MLCM (version 0.3)

binom.diagnostics: 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 mlcm

nsim

integer giving the number of sets of data to simulate

type

character indicating type of residuals. Default is deviance residuals. See residuals.glm for other choices

x

list of class mlcm.diag typically generated by a call to binom.diagnostics

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 hist for more details.

...

additional parameters specifications for the empirical cdf plot

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.

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

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

mlcm

Examples

data(BumpyGlossy)
bg.mlcm <- mlcm(BumpyGlossy)
bg.diag <- binom.diagnostics(bg.mlcm)
plot(bg.diag)