Reports the residual correlation edge(s) between two responses — the
within-observation coupling rho12 that remains after each response's mean and
scale (class 2 in docs/design/07-bivariate-covariance-edges.md). For an
unfitted drm_pair() it is the declared edge (estimate is NA). For a
drm_sem() that holds a joint bivariate drmTMB fit it is the Wald table of
rho12 coefficients: intercept and any rho12 ~ x predictors, with standard
error and p-value, on the tanh (engine: atanh_guarded) link. Distinct from
corpairs() (higher-level random-effect correlations) and kept out of
paths() except for directed x -> rho12 paths.
Usage
rho12(object, ...)
# S3 method for class 'drm_pair'
rho12(object, ...)
# S3 method for class 'drm_sem'
rho12(object, ...)Value
A drm_rho12 data frame: y1, y2, term, estimate,
std.error, statistic, p.value, link, predictors, constant.
References
Shipley B (2016). Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations and Causal Inference with R, 2nd edition. Cambridge University Press, Cambridge.
Bollen KA (1989). Structural Equations with Latent Variables. Wiley, New York.
Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug HJ, Maechler M, Bolker BM (2017). “glmmTMB Balances Speed and Flexibility Among Packages for Zero-Inflated Generalized Linear Mixed Models.” The R Journal, 9(2), 378–400. doi:10.32614/RJ-2017-066 .
Examples
rho12(drm_pair(activity ~ x, boldness ~ x, rho12 = ~ x))
#> <residual correlation (rho12): 1 edge>
#> y1 y2 term estimate std.error statistic p.value link predictors
#> activity boldness <NA> NA NA NA NA tanh x
#> constant
#> FALSE
#> estimate NA: declaration only; fit the pair with drm_sem() or pass a bivariate
#> drmTMB fit to drm_psem().