logistfplot             package:logistf             R Documentation

_P_l_o_t _p_e_n_a_l_i_z_e_d _p_r_o_f_i_l_e _l_i_k_e_l_i_h_o_o_d

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

     This function plots the penalized profile likelihood for a
     specified parameter.

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

     logistfplot <- function(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)

_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. 

   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 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: 

 legends: if FALSE, legends on the bottom of the plot would be omitted
          (default is TRUE).

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

     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 (2001)).

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

     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.

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

     logistf, logistftest

