coxme                package:kinship                R Documentation

_F_i_t _a _m_i_x_e_d-_e_f_f_e_c_t_s _C_o_x _m_o_d_e_l

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

     Returns an object of class 'coxme' representing the fitted model.

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

     coxme(fixed, data=parent.frame(), random, 
        weights, subset, na.action, init, control, 
        ties=c("efron", "breslow", "exact"), singular.ok=T, 
        varlist, variance, vinit=.2, sparse=c(50, .02), rescale=T, pdcheck=T, x=F, y=T, shortlabel=T,...)

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

   fixed: formula decribing the fixed effects part of the model. 

    data: a data frame containing the variables. 

  random: a one-sided formula describing the random effects part of the
          model. 

 weights: case weights for each observation 

  subset: an expression describing the subset of the data that should
          be used in the fit. 

na.action : a function giving the default action on encountering
          missing values.   It is more usual to use the global
          na.action system option to control this. 

    init: initial values for the coefficients for the fixed portion of
          the model, or  the frailties followed by the fixed effect
          coefficients. 

 control: the result of a call to 'coxme.control' 

    ties: the approximation to be used for tied death times: either
          "efron" or "breslow" 

singular.ok : if TRUE, then redundant coefficients among the fixed
          effects are set to NA, if FALSE the program will fail with an
          error message if there are redundant variables. 

 varlist: variance specifications, often of class 'bdsmatrix',
          decsribing the variance/covariance structure of one or more
          of the random effects.   

variance: fixed values for the variances of selected random effects.
          Values of 0 indicate that the final value should be solved
          for. 

   vinit: vector of initial values for variance terms. It is necessary
          that the initial variance matrix be symmetric positive
          definite.  Normally, a simple sum of the 'varlist' matrices
          will suffice, i.e., a vector of 1s; but not always. 

  sparse: determines which levels of random effects factor variables,
          if any,  for which the program will use sparse matrix
          techniques. If a grouping variable has less than sparse[1]
          levels, then sparse methods are not used for that variable.
          If it has greater than or equal to sparse[1] unique levels,
          sparse methods will be used for those values which represent
          less than sparse[2] as a  proportion of the data.  For
          instance, if a grouping variable has 4000 levels, but 40% of
          the subjects are in group 1 then 3999 of the levels will be
          represented sparsely in the variance matrix.  A single
          logical value of F is equivalent to setting sparse[1] to
          infinity. 

 rescale: scale any user supplied variance matrices so as to have a
          diagonal of 1.0. 

 pdcheck: verify that any user-supplied variance matrix is positive
          definite (SPD). It has been observed that IBD matrices
          produced by some software are not strictly SPD.  Sometimes
          models with these matrices still work (throughout the
          iteration path, the weighted sum of variance matrices was
          always SPD) and sometimes they don't.  In the latter case,
          messages about taking the log of negative numbers will occur,
          and the results of the fit are not necessarily trustworthy. 

       x: retain the X matrix in the output. 

       y: retain the dependent variable (a Surv object) in the output. 

shortlabel: no comment(s) 

     ...: any other arguments

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

     an object of class coxme

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

     'coxph'

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

     ## Not run: 
     coxme(Surv(time, status) ~ rx, data=rats, random= ~1|litter)

     Cox mixed-effects kinship model fit by maximum likelihood
       Data: rats 
       n= 150 
                         NULL Integrated Penalized 
     Log-likelihood -185.6556   -180.849  -173.774

       Penalized loglik: chisq= 23.76 on 13.17 degrees of freedom, p= 0.036 
      Integrated loglik: chisq= 9.61 on 2 degrees of freedom, p= 0.0082 

     Fixed effects: Surv(time, status) ~ rx 
             coef exp(coef)  se(coef)        z         p 
     rx 0.9132825  2.492491 0.3226856 2.830255 0.0046511

     Random effects:  ~ 1 | litter 
                  litter 
     Variance: 0.4255484
     ## End(Not run)

