logistftest {logistf}R Documentation

Penalized likelihood ratio test

Description

This function performs a penalized likelihood ratio test on some (or all) selected factors. The resulting object is of the class logistftest and includes the information printed by the proper print method.

Usage

logistftest(formula=attr(data, "formula"), data=sys.parent(),
  test, values, maxit = 25, maxhs=5, epsilon = .0001,
  maxstep = 10, firth=TRUE, beta0)

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 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.
test righthand formula of parameters to test (e.g. ~ B + D - 1). As default all parameter apart from the intercept are tested. If the formula includes -1, the intercept is omitted from testing. As alternative to the formula one can give the indexes of the ordered effects to test (a vector of integers). To test only the intercept specify test = ~ - . or test = 1.
values null hypothesis values, default values are 0. For testing the specific hypothesis B1=1, B4=2, B5=0 we specify test= ~ B1 + B4 + B5 - 1 and values=c(1, 2, 0).
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, 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.
beta0 specifies the initial values of the coefficients for the fitting algorithm.

Details

This function performs a penalized likelihood ratio test on some (or all) selected factors. The resulting object is of the class logistftest and includes the information printed by the proper print method. Further documentation can be found in Heinze & Ploner (2004).

Value

The object returned is of the class logistf and has the following attributes:
testcov a vector of the fixed values of each covariate; NA stands for a parameter which is not tested.
loglik a vector of the (penalized) log-likelihood of the full and the restricted models. If the argument beta0 not missing, the full model isn't evaluated.
df: the number of degrees of freedom in the model.
prob the p-value of the test.
call the call object
method depending on the fitting method ‘Penalized ML’ or ‘Standard ML’.
beta the coefficients on the restricted solution.

References

Firth D (1993). Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38.

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

See Also

logistf, logistfplot

Examples

data(sex2)
logistftest(case ~ age+oc+vic+vicl+vis+dia,  sex2,
            test = ~ vic + vicl - 1, values = c(2, 0))

[Package logistf version 1.06 Index]