Create data structure to contain the sensitivity analysis result for Sandia Thermal Problem

[damar-wicaksono]

A possible complication might arise from the fact that there are two layers of output in a Principal component model.

The first is the principal component score metamodeling error using the usual metric, Q2 and RMSE. The second is the reconstruction error from using a truncated principal component coupled with the score predicted by the metamodel. The error here will be more intuitive.

[damar-wicaksono]

The data frame aggregates the results from all experimental configuration in the challenge problem to one observation for a given number of training samples, regression formula, design experiment, covariance and transformation.

err_sandia <- data.frame(pc1_q2 = c(), pc1_rmse = c(),
                         pc2_q2 = c(), pc2_rmse = c(),
                         pc3_q2 = c(), pc3_rmse = c(),
                         rec_err = c(),
                         cov_type = c(),
                         doe = c(),
                         reg = c(),
                         n = c(),
                         transf_name = c())