ANOVA summary of the impact of the importance sampling variants on the cost p=0.050 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 688.10 172.02 18.980 1.306e-13 *** Residuals 245 2220.50 9.06 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 3.9e-08 - - - sb 1.6e-12 9.1e-05 - - wb 4.3e-09 0.0352 1.0000 - pb 8.4e-12 0.0017 0.2121 1.0000 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 3.1e-07 - - - sb 9.3e-09 1.0e-06 - - wb 8.8e-08 9.9e-05 0.46 - pb 1.1e-08 9.9e-05 0.16 0.46 P value adjustment method: holm p=0.075 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 364.96 91.24 9.2009 6.092e-07 *** Residuals 245 2429.48 9.92 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 5.1e-05 - - - sb 2.5e-05 1 - - wb 3.3e-05 1 1 - pb 7.6e-05 1 1 1 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 1.8e-06 - - - sb 1.4e-07 1 - - wb 1.8e-06 1 1 - pb 5.5e-07 1 1 1 P value adjustment method: holm p=0.100 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 243.30 60.83 6.2113 8.922e-05 *** Residuals 245 2399.23 9.79 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.00025 - - - sb 7.6e-05 1.00000 - - wb 0.01931 0.37393 0.77144 - pb 0.00316 1.00000 1.00000 1.00000 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 2.7e-05 - - - sb 1.9e-05 1.00000 - - wb 0.00357 0.91025 0.91025 - pb 0.00034 0.85224 0.68034 1.00000 P value adjustment method: holm p=0.125 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 96.88 24.22 3.8246 0.004913 ** Residuals 245 1551.47 6.33 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.26295 - - - sb 0.00015 0.26295 - - wb 0.00287 0.26295 1.00000 - pb 0.00036 0.26295 1.00000 1.00000 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.20395 - - - sb 0.00018 0.31574 - - wb 0.00063 0.31405 1.00000 - pb 0.00034 0.31405 1.00000 1.00000 P value adjustment method: holm p=0.150 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 39.90 9.98 1.9194 0.1078 Residuals 245 1273.23 5.20 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.0011 - - - sb 0.5544 1.0000 - - wb 0.0060 1.0000 1.0000 - pb 0.1205 1.0000 1.0000 1.0000 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.0019 - - - sb 0.2040 1.0000 - - wb 0.0034 1.0000 1.0000 - pb 0.0293 1.0000 1.0000 1.0000 P value adjustment method: holm p=0.175 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 17.04 4.26 1.0789 0.3675 Residuals 245 967.53 3.95 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.034 - - - sb 1.000 1.000 - - wb 1.000 1.000 1.000 - pb 1.000 1.000 1.000 1.000 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.0087 - - - sb 1.0000 0.5264 - - wb 1.0000 1.0000 1.0000 - pb 0.7747 1.0000 1.0000 1.0000 P value adjustment method: holm p=0.200 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 59.58 14.90 3.7607 0.005465 ** Residuals 245 970.42 3.96 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.0021 - - - sb 0.0015 1.0000 - - wb 0.0021 1.0000 1.0000 - pb 0.0015 1.0000 1.0000 1.0000 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 0.0039 - - - sb 0.0018 1.0000 - - wb 0.0046 1.0000 1.0000 - pb 0.0030 1.0000 1.0000 1.0000 P value adjustment method: holm p=0.300 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 5.75 1.44 0.3576 0.8386 Residuals 245 985.45 4.02 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 1.00 - - - sb 1.00 1.00 - - wb 1.00 1.00 - - pb 1.00 0.98 1.00 1.00 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb 1.0 - - - sb 1.0 1.0 - - wb 1.0 1.0 - - pb 1.0 0.9 1.0 1.0 P value adjustment method: holm p=0.400 Df Sum Sq Mean Sq F value Pr(>F) is.type 4 5.16 1.29 0.3692 0.8305 Residuals 245 856.65 3.50 Pairwise comparisons using t tests with non-pooled SD data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb - - - - sb 1 1 - - wb 1 1 - - pb 1 1 1 1 P value adjustment method: holm Pairwise comparisons using Wilcoxon rank sum test data: currentDataAnova$cost and currentDataAnova$is.type ub gb sb wb gb - - - - sb 1 1 - - wb 1 1 - - pb 1 1 1 1 P value adjustment method: holm