| residuals.gllvm {gllvm} | R Documentation |
Dunn-Smyth residuals for gllvm model
Description
Calculates Dunn-Smyth residuals for gllvm model.
Usage
## S3 method for class 'gllvm'
residuals(object, ...)
Arguments
object |
an object of class 'gllvm'. |
... |
not used. |
Details
Computes Dunn-Smyth residuals (randomized quantile residuals, Dunn and Smyth, 1996) for gllvm model.
For the observation Y_{ij} Dunn-Smyth residuals are defined as
r_{ij}=\Phi^{-1}(u_{ij}F_{ij}(y_{ij}) + (1-u_{ij})F_{ij}^-(y_{ij})),
where \Phi(.) and F_{ij}(.) are the cumulative probability functions of the standard normal
distribution, F_{ij}^-(y)) is the limit as F_{ij}(y) is approached from the negative side, and u_{ij} has been
generated at random from the standard uniform distribution.
Value
residuals |
matrix of residuals |
linpred |
matrix of linear predictors |
Author(s)
Jenni Niku <jenni.m.e.niku@jyu.fi>
References
Dunn, P. K., and Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5, 236-244.
Hui, F. K. C., Taskinen, S., Pledger, S., Foster, S. D., and Warton, D. I. (2015). Model-based approaches to unconstrained ordination. Methods in Ecology and Evolution, 6:399-411.
Examples
## Not run:
# Load a dataset from the mvabund package
data(antTraits)
y <- as.matrix(antTraits$abund)
# Fit gllvm model
fit <- gllvm(y = y, family = poisson())
# residuals
res <- residuals(fit)
## End(Not run)