Class CoxR

  • public class CoxR
    extends Object
    This is a port of the R survival code used for doing Cox Regression. The algorithm was a fairly easy port from C code to Java where the challenge was making the code a little more object friendly. In the R code everything is passed around as an array and a large portion of the code is spent extracting data from the array for use in different calculations. By organizing the data in a class for each data point was able to simplify much of the code. Not all variants of different methods that you can select for doing various statistical calculations are implemented. Wouldn't be difficult to go back in add them in if they are important.

    In R you can pass in different paramaters to override defaults which requires parsing of the paramaters. In the Java code tried to be a little more exact in the code related to paramaters where using strata, weighting, robust and cluster are advance options. Additionaly code is implemented from Bob Gray to do variance correction when using weighted paramaters in a data set. /Users/Scooter/NetBeansProjects/biojava3-survival/docs/wtexamples.docx

    The CoxHelper class is meant to hide some of the implementation details.


    • sign in CoxMart?
    • double toler_chol = 1.818989e-12; Different value for some reason
    • In robust linear_predictor set to 0 which implies score = 1 but previous score value doesn't get reset
    Cox regression fit, replacement for coxfit2 in order to be more frugal about memory: specificly that we don't make copies of the input data.

    the input parameters are

          maxiter      :number of iterations
          time(n)      :time of status or censoring for person i
          status(n)    :status for the ith person    1=dead , 0=censored
          covar(nv,n)  :covariates for person i.
                           Note that S sends this in column major order.
          strata(n)    :marks the strata.  Will be 1 if this person is the
                          last one in a strata.  If there are no strata, the
                          vector can be identically zero, since the nth person's
                          value is always assumed to be = to 1.
          offset(n)    :offset for the linear predictor
          weights(n)   :case weights
          init         :initial estimate for the coefficients
          eps          :tolerance for convergence.  Iteration continues until
                          the percent change in loglikelihood is <= eps.
          chol_tol     : tolerance for the Cholesky decompostion
          method       : 0=Breslow, 1=Efron
          doscale      : 0=don't scale the X matrix, 1=scale the X matrix
    returned parameters
          means(nv)    : vector of column means of X
          beta(nv)     :the vector of answers (at start contains initial est)
          u(nv)        :score vector
          imat(nv,nv)  :the variance matrix at beta=final
                         (returned as a vector)
          loglik(2)    :loglik at beta=initial values, at beta=final
          sctest       :the score test at beta=initial
          flag         :success flag  1000  did not converge
                                      1 to nvar: rank of the solution
          iterations         :actual number of iterations used
    work arrays
          a(nvar), a2(nvar)
          cmat(nvar,nvar)       ragged array
          newbeta(nvar)         always contains the "next iteration"
    calls functions: cholesky2, chsolve2, chinv2

    the data must be sorted by ascending time within strata

    Scooter Willis