jmdem.summaries {jmdem} | R Documentation |
Accessing Joint Mean and Dispersion Effect Model Fits
Description
These functions are all methods
for class jmdem
or summary.jmdem
objects.
Usage
## S3 method for class 'jmdem'
formula(x, submodel = c("both", "mean", "dispersion"), ...)
## S3 method for class 'jmdem'
family(object, submodel = c("both", "mean", "dispersion"), ...)
## S3 method for class 'jmdem'
residuals(object, type = c("deviance", "pearson", "working",
"response", "partial"), ...)
Arguments
x , object |
the function family accesses the family objects which are stored within objects created by |
submodel |
character. The family of the specified submodel. For |
type |
character. For |
... |
further arguments passed to methods. |
Details
family
is a generic function with methods for class "jmdem". See family
for details.
Here formula
is referred to the case that it is called on a fitted jmdem
model object. The default first, depending on the specified submodel
argument, looks for a "mean.formula
" and/or "dispersion.formula
" component of the jmdem
object (and evaluates it), then a "mean.terms
" and/or "dispersion.terms
" component, then a mformula
and/or dformula
parameter of the call (and evaluates its value) and finally a "formula
" attribute.
The references define the types of residuals: Davison & Snell is a good reference for the usages of each.
The partial residuals are a matrix of working residuals, with each column formed by omitting a term from the model.
How residuals
treats cases with missing values in the original fit is determined by the na.action
argument of that fit. If na.action = na.omit
omitted cases will not appear in the residuals, whereas if na.action = na.exclude
they will appear, with residual value NA
. See also naresid
.
For fits done with y = FALSE
the response values are computed from other components.
Author(s)
Karl Wu Ka Yui (karlwuky@suss.edu.sg)
References
Cox, D. R. and Snell, E. J. (1981). Applied Statistics; Principles and Examples. London: Chapman and Hall.
Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.
Davison, A. C. and Snell, E. J. (1991). Residuals and diagnostics. In: Statistical Theory and Modelling. In Honour of Sir David Cox, FRS, eds. Hinkley, D. V., Reid, N. and Snell, E. J., Chapman & Hall.
Dobson, A. J. (1983). An Introduction to Statistical Modelling. London: Chapman and Hall.
Hastie, T. J. and Pregibon, D. (1992). Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
McCullagh P. and Nelder, J. A. (1989). Generalized Linear Models. London: Chapman and Hall.
See Also
jmdem
, anova.jmdem
, coef
, deviance
, df.residual
, effects
, fitted
, weighted.residuals
, residuals
, residuals.jmdem
, summary.jmdem
, weights
.
Examples
## The jmdem(...) example
MyData <- simdata.jmdem.sim(mformula = y ~ x, dformula = ~ z,
mfamily = poisson(),
dfamily = Gamma(link = "log"),
beta.true = c(0.5, 4),
lambda.true = c(2.5, 3), n = 100)
fit <- jmdem(mformula = y ~ x, dformula = ~ z, data = MyData,
mfamily = poisson, dfamily = Gamma(link = "log"),
dev.type = "deviance", method = "CG")
coef(fit)
plot(resid(fit), fitted(fit))
abline(h = 0, lty = 2, col = "gray")