Orthonormal Contrast Matrices for Bayesian Estimation
Returns a design or model matrix of orthonormal contrasts such that the
marginal prior on all effects is identical. Implementation from Singmann &
Gronau's bfrms
, following
the description in Rouder, Morey, Speckman, & Province (2012, p. 363).
Though using this factor coding scheme might obscure the interpretation of
parameters, it is essential for correct estimation of Bayes factors for
contrasts and order restrictions of multi-level factors (where 'k>2'). See
info on specifying correct priors for factors with more than 2 levels in
the
Bayes factors vignette.
contr.orthonorm(n, contrasts = TRUE, sparse = FALSE)
When 'contrasts = FALSE', the returned contrasts are equivalent to 'contr.treatment(, contrasts = FALSE)', as suggested by McElreath (also known as one-hot encoding).
A matrix
with n rows and k columns, with k=n-1 if contrasts is
TRUE
and k=n if contrasts is FALSE
.
- McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press.
- Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. *Journal of Mathematical Psychology*, 56(5), 356-374. https://doi.org/10.1016/j.jmp.2012.08.001
contr.orthonorm(2) # Q_2 in Rouder et al. (2012, p. 363) contr.orthonorm(5) # equivalent to Q_5 in Rouder et al. (2012, p. 363) ## check decomposition Q3 <- contr.orthonorm(3) Q3 %*% t(Q3) ## 2/3 on diagonal and -1/3 on off-diagonal elements
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