logistftest {logistf}R Documentation

Bias-reduced logistic regression

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 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.
test righthand formula of parameters to test (e.g. ~ B + D - 1). As default all parameter apart from the intercept are tested. If -1 is not included in the formula, the intercept would be tested, too! 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 1 = 1,4 = 2,5 = 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 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.
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.

References

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

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.

Heinze G, Ploner M (2003). Fixing the nonconvergence bug in logistic regression with SPLUS and SAS. Computer Methods and Programs in Biomedicine 71: 181-187.

Ploner, M. (2001). Technical Report 2/2001: An SPLUS library to perform logistic regression without convergence problems. Section of Clinical Biometrics, Department of Medical Computer Sciences, University of Vienna, Vienna.

See Also

logistf, logistfplot

Examples

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

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