vdralinear: Vertical Distributed Linear Regression Results Object
In vdra: Vertical Distributed Regression Analysis

Description Arguments See Also

This class of object is returned by the two party, three party, and K-party distributed regression analysis programs when "linear" regression is specified. Objects of this class have methods for the functions print and summary.

The following components must be included in a legitimate vdralinear 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 `NA` values in the vector corresponding to the redudant columns model matrix.
`tvals`	the t-values of the coefficietns.
`secoef`	the vector of the standard error of the coefficients.
`pvals`	the p-values of the coefficients.
`sse`	sum of squared errors.
`rstderr`	residual standard error.
`rsquare`	r squared.
`adjrsquare`	adjusted r squared.
`Fstat`	the F-statistic for the linear regression.
`Fpval`	the p-value of the F-statistic for the linear regression.
`df1`	The numerator degrees of freedom for the F-statistic.
`df2`	The denominator degrees of freedom for the F-statistic.
`n`	the number of observations in the data.
`xtx`	a matrix of the transpose of the covariates times the covarites. Used by `differentModel`.
`xty`	a matrix of the transpose of the covarites times the response. Used by `differentModel`.
`yty`	sum of squares of the reponse. Used by `differentModel`.
`meansy`	the mean of the response. Used by `differentModel`.
`means`	the mean of each covaraite. Used by `differentModel`.