When a column is not used for its corresponding row, the column contains a value of zero (0). This is a description of the columns that appear in the output table:
|attribute||The index of each predictor, starting from 0.|
|predictor||The column name for each predictor that was supplied as input to the function.|
|category||The category names of each predictor. Numeric predictors have NULL values in this column.|
|estimate||The mean of the supplied values for each predictor.|
|std_error||Standard deviation of the mean for each predictor (standard error).|
|z_score or t_score||If the Family argument specifies the BINOMIAL, LOGISTIC, POISSON, GAMMA, INVERSE_GAUSSIAN, or INVERSE_BINOMIAL family, the name of the column is z_score.
The z-score is a measure of the likelihood that the NULL hypothesis is true, given this sample. It is derived by taking the difference between the observed sample mean and the hypothesized mean, divided by the standard error. The z-score statistic follows the N(0,1) distribution.
If the Family argument specifies the GAUSSIAN family, the name of the column is t_score. The t_score statistic follows a t(N-p-1) distribution.
|p_value||The significance level (p-value) for each predictor.|
|significance||The likelihood that the predictor is significant (see Output in the function: CoxPH).|
The output includes a row for each of the following with a value for estimated value, standard error, z-score, p-value, and significance:
|Loglik||The log likelihood of the model.|
|Intercept||The value of the logit (Y) when all predictors are 0.|
|Predictors||A row for each predictor value (X1,X2,...,Xp). Each numeric input column corresponds to one predictor.|