twinSIR_profile.Rd
Function to compute estimated and profile likelihood based confidence
intervals. Computations might be cumbersome!
There is a simple plot
-method for the result.
# S3 method for twinSIR
profile(fitted, profile, alpha = 0.05,
control = list(fnscale = -1, factr = 10, maxit = 100), ...)
an object of class "twinSIR"
.
a list with elements being numeric vectors of length 4. These vectors must
have the form c(index, lower, upper, gridsize)
.
index
:index of the parameter to be profiled in the vector coef(fitted)
.
lower, upper
:lower/upper limit of the grid on which the profile log-likelihood is
evaluated. Can also be NA
in which case lower/upper
equals
the lower/upper bound of the respective 0.3 % Wald confidence interval
(+-3*se).
gridsize
:grid size of the equally spaced grid between lower and upper. Can also be 0 in which case the profile log-likelihood for this parameter is not evaluated on a grid.
\((1-\alpha) 100\%\) profile likelihood based confidence
intervals are computed. If alpha <= 0
, then no confidence intervals are
computed.
control object to use in optim
for the profile log-likelihood
computations.
unused (argument of the generic).
a list with profile log-likelihood evaluations on the grid and highest likelihood
and Wald confidence intervals. The argument profile
is also returned.
The result has class "profile.twinSIR"
, for which a simple (undocumented)
plot
-method is available.
Michael Höhle and Sebastian Meyer
data("hagelloch")
fit <- twinSIR(~ household, data = hagelloch)
gridsize <- if (interactive()) 35 else 5 # for fast tests
prof <- profile(fit, list(c(1, NA, NA, gridsize)))
prof$ci.hl
plot(prof)