logistftest             package:logistf             R Documentation

_P_e_n_a_l_i_z_e_d _l_i_k_e_l_i_h_o_o_d _r_a_t_i_o _t_e_s_t

_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 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.

_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.  Further documentation can be found in Heinze &
     Ploner (2004).

_V_a_l_u_e:

     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.

_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, 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. <URL:
     http://www.meduniwien.ac.at/user/georg.heinze/techreps/tr2_2004.pd
     f>

_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))

