block is fine while y ~ x * group * block is not. A grouping variable stored as a number (0/1, say) is a numeric predictor to R, not a factor, so convert it with factor first.

References

Johnson, P. O., & Neyman, J. (1936). Tests of certain linear hypotheses and their application to some educational problems. Statistical Research Memoirs, 1, 57--93.

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

Potthoff, R. F. (1964). On the Johnson-Neyman technique and some extensions thereof. Psychometrika, 29(3), 241--256. tools:::Rd_expr_doi("10.1007/BF02289721")

Rogosa, D. (1980). Comparing nonparallel regression lines. Psychological Bulletin, 88(2), 307--321. tools:::Rd_expr_doi("10.1037/0033-2909.88.2.307")

Examples

Run this code

# ---- The Pygmalion teacher-expectancy data ----
# Post-test IQ, averaged over the two follow-up assessments, on
# pretest IQ, separately by condition. The slopes differ, so the
# expectancy effect depends on where the child started.
data(pygmalion)
pygmalion$iq_post <- (pygmalion$iq_4 + pygmalion$iq_8) / 2
fit <- lm(iq_post ~ iq_pre * treatment, data = pygmalion)

regions_of_significance(fit)

# The plain-language reading of each pair is on an attribute.
attr(regions_of_significance(fit), "pairs")$region

# The pointwise (classic) critical value gives a wider region,
# because it does not pay for scanning the whole covariate.
regions_of_significance(fit, method = "pointwise")

# ---- Formula interface, and more than two groups ----
set.seed(113)
n <- 150
g <- factor(rep(c("control", "low", "high"), each = n / 3))
x <- rnorm(n, 50, 10)
y <- 2 + 0.5 * x + (g == "high") * (0.4 * x - 15) + rnorm(n, 0, 5)
d <- data.frame(y, x, g)
regions_of_significance(y ~ x * g, data = d)

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