logistfplot {logistf} | R Documentation |
This function plots the penalized profile likelihood for a specified parameter.
logistfplot(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
S 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-\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 5. |
firth |
use of Firth's (1993) penalized maximum likelihood (firth=TRUE , the 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. |
legends |
if FALSE , legends in 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 (2002)). Further documentation can be found in Heinze & Ploner (2004).
The object returned is a simple data.frame containing three columns which allow reproducing the plot. Each row represents one point of the interpolation. The columns are as follows:
std |
distance from the maximum of the profile likelihood (in standard errors of the parameter estimate). |
name |
the value of the parameter for the variable name specified
in argument which . |
loglik.pen |
the value of the penalized likelihood. |
Firth D (1993). Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38.
Heinze G, Schemper M (2002). A solution to the problem of separation in logistic regression. Statistics in Medicine 21: 2409-2419.
Heinze G, Ploner M (2004). Technical Report 2/2004: A SAS-macro, S-PLUS library and R package to perform logistic regression without convergence problems. Section of Clinical Biometrics, Department of Medical Computer Sciences, Medical University of Vienna, Vienna, Austria. http://www.meduniwien.ac.at/user/georg.heinze/techreps/tr2_2004.pdf
logistf, logistftest