Models the counts across two or more mutually exclusive categories from a
fixed number of trials, in long format: one row per category per
trial, with group identifying which rows belong to the same trial (see
log_lik_multinom() for details). res_multinom() returns one residual
per row, not one per trial, since a trial's categories aren't
independent and so have no single meaningful residual as a whole; the
classic per-trial deviance statistic can be recovered by summing the
squared type = "dev" residuals within a group.
Arguments
- x
A non-negative whole numeric vector of the category counts.
- size
A non-negative whole numeric vector of the number of trials.
- prob
A numeric vector of the probability of the category. Must sum to 1 across the rows sharing the same
group.- group
A vector identifying which rows belong to the same multinomial trial (whose
xvalues sum tosizeandprobvalues sum to 1). Every group must have at least 2 rows and the same number of rows as the rest of the data (a fixed set of categories, as in multinomial logistic regression), and must not containNA.- type
A string of the residual type. 'raw' for raw residuals 'dev' for deviance residuals and 'data' for the data.
- simulate
A flag specifying whether to simulate residuals.
Details
group is validated (same size, prob summing to 1, no singleton or
short groups, no NA) regardless of simulate, but is only otherwise
used when simulate = TRUE, to draw a joint, correlation-preserving
replicate per trial (via ran_multinom()) rather than simulating each
category independently, which requires res_multinom() to see every row
of a group in the same call.
References
Haberman, S.J. 1973. The analysis of residuals in cross-classified tables. Biometrics 29(1): 205-220. doi:10.2307/2529686 .
Pierce, D.A., and Schafer, D.W. 1986. Residuals in generalized linear models. Journal of the American Statistical Association 81(396): 977-986. doi:10.1080/01621459.1986.10478361 .
Gelman, A., Meng, X.-L., and Stern, H. 1996. Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica 6(4): 733-807.