profile.glm {MASS} | R Documentation |
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)