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forward (version 1.0.3)

plot.fwdglm: Forward Search in Generalized Linear Models

Description

This function plots the results of a forward search analysis in generalized linear models.

Usage

"plot"(x, which.plots = 1:11, squared = FALSE, scaled =FALSE,  ylim = NULL, xlim = NULL, th.Res = 4, th.Lev = 0.25, sig.Tst =2.58,  sig.score = 1.96, plot.pf = FALSE, labels.in.plot = TRUE, ...)

Arguments

x
a "fwdglm" object.
which.plots
select which plots to draw, by default all. Each graph is addressed by an integer:
  1. leverages
  2. maximum deviance residuals
  3. minimum deviance residuals
  4. coefficients
  5. t statistics, i.e. coef.est/SE(coef.est)
  6. likelihood matrix: deviance, deviance explained, pseudo R-squared, dispersion parameter
  7. score statistic for the goodness of link test
  8. forward Cook's distances
  9. modified forward Cook's distances
  10. weights used at each step of the forward search for the units included

squared
logical, if TRUE plots squared deviance residuals.
scaled
logical, if TRUE plots scaled coefficient estimates.
ylim
a two component vector for the min and max of the y axis.
xlim
a two component vector for the min and max of the x axis.
th.Res
numerical, a threshold for labelling the residuals.
th.Lev
numerical, a threshold for labelling the leverages.
sig.Tst
numerical, a value used to draw the confidence interval on the plot of the t statistics.
sig.score
numerical, a value used to draw the confidence interval on the plot of the score test statistic.
plot.pf
logical, in case of binary response if TRUE graphs contain all the step of the forward search, otherwise only those in which there is no perfect fit.
labels.in.plot
logical, if TRUE units are labelled in the plots when required.
...
further arguments passed to or from other methods.

References

Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer, Chapter 6.

See Also

fwdglm, fwdlm, fwdsco.

Examples

Run this code
## Not run: data(cellular)
## Not run: mod <- fwdglm(y ~ as.factor(TNF) + as.factor(IFN), data=cellular, 
#               family=poisson(log), nsamp=200)## End(Not run)
## Not run: summary(mod)
## Not run: plot(mod)

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