Method for Profiling glm Objects
Investigates the profile log-likelihood function for a fitted model of
class "glm".
## S3 method for class 'glm'
profile(fitted, which = 1:p, alpha = 0.01, maxsteps = 10,
del = zmax/5, trace = FALSE, ...)fitted |
the original fitted model object. |
which |
the original model parameters which should be profiled. This can be a numeric or character vector. By default, all parameters are profiled. |
alpha |
highest significance level allowed for the profile t-statistics. |
maxsteps |
maximum number of points to be used for profiling each parameter. |
del |
suggested change on the scale of the profile t-statistics. Default value chosen to allow profiling at about 10 parameter values. |
trace |
logical: should the progress of profiling be reported? |
... |
further arguments passed to or from other methods. |
The profile t-statistic is defined as the square root of change in sum-of-squares divided by residual standard error with an appropriate sign.
A list of classes "profile.glm" and "profile" with an
element for each parameter being profiled. The elements are
data-frames with two variables
par.vals |
a matrix of parameter values for each fitted model. |
tau |
the profile t-statistics. |
Originally, D. M. Bates and W. N. Venables. (For S in 1996.)
options(contrasts = c("contr.treatment", "contr.poly"))
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
budworm.lg <- glm(SF ~ sex*ldose, family = binomial)
pr1 <- profile(budworm.lg)
plot(pr1)
pairs(pr1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.