summary.vdracox: Summary Method for Vertical Distributed COX Models
In vdra: Vertical Distributed Regression Analysis

Description Usage Arguments Value See Also Examples

Produces a summary of a fitted vdra cox model.

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  ## S3 method for class 'vdracox'
  ## S3 method for class 'vdracox'
summary(object, ...)

`object`	a `vdracox` object.
`...`	futher argumetns passed to or from other methods.

Returns an object of class summary.vdracox. Objects of this class have a method for the function print. The following components must be included in summary.vdracox object.

`failed`	logical value. If `FALSE`, then there was an error processing the data. if `TRUE`, there were no errors.
`converged`	logical value. If `TRUE`, the regression converged. If `FALSE`, it did not.
`party`	a vector which indicates the party from which each covariate came.
`coefficients`	the vector of coefficients. If the model is over-determined, there will be missing values in the vector corresponding to the redudant columns model matrix.
`expcoef`	a vector which represents exp(coefficients).
`secoef`	the vector of the standard error of the coefficients.
`zvals`	the z-values of the coefficients.
`pvals`	the p-values of the coefficients.
`expncoef`	a vector which represents exp(-coefficients).
`lower95`	a vector of the lower bounds of the 95% confidence interval for exp(coefficients).
`upper95`	a vector of the upper bounds of the 95% confidence interval for exp(coefficients).
`n`	the number of observations in the data.
`nevent`	the number of events used in the fit.
`concordance`	a vector containing the number of events which are concordant, discordant, tied.risk, tied.time. Also contains the concordance statistic and its standard error. Calculated using the `survival` package, if installed. If not installed, all values are `NA`.
`rsquare`	a vector containing an r-square value for the fit and its p-value.
`lrt`	a vector contaiing the likelihood ratio test statistic and its p-value.
`df`	the degrees of freedom.
`wald.test`	a vector containg the Wald test statistic and its p-value.
`score`	a vector contining the score test statistic and its p-value.
`iter`	the number of iterations of the cox algorithm before convergence.