MPDiR (version 0.2)

binom.diagnostics: Diagnostics for Binary GLM

Description

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

Usage

binom.diagnostics(obj, nsim = 200, type = "deviance", no.warn = TRUE)
# S3 method for binom.diag
plot(x, alpha = 0.025, breaks = "Sturges", ...)

Value

binom.diagnostics returns a list of class ‘binom.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.

Arguments

obj

Object of class ‘glm’ from a fit to data with a binary response.

nsim

number of bootstrap simulations to run.

type

character indicating type of residuals (defaults to “deviance”) to use in the simulations.

no.warn

logical indicating whether to suppress warnings from glm. Defaults to TRUE.

x

object of class ‘binom.diag’, typically obtained from running binom.diagnostics

alpha

confidence level for envelope of the distribution of residuals

breaks

character indicating algorithm for choosing the breaks in the histogram of runs of residuals. Defaults to “Sturges”. See hist.

...

additional parameters specifications for the empirical cdf plot

Author

Ken Knoblauch

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.

Examples

if (FALSE) {
# generate psychometric function and binary data
levs <-  10^seq(-2, 0, len = 5)
psyF <-  pnorm(levs, mean = 0.2, sd = 0.2)
Ntrials <- 100
Resp <- rbinom(Ntrials * length(psyF), 1, psyF)
d.df <- data.frame(resp = Resp, levs = levs)
fit.glm <-  glm(resp ~ levs, binomial, d.df)
fit.diag <- binom.diagnostics(fit.glm)
plot(fit.diag)
}