binom.diagnostics {MLCM} | R Documentation |
Diagnostics for Binary GLM
Description
Two techniques for evaluating the adequacy of the binary glm model used in mlcm
, based on code in Wood (2006).
Usage
binom.diagnostics(obj, nsim = 200, type = "deviance")
## S3 method for class 'mlcm.diag'
plot(x, alpha = 0.025, breaks = "Sturges", ...)
Arguments
obj |
list of class ‘mlcm’ typically generated by a call to the |
nsim |
integer giving the number of sets of data to simulate |
type |
character indicating type of residuals. Default is deviance residuals. See |
x |
list of class ‘mlcm.diag’ typically generated by a call to |
alpha |
numeric between 0 and 1, the envelope limits for the cdf of the deviance residuals |
breaks |
character or numeric indicating either the method for calculating the number of breaks or the suggested number of breaks to employ. See |
... |
additional parameters specifications for the empirical cdf plot |
Details
Wood (2006) describes two diagnostics of the adequacy of a binary glm model based on analyses of residuals (see, p. 115, Exercise 2 and his solution on pp 346-347). The first one compares the empirical cdf of the deviance residuals to a bootstrapped confidence envelope of the curve. The second examines the number of runs in the sorted residuals with those expected on the basis of independence in the residuals, again using a resampling based on the models fitted values. The plot method generates two graphs, the first being the empirical cdf and the envelope. The second is a histogram of the number of runs from the bootstrap procedure with the observed number indicated by a vertical line. Currently, this only works if the ‘glm’ method is used to perform the fit and not the ‘optim’ method
Value
binom.diagnostics
returns a list of class ‘mlcm.diag’ with components
NumRuns |
integer vector giving the number of runs obtained for each simulation |
resid |
numeric matrix giving the sorted deviance residuals in each column from each simulation |
Obs.resid |
numeric vector of the sorted observed deviance residuals |
ObsRuns |
integer giving the observed number of runs in the sorted deviance residuals |
p |
numeric giving the proportion of runs in the simulation less than the observed value. |
Author(s)
Ken Knoblauch
References
Wood, SN Generalized Additive Models: An Introduction with R, Chapman \& Hall/CRC, 2006
Ho, Y. H., Landy. M. S. and Maloney, L. T. (2008). Conjoint measurement of gloss and surface texture. Psychological Science, 19, 196–204.
See Also
Examples
## Not run:
data(BumpyGlossy)
bg.mlcm <- mlcm(BumpyGlossy)
bg.diag <- binom.diagnostics(bg.mlcm)
plot(bg.diag)
## End(Not run)