Diagnostics plots for generalized linear models
Makes plot of jackknife deviance residuals against linear predictor, normal scores plots of standardized deviance residuals, plot of approximate Cook statistics against leverage/(1-leverage), and case plot of Cook statistic.
glm.diag.plots(glmfit, glmdiag = glm.diag(glmfit), subset = NULL,
iden = FALSE, labels = NULL, ret = FALSE)glmfit |
|
glmdiag |
Diagnostics of |
subset |
Subset of |
iden |
A logical argument. If |
labels |
A vector of labels for use with |
ret |
A logical argument indicating if |
The diagnostics required for the plots are calculated by glm.diag. These are
then used to produce the four plots on the current graphics device.
The plot on the top left is a plot of the jackknife deviance residuals against the fitted values.
The plot on the top right is a normal QQ plot of the standardized deviance residuals. The dotted line is the expected line if the standardized residuals are normally distributed, i.e. it is the line with intercept 0 and slope 1.
The bottom two panels are plots of the Cook statistics. On the left is a plot of the Cook statistics against the standardized leverages. In general there will be two dotted lines on this plot. The horizontal line is at 8/(n-2p) where n is the number of observations and p is the number of parameters estimated. Points above this line may be points with high influence on the model. The vertical line is at 2p/(n-2p) and points to the right of this line have high leverage compared to the variance of the raw residual at that point. If all points are below the horizontal line or to the left of the vertical line then the line is not shown.
The final plot again shows the Cook statistic this time plotted against case number enabling us to find which observations are influential.
Use of iden=T is encouraged for proper exploration of these four plots as
a guide to how well the model fits the data and whether certain observations
have an unduly large effect on parameter estimates.
If ret is TRUE then the value of glmdiag is returned otherwise there is
no returned value.
The current device is cleared and four plots are plotted by use of
split.screen(c(2,2)). If iden is TRUE, interactive identification of
points is enabled. All screens are closed, but not cleared, on termination of
the function.
Davison, A. C. and Hinkley, D. V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.
Davison, A.C. and Snell, E.J. (1991) Residuals and diagnostics. In Statistical Theory and Modelling: In Honour of Sir David Cox D.V. Hinkley, N. Reid, and E.J. Snell (editors), 83–106. Chapman and Hall.
# In this example we look at the leukaemia data which was looked at in # Example 7.1 of Davison and Hinkley (1997) data(leuk, package = "MASS") leuk.mod <- glm(time ~ ag-1+log10(wbc), family = Gamma(log), data = leuk) leuk.diag <- glm.diag(leuk.mod) glm.diag.plots(leuk.mod, leuk.diag)
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