binom.diagnostics {MLDS} | R Documentation |
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") ## S3 method for class 'mlds.diag': plot(x, alpha = 0.025, breaks = "Sturges", ...)
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 |
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 |
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.
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. |
Ken Knoblauch
Wood, SN Generalized Additive Models: An Introduction with R, Chapman & Hall/CRC, 2006
## Not run: data(kk1) kk1.mlds <- mlds(kk1) kk1.diag <- binom.diagnostics(kk1.mlds) plot(kk1.diag) ## End(Not run)