logistftest             package:logistf             R Documentation

_B_i_a_s-_r_e_d_u_c_e_d _l_o_g_i_s_t_i_c _r_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

 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.}

_D_e_t_a_i_l_s:

     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.

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

     logistf, logistfplot

_E_x_a_m_p_l_e_s:

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

