logistfplot {logistf} | R Documentation |
This function plots the penalized profile likelihood for a specified parameter.
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)
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
to include contrasts, interactions, nested effects, cubic or polynomial splines and all
the S-PLUS features, as well, e.g. Y ~ X1^*X2 + ns(X3, df=4) .
|
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 parameter, respectively. |
alpha |
the significance level (1-α 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) one obtains profile likelihood confidence intervals for maximum likelihood logistic regression parameters. |
β0 |
|
legends |
if FALSE, legends on the bottom of the plot would be omitted (default is TRUE). |
This function plots the profile likelihood of a specific parameter based on the penalized likelihood. A symmetric shape of the profile penalized log likelihood (PPL) function allows use of Wald intervals, while an asymmetric shape demands profile penalized likelihood intervals (Heinze & Schemper (2001)).
Heinze G (1999). Technical Report 10: The application of Firth's procedure to Cox and logistic regression. Department of Medical Computer Sciences, Section of Clinical Biometrics, Vienna University, Vienna.
Heinze G, Schemper M (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine 21: 2409-2419.
logistf, logistftest