binom.diagnostics function

Diagnostics for Binary GLM

Two techniques for evaluating the adequacy of the binary glm model used in mlds, based on code in Wood (2006).

binom.diagnostics(obj, nsim = 200, type = "deviance", no.warn = TRUE)

## S3 method for class 'mlds.diag'
plot(x, alpha = 0.025, breaks = "Sturges", ...)

Arguments

• obj: list of class mlds typically generated by a call to the mlds
• 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
• no.warn: logical indicating when TRUE (default) to suppress warnings from glm
• x: list of class mlds.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

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

Returns

binom.diagnostics returns a list of class mlds.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.

References

Wood, SN Generalized Additive Models: An Introduction with R, Chapman & Hall/CRC, 2006

Knoblauch, K. and Maloney, L. T. (2008) MLDS: Maximum likelihood difference scaling in R. Journal of Statistical Software, 25:2 , 1--26, tools:::Rd_expr_doi("10.18637/jss.v025.i02") .

Author(s)

Ken Knoblauch

See Also

mlds

Examples

## Not run:

data(kk1)
kk1.mlds <- mlds(kk1)
kk1.diag <- binom.diagnostics(kk1.mlds)
plot(kk1.diag)
## End(Not run)