ellipse.profile.glm {ellipse} | R Documentation |
This routine approximates a pairwise confidence region for a glm model.
## S3 method for class 'profile.glm' ellipse(x, which = c(1, 2), level = 0.95, t, npoints = 100, dispersion, ...)
x |
An object of class |
which |
Which pair of parameters to use. |
level |
The |
t |
The square root of the value to be contoured. By default, this is |
npoints |
How many points to use in the ellipse. |
dispersion |
If specified, fixed dispersion is assumed, otherwise the dispersion is taken from the model. |
... |
Extra parameters which are not used (for compatibility with the generic). |
This function uses the 4 point approximation to the contour as described in Appendix 6 of Bates and Watts (1988). It produces the exact contour for quadratic surfaces, and good approximations for mild deviations from quadratic. If the surface is multimodal, the algorithm is likely to produce nonsense.
An npoints
x 2
matrix with columns having the chosen parameter names,
which approximates a contour of the function that was profiled.
Bates and Watts (1988) Nonlinear Regression Analysis \& its Applications
## MASS has a pairs.profile function that conflicts with ours, so ## do a little trickery here noMASS <- is.na(match('package:MASS', search())) if (noMASS) require(MASS) ## Dobson (1990) Page 93: Randomized Controlled Trial : counts <- c(18,17,15,20,10,20,25,13,12) outcome <- gl(3,1,9) treatment <- gl(3,3) glm.D93 <- glm(counts ~ outcome + treatment, family=poisson()) ## Plot an approximate 95% confidence region for the two outcome variables prof.D93 <- profile(glm.D93) plot(ellipse(prof.D93, which = 2:3), type = 'l') lines(ellipse(glm.D93, which = 2:3), lty = 2) params <- glm.D93$coefficients points(params[2],params[3]) ## Clean up our trickery if (noMASS) detach('package:MASS')