Usage
logistfplot <- function(formula = attr(data, "formula"),
data = sys.parent(), which, pitch = 0.05, limits, alpha = 0.05,
maxit = 25, maxhs = 5, epsilon = 0.0001, maxstep = 10, firth = TRUE, legends = TRUE)Arguments
formula
a formula object, with the response on the left of the operator, and the
model terms on the right. The response must be a vector with 0 and 1 or FALSE and
TRUE for the model outcome, where the higher value (1 or TRUE) is modeled. It's possible
data
a data.frame where the variables named in the formula can be found, i. e.
the variables containing the binary response and the covariates.
which
a righthand formula specifying the plotted parameter, interaction or
general term, e.g. ~ A - 1 or ~ A : C - 1. The profile likelihood of the
intercept would be obtained by the formula ~ - ..
pitch
distances between the interpolated points in standard errors of
the parameter estimate, the default value is 0.05.
limits
vector of the minimum and the maximum on the x-scale in standard
deviations distant form the maximum likelihood. The default values
are the extremes of both confidence intervals, Wald and PL, plus or minus half a
standard deviation of the para
alpha
the significance level (1-$\alpha$ the confidence level,
0.05 as default).
maxit
maximum number of iterations (default value is 25)
maxhs
maximum number of step-halvings per iterations (default value is 5)
epsilon
specifies the maximum allowed change in penalized log likelihood to
declare convergence. Default value is 0.0001.
maxstep
specifies the maximum change of (standardized) parameter values allowed
in one iteration. Default value is 0.5.
firth
use of Firth's penalized maximum likelihood (firth=TRUE, default) or the
standard maximum likelihood method (firth=FALSE) for the logistic regression. Note
that by specifying pl=TRUE and firth=FALSE (and probably a lower number of iterations)
o
legends
if FALSE, legends on the bottom of the plot would be omitted
(default is TRUE).