diff --git a/DESCRIPTION b/DESCRIPTION index d83adbd..110d5c1 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: pROC Type: Package Title: Display and Analyze ROC Curves -Version: 1.18.5 -Date: 2023-11-01 +Version: 1.18.9999 +Date: 2024-07-07 Encoding: UTF-8 Depends: R (>= 2.14) Imports: methods, plyr, Rcpp (>= 0.11.1) diff --git a/NEWS b/NEWS index 541c631..e45b1ba 100644 --- a/NEWS +++ b/NEWS @@ -1,3 +1,6 @@ +1.18.9999 + * Added "lr_pos" and "lr_neg" to 'coords' (issue #102) + 1.18.5 (2023-11-01): * Fixed formula input when given as variable and combined with `with` (issue #111) * Fixed formula containing variables with spaces (issue #120) diff --git a/R/roc.utils.R b/R/roc.utils.R index beb1aac..f5bb517 100644 --- a/R/roc.utils.R +++ b/R/roc.utils.R @@ -309,7 +309,7 @@ roc.utils.valid.coords <- c("specificity", "sensitivity", "accuracy", "npv", "ppv", "fdr", "fpr", "tpr", "tnr", "fnr", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv", - "precision", "recall", + "precision", "recall", "lr_pos", "lr_neg", "youden", "closest.topleft") # Arguments which can be returned by coords @@ -472,6 +472,8 @@ roc_utils_calc_coords <- function(roc, thr, se, sp, best.weights) { "1-ppv"=substr.percent-ppv, precision=precision, recall=recall, + "lr_pos"=(se / substr.percent) / (1 - sp / substr.percent), + "lr_neg"=(1 - se / substr.percent) / (sp / substr.percent), youden=youden, closest.topleft=closest.topleft )) @@ -565,6 +567,8 @@ coord.is.monotone <- c( "1-ppv"=FALSE, "precision"=FALSE, "recall"=TRUE, + "lr_pos"=FALSE, + "lr_neg"=FALSE, "youden"=FALSE, "closest.topleft"=FALSE ) @@ -592,6 +596,8 @@ coord.is.decreasing <- c( "1-ppv"=NA, "precision"=NA, "recall"=TRUE, + "lr_pos"=NA, + "lr_neg"=NA, "youden"=NA, "closest.topleft"=NA ) diff --git a/man/coords.Rd b/man/coords.Rd index 12abbfa..176d286 100644 --- a/man/coords.Rd +++ b/man/coords.Rd @@ -184,6 +184,8 @@ as.matrix=FALSE, ...) \code{tnr} \tab True Negative Rate \tab tn / (tn + fp) \tab specificity \tab Yes \cr \code{fnr} \tab False Negative Rate \tab fn / (tp + fn) \tab 1-sensitivity \tab Yes \cr \code{fdr} \tab False Discovery Rate \tab fp / (tp + fp) \tab 1-ppv \tab No \cr + \code{lr_pos} \tab Positive Likelihood Ratio \tab se / (1 - sp) \tab - \tab No \cr + \code{lr_neg} \tab Negative Likelihood Ratio \tab (1 - se) / (sp) \tab - \tab No \cr \code{youden} \tab Youden Index \tab se + r * sp \tab - \tab No \cr diff --git a/tests/testthat/helper-coords-expected-smooth.R b/tests/testthat/helper-coords-expected-smooth.R index 71230dc..def2c21 100644 --- a/tests/testthat/helper-coords-expected-smooth.R +++ b/tests/testthat/helper-coords-expected-smooth.R @@ -1,437 +1,439 @@ +# expected.coords.smooth <- coords(smooth(r.s100b), "all", ret="all") +# dump("expected.coords.smooth", "tests/testthat/helper-coords-expected-smooth.R_new") + expected.coords.smooth <- -structure(list(specificity = c(0, 0, 0.0063876682047232566, 0.013341366018777983, -0.020344288637047788, 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-0.339449541284404, 0, 1, 0.0975609756097561, 1.09756097560976, -0.814396192742415, 0.84, 1, 0.0731707317073171, 0.663716814159292, -72, 3, 38, 0, 0.654545454545455, 1, 0, 0, 0.0731707317073171, -1, 0.926829268292683, 0, 0.926829268292683, 0.336283185840708, -0.345454545454545, 0, 1, 0.0731707317073171, 1.07317073170732, -0.85901249256395, 0.91, 1, 0.0487804878048781, 0.654867256637168, -72, 2, 39, 0, 0.648648648648649, 1, 0, 0, 0.0487804878048781, -1, 0.951219512195122, 0, 0.951219512195122, 0.345132743362832, -0.351351351351351, 0, 1, 0.0487804878048781, 1.04878048780488, -0.904818560380726, 1.515, 1, 0.024390243902439, 0.646017699115044, -72, 1, 40, 0, 0.642857142857143, 1, 0, 0, 0.024390243902439, -1, 0.975609756097561, 0, 0.975609756097561, 0.353982300884956, -0.357142857142857, 0, 1, 0.024390243902439, 1.02439024390244, -0.951814396192742, Inf, 1, 0, 0.63716814159292, 72, 0, 41, 0, -0.63716814159292, NaN, NaN, 0, 0, 1, 1, 0, 1, 0.36283185840708, -0.36283185840708, NaN, NaN, 0, 1, 1), .Dim = c(24L, 51L), .Dimnames = list( - c("threshold", "specificity", "sensitivity", "accuracy", - "tn", "tp", "fn", "fp", "npv", "ppv", "fdr", "fpr", "tpr", - "tnr", "fnr", "1-specificity", "1-sensitivity", "1-accuracy", - "1-npv", "1-ppv", "precision", "recall", "youden", "closest.topleft" - ), NULL)) +# expected.coords <- coords(r.s100b, "all", ret="all") +# dump("expected.coords", "", control = c("all", "hexNumeric")) +expected.coords <- +structure(list(threshold = c(-Inf, 0x1.1eb851eb851ecp-5, 0x1.70a3d70a3d70ap-5, +0x1.c28f5c28f5c29p-5, 0x1.0a3d70a3d70a4p-4, 0x1.3333333333334p-4, +0x1.5c28f5c28f5c2p-4, 0x1.851eb851eb852p-4, 0x1.ae147ae147ae2p-4, +0x1.d70a3d70a3d7p-4, 0x1p-3, 0x1.147ae147ae148p-3, 0x1.28f5c28f5c29p-3, +0x1.3d70a3d70a3d7p-3, 0x1.51eb851eb851fp-3, 0x1.6666666666666p-3, +0x1.7ae147ae147aep-3, 0x1.a3d70a3d70a3ep-3, 0x1.ccccccccccccdp-3, +0x1.e147ae147ae14p-3, 0x1.f5c28f5c28f5cp-3, 0x1.051eb851eb852p-2, +0x1.0f5c28f5c28f6p-2, 0x1.199999999999ap-2, 0x1.28f5c28f5c29p-2, +0x1.3d70a3d70a3d7p-2, 0x1.4cccccccccccdp-2, 0x1.570a3d70a3d71p-2, +0x1.6147ae147ae14p-2, 0x1.75c28f5c28f5cp-2, 0x1.947ae147ae148p-2, +0x1.ae147ae147ae1p-2, 0x1.bd70a3d70a3d7p-2, 0x1.c7ae147ae147bp-2, +0x1.d1eb851eb851fp-2, 0x1.dc28f5c28f5c2p-2, 0x1.e666666666666p-2, +0x1.f0a3d70a3d70ap-2, 0x1.fae147ae147aep-2, 0x1.051eb851eb852p-1, +0x1.147ae147ae148p-1, 0x1.23d70a3d70a3ep-1, 0x1.47ae147ae147ap-1, +0x1.68f5c28f5c28fp-1, 0x1.7333333333333p-1, 0x1.828f5c28f5c29p-1, +0x1.970a3d70a3d7p-1, 0x1.ae147ae147ae1p-1, 0x1.d1eb851eb851ep-1, +0x1.83d70a3d70a3dp+0, Inf), specificity = c(0x0p+0, 0x0p+0, 0x1.1c71c71c71c72p-4, +0x1.c71c71c71c71cp-4, 0x1.1c71c71c71c72p-3, 0x1.c71c71c71c71cp-3, +0x1.38e38e38e38e4p-2, 0x1.8e38e38e38e39p-2, 0x1.f1c71c71c71c7p-2, +0x1.1555555555555p-1, 0x1.1555555555555p-1, 0x1.2aaaaaaaaaaabp-1, +0x1.471c71c71c71cp-1, 0x1.638e38e38e38ep-1, 0x1.78e38e38e38e4p-1, +0x1.871c71c71c71cp-1, 0x1.8e38e38e38e39p-1, 0x1.9c71c71c71c72p-1, +0x1.9c71c71c71c72p-1, 0x1.9c71c71c71c72p-1, 0x1.a38e38e38e38ep-1, +0x1.a38e38e38e38ep-1, 0x1.a38e38e38e38ep-1, 0x1.a38e38e38e38ep-1, +0x1.aaaaaaaaaaaabp-1, 0x1.aaaaaaaaaaaabp-1, 0x1.b1c71c71c71c7p-1, +0x1.b8e38e38e38e4p-1, 0x1.cp-1, 0x1.cp-1, 0x1.c71c71c71c71cp-1, +0x1.c71c71c71c71cp-1, 0x1.ce38e38e38e39p-1, 0x1.ce38e38e38e39p-1, +0x1.d555555555555p-1, 0x1.dc71c71c71c72p-1, 0x1.eaaaaaaaaaaabp-1, +0x1.f1c71c71c71c7p-1, 0x1.f1c71c71c71c7p-1, 0x1p+0, 0x1p+0, 0x1p+0, +0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, +0x1p+0), sensitivity = c(0x1p+0, 0x1.f3831f3831f38p-1, 0x1.f3831f3831f38p-1, +0x1.f3831f3831f38p-1, 0x1.f3831f3831f38p-1, 0x1.ce0c7ce0c7ce1p-1, +0x1.c18f9c18f9c19p-1, 0x1.a895da895da89p-1, 0x1.8f9c18f9c18fap-1, +0x1.831f3831f3832p-1, 0x1.76a2576a2576ap-1, 0x1.5da895da895dbp-1, +0x1.512bb512bb513p-1, 0x1.512bb512bb513p-1, 0x1.44aed44aed44bp-1, +0x1.44aed44aed44bp-1, 0x1.44aed44aed44bp-1, 0x1.44aed44aed44bp-1, +0x1.3831f3831f383p-1, 0x1.2bb512bb512bbp-1, 0x1.2bb512bb512bbp-1, +0x1.1f3831f3831f4p-1, 0x1.12bb512bb512cp-1, 0x1.063e7063e7064p-1, +0x1.063e7063e7064p-1, 0x1.f3831f3831f38p-2, 0x1.da895da895da9p-2, +0x1.c18f9c18f9c19p-2, 0x1.c18f9c18f9c19p-2, 0x1.a895da895da89p-2, +0x1.a895da895da89p-2, 0x1.8f9c18f9c18fap-2, 0x1.8f9c18f9c18fap-2, +0x1.5da895da895dbp-2, 0x1.5da895da895dbp-2, 0x1.5da895da895dbp-2, +0x1.5da895da895dbp-2, 0x1.44aed44aed44bp-2, 0x1.2bb512bb512bbp-2, +0x1.2bb512bb512bbp-2, 0x1.12bb512bb512cp-2, 0x1.f3831f3831f38p-3, +0x1.c18f9c18f9c19p-3, 0x1.8f9c18f9c18fap-3, 0x1.2bb512bb512bbp-3, +0x1.f3831f3831f38p-4, 0x1.8f9c18f9c18fap-4, 0x1.2bb512bb512bbp-4, +0x1.8f9c18f9c18fap-5, 0x1.8f9c18f9c18fap-6, 0x0p+0), accuracy = c(0x1.738a31d738a32p-2, +0x1.6a7a5616a7a56p-2, 0x1.97c9a0d97c9a1p-2, 0x1.b2f9341b2f934p-2, +0x1.c518eb9c518ecp-2, 0x1.e0487ede0487fp-2, 0x1.06cbe4d06cbe5p-1, +0x1.18eb9c518eb9cp-1, 0x1.2f9341b2f9342p-1, 0x1.3d2b0b53d2b0bp-1, +0x1.38a31d738a31dp-1, 0x1.3d2b0b53d2b0bp-1, 0x1.4ac2d4f4ac2d5p-1, +0x1.5ce28c75ce28cp-1, 0x1.65f268365f268p-1, 0x1.6f0243f6f0244p-1, +0x1.738a31d738a32p-1, 0x1.7c9a0d97c9a0ep-1, 0x1.78121fb78122p-1, +0x1.738a31d738a32p-1, 0x1.78121fb78122p-1, 0x1.738a31d738a32p-1, +0x1.6f0243f6f0244p-1, 0x1.6a7a5616a7a56p-1, 0x1.6f0243f6f0244p-1, +0x1.6a7a5616a7a56p-1, 0x1.6a7a5616a7a56p-1, 0x1.6a7a5616a7a56p-1, +0x1.6f0243f6f0244p-1, 0x1.6a7a5616a7a56p-1, 0x1.6f0243f6f0244p-1, +0x1.6a7a5616a7a56p-1, 0x1.6f0243f6f0244p-1, 0x1.65f268365f268p-1, +0x1.6a7a5616a7a56p-1, 0x1.6f0243f6f0244p-1, 0x1.78121fb78122p-1, +0x1.78121fb78122p-1, 0x1.738a31d738a32p-1, 0x1.7c9a0d97c9a0ep-1, +0x1.78121fb78122p-1, 0x1.738a31d738a32p-1, 0x1.6f0243f6f0244p-1, +0x1.6a7a5616a7a56p-1, 0x1.616a7a5616a7ap-1, 0x1.5ce28c75ce28cp-1, +0x1.585a9e9585a9fp-1, 0x1.53d2b0b53d2b1p-1, 0x1.4f4ac2d4f4ac3p-1, +0x1.4ac2d4f4ac2d5p-1, 0x1.463ae71463ae7p-1), tn = c(0x0p+0, 0x0p+0, +0x1.4p+2, 0x1p+3, 0x1.4p+3, 0x1p+4, 0x1.6p+4, 0x1.cp+4, 0x1.18p+5, +0x1.38p+5, 0x1.38p+5, 0x1.5p+5, 0x1.7p+5, 0x1.9p+5, 0x1.a8p+5, +0x1.b8p+5, 0x1.cp+5, 0x1.dp+5, 0x1.dp+5, 0x1.dp+5, 0x1.d8p+5, +0x1.d8p+5, 0x1.d8p+5, 0x1.d8p+5, 0x1.ep+5, 0x1.ep+5, 0x1.e8p+5, +0x1.fp+5, 0x1.f8p+5, 0x1.f8p+5, 0x1p+6, 0x1p+6, 0x1.04p+6, 0x1.04p+6, +0x1.08p+6, 0x1.0cp+6, 0x1.14p+6, 0x1.18p+6, 0x1.18p+6, 0x1.2p+6, +0x1.2p+6, 0x1.2p+6, 0x1.2p+6, 0x1.2p+6, 0x1.2p+6, 0x1.2p+6, 0x1.2p+6, +0x1.2p+6, 0x1.2p+6, 0x1.2p+6, 0x1.2p+6), tp = c(0x1.48p+5, 0x1.4p+5, +0x1.4p+5, 0x1.4p+5, 0x1.4p+5, 0x1.28p+5, 0x1.2p+5, 0x1.1p+5, +0x1p+5, 0x1.fp+4, 0x1.ep+4, 0x1.c000000000001p+4, 0x1.bp+4, 0x1.bp+4, +0x1.ap+4, 0x1.ap+4, 0x1.ap+4, 0x1.ap+4, 0x1.9p+4, 0x1.8p+4, 0x1.8p+4, +0x1.7000000000001p+4, 0x1.6p+4, 0x1.5p+4, 0x1.5p+4, 0x1.4p+4, +0x1.3p+4, 0x1.2p+4, 0x1.2p+4, 0x1.1p+4, 0x1.1p+4, 0x1p+4, 0x1p+4, +0x1.c000000000001p+3, 0x1.c000000000001p+3, 0x1.c000000000001p+3, +0x1.c000000000001p+3, 0x1.ap+3, 0x1.8p+3, 0x1.8p+3, 0x1.6p+3, +0x1.4p+3, 0x1.2p+3, 0x1p+3, 0x1.8p+2, 0x1.4p+2, 0x1p+2, 0x1.8p+1, +0x1p+1, 0x1p+0, 0x0p+0), fn = c(0x0p+0, 0x1p+0, 0x1p+0, 0x1p+0, +0x1p+0, 0x1p+2, 0x1.4p+2, 0x1.cp+2, 0x1.2p+3, 0x1.4p+3, 0x1.6p+3, +0x1.9fffffffffffep+3, 0x1.cp+3, 0x1.cp+3, 0x1.ep+3, 0x1.ep+3, +0x1.ep+3, 0x1.ep+3, 0x1p+4, 0x1.1p+4, 0x1.1p+4, 0x1.1ffffffffffffp+4, +0x1.3p+4, 0x1.4p+4, 0x1.4p+4, 0x1.5p+4, 0x1.6p+4, 0x1.7p+4, 0x1.7p+4, +0x1.8p+4, 0x1.8p+4, 0x1.9p+4, 0x1.9p+4, 0x1.bp+4, 0x1.bp+4, 0x1.bp+4, +0x1.bp+4, 0x1.cp+4, 0x1.dp+4, 0x1.dp+4, 0x1.ep+4, 0x1.fp+4, 0x1p+5, +0x1.08p+5, 0x1.18p+5, 0x1.2p+5, 0x1.28p+5, 0x1.3p+5, 0x1.38p+5, +0x1.4p+5, 0x1.48p+5), fp = c(0x1.2p+6, 0x1.2p+6, 0x1.0cp+6, 0x1p+6, +0x1.fp+5, 0x1.cp+5, 0x1.9p+5, 0x1.6p+5, 0x1.28p+5, 0x1.08p+5, +0x1.08p+5, 0x1.ep+4, 0x1.ap+4, 0x1.6p+4, 0x1.3p+4, 0x1.1p+4, +0x1p+4, 0x1.cp+3, 0x1.cp+3, 0x1.cp+3, 0x1.ap+3, 0x1.ap+3, 0x1.ap+3, +0x1.ap+3, 0x1.8p+3, 0x1.8p+3, 0x1.6p+3, 0x1.4p+3, 0x1.2p+3, 0x1.2p+3, +0x1p+3, 0x1p+3, 0x1.cp+2, 0x1.cp+2, 0x1.8p+2, 0x1.4p+2, 0x1.8p+1, +0x1p+1, 0x1p+1, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, +0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0), npv = c(NaN, +0x0p+0, 0x1.aaaaaaaaaaaabp-1, 0x1.c71c71c71c71cp-1, 0x1.d1745d1745d17p-1, +0x1.999999999999ap-1, 0x1.a12f684bda12fp-1, 0x1.999999999999ap-1, +0x1.9745d1745d174p-1, 0x1.97829cbc14e5ep-1, 0x1.8f5c28f5c28f6p-1, +0x1.86fb586fb587p-1, 0x1.8888888888889p-1, 0x1.9p-1, 0x1.8f0f0f0f0f0f1p-1, +0x1.9249249249249p-1, 0x1.93d4bb7e327a9p-1, 0x1.96cb65b2d96cbp-1, +0x1.914c1bacf914cp-1, 0x1.8bf258bf258bfp-1, 0x1.8d79435e50d79p-1, +0x1.884fcace213f3p-1, 0x1.8348348348348p-1, 0x1.7e613716aefccp-1, +0x1.8p-1, 0x1.7b425ed097b42p-1, 0x1.784a062b2e43ep-1, 0x1.7575757575757p-1, +0x1.7711dc47711dcp-1, 0x1.72c234f72c235p-1, 0x1.745d1745d1746p-1, +0x1.702e05c0b817p-1, 0x1.71c71c71c71c7p-1, 0x1.69bd37a6f4deap-1, +0x1.6b5ad6b5ad6b6p-1, 0x1.6cefa8d9df51bp-1, 0x1.7p-1, 0x1.6db6db6db6db7p-1, +0x1.6a052bf5a814bp-1, 0x1.6cfd7720f353ap-1, 0x1.6969696969697p-1, +0x1.65e7254813e23p-1, 0x1.6276276276276p-1, 0x1.5f15f15f15f16p-1, +0x1.5885fb37072d7p-1, 0x1.5555555555555p-1, 0x1.5233ab7315234p-1, +0x1.4f2094f2094f2p-1, 0x1.4c1bacf914c1cp-1, 0x1.4924924924925p-1, +0x1.463ae71463ae7p-1), ppv = c(0x1.738a31d738a32p-2, 0x1.6db6db6db6db7p-2, +0x1.7ecdc1cb5d4efp-2, 0x1.89d89d89d89d9p-2, 0x1.9191919191919p-2, +0x1.9765d9765d976p-2, 0x1.aca6b29aca6b3p-2, 0x1.be5be5be5be5cp-2, +0x1.dae6076b981dbp-2, 0x1.fp-2, 0x1.e79e79e79e79ep-2, 0x1.ee58469ee5848p-2, +0x1.04d4873ecade3p-1, 0x1.1a1f58d0fac68p-1, 0x1.27d27d27d27d2p-1, +0x1.3594d653594d6p-1, 0x1.3cf3cf3cf3cf4p-1, 0x1.4cccccccccccdp-1, +0x1.4834834834835p-1, 0x1.435e50d79435ep-1, 0x1.4c1bacf914c1cp-1, +0x1.471c71c71c71dp-1, 0x1.41d41d41d41d4p-1, 0x1.3c3c3c3c3c3c4p-1, +0x1.45d1745d1745dp-1, 0x1.4p-1, 0x1.4444444444444p-1, 0x1.4924924924925p-1, +0x1.5555555555555p-1, 0x1.4ec4ec4ec4ec5p-1, 0x1.5c28f5c28f5c3p-1, +0x1.5555555555555p-1, 0x1.642c8590b2164p-1, 0x1.5555555555556p-1, +0x1.6666666666667p-1, 0x1.79435e50d7944p-1, 0x1.a5a5a5a5a5a5bp-1, +0x1.bbbbbbbbbbbbcp-1, 0x1.b6db6db6db6dbp-1, 0x1p+0, 0x1p+0, 0x1p+0, +0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, +NaN), fdr = c(0x1.463ae71463ae7p-1, 0x1.4924924924925p-1, 0x1.40991f1a51588p-1, +0x1.3b13b13b13b14p-1, 0x1.3737373737373p-1, 0x1.344d1344d1345p-1, +0x1.29aca6b29aca7p-1, 0x1.20d20d20d20d2p-1, 0x1.128cfc4a33f13p-1, +0x1.08p-1, 0x1.0c30c30c30c31p-1, 0x1.08d3dcb08d3ddp-1, 0x1.f656f1826a43ap-2, +0x1.cbc14e5e0a72fp-2, 0x1.b05b05b05b05bp-2, 0x1.94d653594d653p-2, +0x1.8618618618618p-2, 0x1.6666666666666p-2, 0x1.6f96f96f96f97p-2, +0x1.79435e50d7943p-2, 0x1.67c8a60dd67c9p-2, 0x1.71c71c71c71c7p-2, +0x1.7c57c57c57c58p-2, 0x1.8787878787878p-2, 0x1.745d1745d1746p-2, +0x1.8p-2, 0x1.7777777777777p-2, 0x1.6db6db6db6db7p-2, 0x1.5555555555555p-2, +0x1.6276276276276p-2, 0x1.47ae147ae147bp-2, 0x1.5555555555555p-2, +0x1.37a6f4de9bd38p-2, 0x1.5555555555555p-2, 0x1.3333333333333p-2, +0x1.0d79435e50d79p-2, 0x1.6969696969697p-3, 0x1.1111111111111p-3, +0x1.2492492492492p-3, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, +0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, NaN), fpr = c(0x1p+0, +0x1p+0, 0x1.dc71c71c71c72p-1, 0x1.c71c71c71c71cp-1, 0x1.b8e38e38e38e4p-1, +0x1.8e38e38e38e39p-1, 0x1.638e38e38e38ep-1, 0x1.38e38e38e38e4p-1, +0x1.071c71c71c71cp-1, 0x1.d555555555556p-2, 0x1.d555555555556p-2, +0x1.aaaaaaaaaaaaap-2, 0x1.71c71c71c71c8p-2, 0x1.38e38e38e38e4p-2, +0x1.0e38e38e38e38p-2, 0x1.e38e38e38e39p-3, 0x1.c71c71c71c71cp-3, +0x1.8e38e38e38e38p-3, 0x1.8e38e38e38e38p-3, 0x1.8e38e38e38e38p-3, +0x1.71c71c71c71c8p-3, 0x1.71c71c71c71c8p-3, 0x1.71c71c71c71c8p-3, +0x1.71c71c71c71c8p-3, 0x1.5555555555554p-3, 0x1.5555555555554p-3, +0x1.38e38e38e38e4p-3, 0x1.1c71c71c71c7p-3, 0x1p-3, 0x1p-3, 0x1.c71c71c71c72p-4, +0x1.c71c71c71c72p-4, 0x1.8e38e38e38e38p-4, 0x1.8e38e38e38e38p-4, +0x1.5555555555558p-4, 0x1.1c71c71c71c7p-4, 0x1.555555555555p-5, +0x1.c71c71c71c72p-6, 0x1.c71c71c71c72p-6, 0x0p+0, 0x0p+0, 0x0p+0, +0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, +0x0p+0), tpr = c(0x1p+0, 0x1.f3831f3831f38p-1, 0x1.f3831f3831f38p-1, +0x1.f3831f3831f38p-1, 0x1.f3831f3831f38p-1, 0x1.ce0c7ce0c7ce1p-1, +0x1.c18f9c18f9c19p-1, 0x1.a895da895da89p-1, 0x1.8f9c18f9c18fap-1, +0x1.831f3831f3832p-1, 0x1.76a2576a2576ap-1, 0x1.5da895da895dbp-1, +0x1.512bb512bb513p-1, 0x1.512bb512bb513p-1, 0x1.44aed44aed44bp-1, +0x1.44aed44aed44bp-1, 0x1.44aed44aed44bp-1, 0x1.44aed44aed44bp-1, +0x1.3831f3831f383p-1, 0x1.2bb512bb512bbp-1, 0x1.2bb512bb512bbp-1, +0x1.1f3831f3831f4p-1, 0x1.12bb512bb512cp-1, 0x1.063e7063e7064p-1, +0x1.063e7063e7064p-1, 0x1.f3831f3831f38p-2, 0x1.da895da895da9p-2, +0x1.c18f9c18f9c19p-2, 0x1.c18f9c18f9c19p-2, 0x1.a895da895da89p-2, +0x1.a895da895da89p-2, 0x1.8f9c18f9c18fap-2, 0x1.8f9c18f9c18fap-2, +0x1.5da895da895dbp-2, 0x1.5da895da895dbp-2, 0x1.5da895da895dbp-2, +0x1.5da895da895dbp-2, 0x1.44aed44aed44bp-2, 0x1.2bb512bb512bbp-2, +0x1.2bb512bb512bbp-2, 0x1.12bb512bb512cp-2, 0x1.f3831f3831f38p-3, +0x1.c18f9c18f9c19p-3, 0x1.8f9c18f9c18fap-3, 0x1.2bb512bb512bbp-3, +0x1.f3831f3831f38p-4, 0x1.8f9c18f9c18fap-4, 0x1.2bb512bb512bbp-4, +0x1.8f9c18f9c18fap-5, 0x1.8f9c18f9c18fap-6, 0x0p+0), tnr = c(0x0p+0, +0x0p+0, 0x1.1c71c71c71c72p-4, 0x1.c71c71c71c71cp-4, 0x1.1c71c71c71c72p-3, +0x1.c71c71c71c71cp-3, 0x1.38e38e38e38e4p-2, 0x1.8e38e38e38e39p-2, +0x1.f1c71c71c71c7p-2, 0x1.1555555555555p-1, 0x1.1555555555555p-1, +0x1.2aaaaaaaaaaabp-1, 0x1.471c71c71c71cp-1, 0x1.638e38e38e38ep-1, +0x1.78e38e38e38e4p-1, 0x1.871c71c71c71cp-1, 0x1.8e38e38e38e39p-1, +0x1.9c71c71c71c72p-1, 0x1.9c71c71c71c72p-1, 0x1.9c71c71c71c72p-1, +0x1.a38e38e38e38ep-1, 0x1.a38e38e38e38ep-1, 0x1.a38e38e38e38ep-1, +0x1.a38e38e38e38ep-1, 0x1.aaaaaaaaaaaabp-1, 0x1.aaaaaaaaaaaabp-1, +0x1.b1c71c71c71c7p-1, 0x1.b8e38e38e38e4p-1, 0x1.cp-1, 0x1.cp-1, +0x1.c71c71c71c71cp-1, 0x1.c71c71c71c71cp-1, 0x1.ce38e38e38e39p-1, +0x1.ce38e38e38e39p-1, 0x1.d555555555555p-1, 0x1.dc71c71c71c72p-1, +0x1.eaaaaaaaaaaabp-1, 0x1.f1c71c71c71c7p-1, 0x1.f1c71c71c71c7p-1, +0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, +0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0), fnr = c(0x0p+0, 0x1.8f9c18f9c18fap-6, +0x1.8f9c18f9c18fap-6, 0x1.8f9c18f9c18fap-6, 0x1.8f9c18f9c18fap-6, +0x1.8f9c18f9c18fap-4, 0x1.f3831f3831f38p-4, 0x1.5da895da895dbp-3, +0x1.c18f9c18f9c19p-3, 0x1.f3831f3831f38p-3, 0x1.12bb512bb512cp-2, +0x1.44aed44aed449p-2, 0x1.5da895da895dbp-2, 0x1.5da895da895dbp-2, +0x1.76a2576a2576ap-2, 0x1.76a2576a2576ap-2, 0x1.76a2576a2576ap-2, +0x1.76a2576a2576ap-2, 0x1.8f9c18f9c18fap-2, 0x1.a895da895da89p-2, +0x1.a895da895da89p-2, 0x1.c18f9c18f9c17p-2, 0x1.da895da895da9p-2, +0x1.f3831f3831f38p-2, 0x1.f3831f3831f38p-2, 0x1.063e7063e7064p-1, +0x1.12bb512bb512cp-1, 0x1.1f3831f3831f4p-1, 0x1.1f3831f3831f4p-1, +0x1.2bb512bb512bbp-1, 0x1.2bb512bb512bbp-1, 0x1.3831f3831f383p-1, +0x1.3831f3831f383p-1, 0x1.512bb512bb513p-1, 0x1.512bb512bb513p-1, +0x1.512bb512bb513p-1, 0x1.512bb512bb513p-1, 0x1.5da895da895dbp-1, +0x1.6a2576a2576a2p-1, 0x1.6a2576a2576a2p-1, 0x1.76a2576a2576ap-1, +0x1.831f3831f3832p-1, 0x1.8f9c18f9c18fap-1, 0x1.9c18f9c18f9c2p-1, +0x1.b512bb512bb51p-1, 0x1.c18f9c18f9c19p-1, 0x1.ce0c7ce0c7ce1p-1, +0x1.da895da895da9p-1, 0x1.e7063e7063e7p-1, 0x1.f3831f3831f38p-1, +0x1p+0), `1-specificity` = c(0x1p+0, 0x1p+0, 0x1.dc71c71c71c72p-1, +0x1.c71c71c71c71cp-1, 0x1.b8e38e38e38e4p-1, 0x1.8e38e38e38e39p-1, +0x1.638e38e38e38ep-1, 0x1.38e38e38e38e4p-1, 0x1.071c71c71c71cp-1, +0x1.d555555555556p-2, 0x1.d555555555556p-2, 0x1.aaaaaaaaaaaaap-2, +0x1.71c71c71c71c8p-2, 0x1.38e38e38e38e4p-2, 0x1.0e38e38e38e38p-2, +0x1.e38e38e38e39p-3, 0x1.c71c71c71c71cp-3, 0x1.8e38e38e38e38p-3, +0x1.8e38e38e38e38p-3, 0x1.8e38e38e38e38p-3, 0x1.71c71c71c71c8p-3, +0x1.71c71c71c71c8p-3, 0x1.71c71c71c71c8p-3, 0x1.71c71c71c71c8p-3, +0x1.5555555555554p-3, 0x1.5555555555554p-3, 0x1.38e38e38e38e4p-3, +0x1.1c71c71c71c7p-3, 0x1p-3, 0x1p-3, 0x1.c71c71c71c72p-4, 0x1.c71c71c71c72p-4, +0x1.8e38e38e38e38p-4, 0x1.8e38e38e38e38p-4, 0x1.5555555555558p-4, +0x1.1c71c71c71c7p-4, 0x1.555555555555p-5, 0x1.c71c71c71c72p-6, +0x1.c71c71c71c72p-6, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, +0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0), `1-sensitivity` = c(0x0p+0, +0x1.8f9c18f9c19p-6, 0x1.8f9c18f9c19p-6, 0x1.8f9c18f9c19p-6, 0x1.8f9c18f9c19p-6, +0x1.8f9c18f9c18f8p-4, 0x1.f3831f3831f38p-4, 0x1.5da895da895dcp-3, +0x1.c18f9c18f9c18p-3, 0x1.f3831f3831f38p-3, 0x1.12bb512bb512cp-2, +0x1.44aed44aed44ap-2, 0x1.5da895da895dap-2, 0x1.5da895da895dap-2, +0x1.76a2576a2576ap-2, 0x1.76a2576a2576ap-2, 0x1.76a2576a2576ap-2, +0x1.76a2576a2576ap-2, 0x1.8f9c18f9c18fap-2, 0x1.a895da895da8ap-2, +0x1.a895da895da8ap-2, 0x1.c18f9c18f9c18p-2, 0x1.da895da895da8p-2, +0x1.f3831f3831f38p-2, 0x1.f3831f3831f38p-2, 0x1.063e7063e7064p-1, +0x1.12bb512bb512cp-1, 0x1.1f3831f3831f4p-1, 0x1.1f3831f3831f4p-1, +0x1.2bb512bb512bcp-1, 0x1.2bb512bb512bcp-1, 0x1.3831f3831f383p-1, +0x1.3831f3831f383p-1, 0x1.512bb512bb512p-1, 0x1.512bb512bb512p-1, +0x1.512bb512bb512p-1, 0x1.512bb512bb512p-1, 0x1.5da895da895dap-1, +0x1.6a2576a2576a2p-1, 0x1.6a2576a2576a2p-1, 0x1.76a2576a2576ap-1, +0x1.831f3831f3832p-1, 0x1.8f9c18f9c18fap-1, 0x1.9c18f9c18f9c2p-1, +0x1.b512bb512bb51p-1, 0x1.c18f9c18f9c19p-1, 0x1.ce0c7ce0c7ce1p-1, +0x1.da895da895da9p-1, 0x1.e7063e7063e7p-1, 0x1.f3831f3831f38p-1, +0x1p+0), `1-accuracy` = c(0x1.463ae71463ae7p-1, 0x1.4ac2d4f4ac2d5p-1, +0x1.341b2f9341b3p-1, 0x1.268365f268366p-1, 0x1.1d738a31d738ap-1, +0x1.0fdbc090fdbcp-1, 0x1.f268365f26836p-2, 0x1.ce28c75ce28c8p-2, +0x1.a0d97c9a0d97cp-2, 0x1.85a9e9585a9eap-2, 0x1.8eb9c518eb9c6p-2, +0x1.85a9e9585a9eap-2, 0x1.6a7a5616a7a56p-2, 0x1.463ae71463ae8p-2, +0x1.341b2f9341b3p-2, 0x1.21fb78121fb78p-2, 0x1.18eb9c518eb9cp-2, +0x1.06cbe4d06cbe4p-2, 0x1.0fdbc090fdbcp-2, 0x1.18eb9c518eb9cp-2, +0x1.0fdbc090fdbcp-2, 0x1.18eb9c518eb9cp-2, 0x1.21fb78121fb78p-2, +0x1.2b0b53d2b0b54p-2, 0x1.21fb78121fb78p-2, 0x1.2b0b53d2b0b54p-2, +0x1.2b0b53d2b0b54p-2, 0x1.2b0b53d2b0b54p-2, 0x1.21fb78121fb78p-2, +0x1.2b0b53d2b0b54p-2, 0x1.21fb78121fb78p-2, 0x1.2b0b53d2b0b54p-2, +0x1.21fb78121fb78p-2, 0x1.341b2f9341b3p-2, 0x1.2b0b53d2b0b54p-2, +0x1.21fb78121fb78p-2, 0x1.0fdbc090fdbcp-2, 0x1.0fdbc090fdbcp-2, +0x1.18eb9c518eb9cp-2, 0x1.06cbe4d06cbe4p-2, 0x1.0fdbc090fdbcp-2, +0x1.18eb9c518eb9cp-2, 0x1.21fb78121fb78p-2, 0x1.2b0b53d2b0b54p-2, +0x1.3d2b0b53d2b0cp-2, 0x1.463ae71463ae8p-2, 0x1.4f4ac2d4f4ac2p-2, +0x1.585a9e9585a9ep-2, 0x1.616a7a5616a7ap-2, 0x1.6a7a5616a7a56p-2, +0x1.738a31d738a32p-2), `1-npv` = c(NaN, 0x1p+0, 0x1.5555555555554p-3, +0x1.c71c71c71c72p-4, 0x1.745d1745d1748p-4, 0x1.9999999999998p-3, +0x1.7b425ed097b44p-3, 0x1.9999999999998p-3, 0x1.a2e8ba2e8ba3p-3, +0x1.a1f58d0fac688p-3, 0x1.c28f5c28f5c28p-3, 0x1.e4129e4129e4p-3, +0x1.ddddddddddddcp-3, 0x1.cp-3, 0x1.c3c3c3c3c3c3cp-3, 0x1.b6db6db6db6dcp-3, +0x1.b0ad12073615cp-3, 0x1.a4d269349a4d4p-3, 0x1.bacf914c1badp-3, +0x1.d0369d0369d04p-3, 0x1.ca1af286bca1cp-3, 0x1.dec0d4c77b034p-3, +0x1.f2df2df2df2ep-3, 0x1.033d91d2a2068p-2, 0x1p-2, 0x1.097b425ed097cp-2, +0x1.0f6bf3a9a3784p-2, 0x1.1515151515152p-2, 0x1.11dc47711dc48p-2, +0x1.1a7b9611a7b96p-2, 0x1.1745d1745d174p-2, 0x1.1fa3f47e8fd2p-2, +0x1.1c71c71c71c72p-2, 0x1.2c8590b21642cp-2, 0x1.294a5294a5294p-2, +0x1.2620ae4c415cap-2, 0x1.2p-2, 0x1.2492492492492p-2, 0x1.2bf5a814afd6ap-2, +0x1.260511be1958cp-2, 0x1.2d2d2d2d2d2d2p-2, 0x1.3431b56fd83bap-2, +0x1.3b13b13b13b14p-2, 0x1.41d41d41d41d4p-2, 0x1.4ef40991f1a52p-2, +0x1.5555555555556p-2, 0x1.5b98a919d5b98p-2, 0x1.61bed61bed61cp-2, +0x1.67c8a60dd67c8p-2, 0x1.6db6db6db6db6p-2, 0x1.738a31d738a32p-2 +), `1-ppv` = c(0x1.463ae71463ae7p-1, 0x1.4924924924924p-1, 0x1.40991f1a51588p-1, +0x1.3b13b13b13b14p-1, 0x1.3737373737374p-1, 0x1.344d1344d1345p-1, +0x1.29aca6b29aca6p-1, 0x1.20d20d20d20d2p-1, 0x1.128cfc4a33f12p-1, +0x1.08p-1, 0x1.0c30c30c30c31p-1, 0x1.08d3dcb08d3dcp-1, 0x1.f656f1826a43ap-2, +0x1.cbc14e5e0a73p-2, 0x1.b05b05b05b05cp-2, 0x1.94d653594d654p-2, +0x1.8618618618618p-2, 0x1.6666666666666p-2, 0x1.6f96f96f96f96p-2, +0x1.79435e50d7944p-2, 0x1.67c8a60dd67c8p-2, 0x1.71c71c71c71c6p-2, +0x1.7c57c57c57c58p-2, 0x1.8787878787878p-2, 0x1.745d1745d1746p-2, +0x1.8p-2, 0x1.7777777777778p-2, 0x1.6db6db6db6db6p-2, 0x1.5555555555556p-2, +0x1.6276276276276p-2, 0x1.47ae147ae147ap-2, 0x1.5555555555556p-2, +0x1.37a6f4de9bd38p-2, 0x1.5555555555554p-2, 0x1.3333333333332p-2, +0x1.0d79435e50d78p-2, 0x1.6969696969694p-3, 0x1.111111111111p-3, +0x1.2492492492494p-3, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, +0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, 0x0p+0, NaN), precision = c(0x1.738a31d738a32p-2, +0x1.6db6db6db6db7p-2, 0x1.7ecdc1cb5d4efp-2, 0x1.89d89d89d89d9p-2, +0x1.9191919191919p-2, 0x1.9765d9765d976p-2, 0x1.aca6b29aca6b3p-2, +0x1.be5be5be5be5cp-2, 0x1.dae6076b981dbp-2, 0x1.fp-2, 0x1.e79e79e79e79ep-2, +0x1.ee58469ee5848p-2, 0x1.04d4873ecade3p-1, 0x1.1a1f58d0fac68p-1, +0x1.27d27d27d27d2p-1, 0x1.3594d653594d6p-1, 0x1.3cf3cf3cf3cf4p-1, +0x1.4cccccccccccdp-1, 0x1.4834834834835p-1, 0x1.435e50d79435ep-1, +0x1.4c1bacf914c1cp-1, 0x1.471c71c71c71dp-1, 0x1.41d41d41d41d4p-1, +0x1.3c3c3c3c3c3c4p-1, 0x1.45d1745d1745dp-1, 0x1.4p-1, 0x1.4444444444444p-1, +0x1.4924924924925p-1, 0x1.5555555555555p-1, 0x1.4ec4ec4ec4ec5p-1, +0x1.5c28f5c28f5c3p-1, 0x1.5555555555555p-1, 0x1.642c8590b2164p-1, +0x1.5555555555556p-1, 0x1.6666666666667p-1, 0x1.79435e50d7944p-1, +0x1.a5a5a5a5a5a5bp-1, 0x1.bbbbbbbbbbbbcp-1, 0x1.b6db6db6db6dbp-1, +0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, 0x1p+0, +0x1p+0, 0x1p+0, 0x1p+0, NaN), recall = c(0x1p+0, 0x1.f3831f3831f38p-1, +0x1.f3831f3831f38p-1, 0x1.f3831f3831f38p-1, 0x1.f3831f3831f38p-1, +0x1.ce0c7ce0c7ce1p-1, 0x1.c18f9c18f9c19p-1, 0x1.a895da895da89p-1, +0x1.8f9c18f9c18fap-1, 0x1.831f3831f3832p-1, 0x1.76a2576a2576ap-1, +0x1.5da895da895dbp-1, 0x1.512bb512bb513p-1, 0x1.512bb512bb513p-1, +0x1.44aed44aed44bp-1, 0x1.44aed44aed44bp-1, 0x1.44aed44aed44bp-1, +0x1.44aed44aed44bp-1, 0x1.3831f3831f383p-1, 0x1.2bb512bb512bbp-1, +0x1.2bb512bb512bbp-1, 0x1.1f3831f3831f4p-1, 0x1.12bb512bb512cp-1, +0x1.063e7063e7064p-1, 0x1.063e7063e7064p-1, 0x1.f3831f3831f38p-2, +0x1.da895da895da9p-2, 0x1.c18f9c18f9c19p-2, 0x1.c18f9c18f9c19p-2, +0x1.a895da895da89p-2, 0x1.a895da895da89p-2, 0x1.8f9c18f9c18fap-2, +0x1.8f9c18f9c18fap-2, 0x1.5da895da895dbp-2, 0x1.5da895da895dbp-2, +0x1.5da895da895dbp-2, 0x1.5da895da895dbp-2, 0x1.44aed44aed44bp-2, +0x1.2bb512bb512bbp-2, 0x1.2bb512bb512bbp-2, 0x1.12bb512bb512cp-2, +0x1.f3831f3831f38p-3, 0x1.c18f9c18f9c19p-3, 0x1.8f9c18f9c18fap-3, +0x1.2bb512bb512bbp-3, 0x1.f3831f3831f38p-4, 0x1.8f9c18f9c18fap-4, +0x1.2bb512bb512bbp-4, 0x1.8f9c18f9c18fap-5, 0x1.8f9c18f9c18fap-6, +0x0p+0), lr_pos = c(0x1p+0, 0x1.f3831f3831f38p-1, 0x1.0c65054fddb48p+0, +0x1.18f9c18f9c19p+0, 0x1.220a1220a122p+0, 0x1.29080722c996cp+0, +0x1.43af143af143bp+0, 0x1.5b6355b6355b5p+0, 0x1.84cf3ae52b085p+0, +0x1.a6509a6509a64p+0, 0x1.98b1198b1198ap+0, 0x1.a3971a3971a3bp+0, +0x1.d2da0e68b497cp+0, 0x1.13ddf13ddf13ep+1, 0x1.3398276f67848p+1, +0x1.57c82c131957bp+1, 0x1.6d44aed44aed5p+1, 0x1.a17310f29ec61p+1, +0x1.9164cb5f71485p+1, 0x1.815685cc43ca8p+1, 0x1.9efab77984151p+1, +0x1.8db04529c93eep+1, 0x1.7c65d2da0e68bp+1, 0x1.6b1b608a53927p+1, +0x1.895da895da898p+1, 0x1.76a2576a2576bp+1, 0x1.8441d8441d844p+1, +0x1.949ad949ad94cp+1, 0x1.c18f9c18f9c19p+1, 0x1.a895da895da89p+1, +0x1.dda895da895d6p+1, 0x1.c18f9c18f9c16p+1, 0x1.00e45932d7dc6p+2, +0x1.c18f9c18f9c1bp+1, 0x1.063e7063e7062p+2, 0x1.3ab153ab153adp+2, +0x1.063e7063e7068p+3, 0x1.6d44aed44aed2p+3, 0x1.512bb512bb51p+3, +Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, NaN), + lr_neg = c(NaN, Inf, 0x1.67a6167a6168p-2, 0x1.c18f9c18f9c2p-3, + 0x1.67a6167a6168p-3, 0x1.c18f9c18f9c17p-2, 0x1.98b1198b1198ap-2, + 0x1.c18f9c18f9c1bp-2, 0x1.ce67d3c1eaf2fp-2, 0x1.cd1692f8cba5cp-2, + 0x1.fb326e7813366p-2, 0x1.164cb5f71483fp-1, 0x1.11a553e2ae495p-1, + 0x1.f7821f7821f78p-2, 0x1.fcefdc2f9853dp-2, 0x1.ea6e1ea6e1ea7p-2, + 0x1.e1ac273f54bd1p-2, 0x1.d11025e4df0edp-2, 0x1.f011397ca9a97p-2, + 0x1.0789268a3a221p-1, 0x1.0311ac652c218p-1, 0x1.124ef2c57a054p-1, + 0x1.218c3925c7e92p-1, 0x1.30c97f8615cdp-1, 0x1.2bb512bb512bbp-1, + 0x1.3ab153ab153abp-1, 0x1.4446037aeee8p-1, 0x1.4d8b94d8b94d9p-1, + 0x1.484039164cb6p-1, 0x1.5685cc43ca7b2p-1, 0x1.512bb512bb514p-1, + 0x1.5f3831f3831f4p-1, 0x1.59d0ee3a98bc4p-1, 0x1.757b3eba2a12fp-1, + 0x1.6fd296fd296fdp-1, 0x1.6a552d92381a1p-1, 0x1.5fd4906c96f07p-1, + 0x1.67a6167a6167ap-1, 0x1.747e4e2352991p-1, 0x1.6a2576a2576a2p-1, + 0x1.76a2576a2576ap-1, 0x1.831f3831f3832p-1, 0x1.8f9c18f9c18fap-1, + 0x1.9c18f9c18f9c2p-1, 0x1.b512bb512bb51p-1, 0x1.c18f9c18f9c19p-1, + 0x1.ce0c7ce0c7ce1p-1, 0x1.da895da895da9p-1, 0x1.e7063e7063e7p-1, + 0x1.f3831f3831f38p-1, 0x1p+0), youden = c(0x1p+0, 0x1.f3831f3831f38p-1, + 0x1.0b88ac0de0163p+0, 0x1.163356b88ac0ep+0, 0x1.1d4fc87fa732ap+0, + 0x1.1fe9cca947754p+0, 0x1.2f00b19ab5c46p+0, 0x1.37d926283d0d3p+0, + 0x1.443fd399528efp+0, 0x1.4c3a46c3a46c4p+0, 0x1.45fbd65fbd66p+0, + 0x1.4429a0429a043p+0, 0x1.4c24136cebe18p+0, 0x1.5a5cf6fb24c5p+0, + 0x1.5ec93141e8698p+0, 0x1.65e5a30904db4p+0, 0x1.6973dbec93142p+0, + 0x1.70904db3af85ep+0, 0x1.6a51dd4fc87fap+0, 0x1.64136cebe1796p+0, + 0x1.67a1a5cf6fb24p+0, 0x1.6163356b88ac1p+0, 0x1.5b24c507a1a5dp+0, + 0x1.54e654a3ba9f9p+0, 0x1.58748d8748d88p+0, 0x1.52361d2361d24p+0, + 0x1.4f85e5a30904ep+0, 0x1.4cd5ae22b0378p+0, 0x1.5063e7063e706p+0, + 0x1.4a2576a2576a2p+0, 0x1.4db3af85e5a3p+0, 0x1.47753f21fe9ccp+0, + 0x1.4b0378058cd5bp+0, 0x1.3e86973dbec93p+0, 0x1.4214d0214d021p+0, + 0x1.45a30904db3bp+0, 0x1.4cbf7acbf7accp+0, 0x1.4a0f434b9edf6p+0, + 0x1.43d0d2e7b7d92p+0, 0x1.4aed44aed44afp+0, 0x1.44aed44aed44bp+0, + 0x1.3e7063e7063e7p+0, 0x1.3831f3831f383p+0, 0x1.31f3831f3831fp+0, + 0x1.2576a2576a257p+0, 0x1.1f3831f3831f4p+0, 0x1.18f9c18f9c19p+0, + 0x1.12bb512bb512cp+0, 0x1.0c7ce0c7ce0c8p+0, 0x1.063e7063e7064p+0, + 0x1p+0), closest.topleft = c(0x1p+0, 0x1.0026fc7f508fcp+0, + 0x1.bba9a08035aa9p-1, 0x1.94d908cc0fbd8p-1, 0x1.7bf57a932c2f6p-1, + 0x1.3a9a08035aa96p-1, 0x1.fd0e6285bdb01p-2, 0x1.9c44c669e0a2dp-2, + 0x1.3fc3061228843p-2, 0x1.1406f8b4fd12ap-2, 0x1.20d1d27b6c3eap-2, + 0x1.18b9dca282bb8p-2, 0x1.f9da593c0e914p-3, 0x1.ae00464589d39p-3, + 0x1.a0bd65809f12ep-3, 0x1.844b9e642d4bep-3, 0x1.77422321ce7b3p-3, + 0x1.5f8dfd34c4ffep-3, 0x1.8552988ad044ep-3, 0x1.ad86fbd5e4862p-3, + 0x1.a2dc512b39db8p-3, 0x1.cd807c6b5718cp-3, 0x1.fa946fa07d526p-3, + 0x1.150c156556441p-2, 0x1.101be2d3f44a6p-2, 0x1.2915a46390636p-2, + 0x1.3ebc3a20425c7p-2, 0x1.55ffd69b6c2f4p-2, 0x1.523f0c55e4489p-2, + 0x1.6ee079d50ddbep-2, 0x1.6b84d2537950dp-2, 0x1.895e23cd27621p-2, + 0x1.86679f0f8632ap-2, 0x1.c5c1edf26fcf8p-2, 0x1.c3308bf8c1fbdp-2, + 0x1.c1044cc30783bp-2, 0x1.bddb36a36ca67p-2, 0x1.de5f5d22c7cdp-2, + 0x1.008c1f4343eb6p-1, 0x1.0026fc7f508fcp-1, 0x1.121f5f2e72d3bp-1, + 0x1.24b3b3dad756bp-1, 0x1.37e3fa847e18bp-1, 0x1.4bb0332b6719cp-1, + 0x1.751c7a70ffd9p-1, 0x1.8abc890faf974p-1, 0x1.a0f889aba1948p-1, + 0x1.b7d07c44d5d0dp-1, 0x1.cf4460db4c4c2p-1, 0x1.e754376f05068p-1, + 0x1p+0)), class = "data.frame", row.names = c(NA, -51L)) +r.s100b.reversed <- roc(aSAH$outcome, aSAH$s100b, direction=">", quiet=TRUE) - -r.100b.reversed <- roc(aSAH$outcome, aSAH$s100b, direction=">", quiet=TRUE) +# expected.coords.reverse <- coords(r.s100b.reversed, c(0.05, 0.055, 0.205, 0.52), ret="all") +# dump("expected.coords.reverse", "", control = c("all", "hexNumeric")) expected.coords.reverse <- - structure(c(0.05, 0.888888888888889, 0.024390243902439, 0.575221238938053, - 64, 1, 40, 8, 0.615384615384615, 0.111111111111111, 0.111111111111111, - 0.975609756097561, 0.424778761061947, 0.384615384615385, 0.888888888888889, - 0.055, 0.888888888888889, 0.024390243902439, 0.575221238938053, - 64, 1, 40, 8, 0.615384615384615, 0.111111111111111, 0.111111111111111, - 0.975609756097561, 0.424778761061947, 0.384615384615385, 0.888888888888889, - 0.205, 0.194444444444444, 0.365853658536585, 0.256637168141593, - 14, 15, 26, 58, 0.35, 0.205479452054795, 0.805555555555556, 0.634146341463415, - 0.743362831858407, 0.65, 0.794520547945206, 0.52, 0, 0.731707317073171, - 0.265486725663717, 0, 30, 11, 72, 0, 0.294117647058824, 1, 0.268292682926829, - 0.734513274336283, 1, 0.705882352941176), .Dim = c(15L, 4L), .Dimnames = list( - c("threshold", "specificity", "sensitivity", "accuracy", - "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", - "1-accuracy", "1-npv", "1-ppv"), NULL)) +structure(list(threshold = c(0x1.999999999999ap-5, 0x1.c28f5c28f5c29p-5, +0x1.a3d70a3d70a3dp-3, 0x1.0a3d70a3d70a4p-1), specificity = c(0x1.c71c71c71c71cp-1, +0x1.c71c71c71c71cp-1, 0x1.8e38e38e38e39p-3, 0x0p+0), sensitivity = c(0x1.8f9c18f9c18fap-6, +0x1.8f9c18f9c18fap-6, 0x1.76a2576a2576ap-2, 0x1.76a2576a2576ap-1 +), accuracy = c(0x1.268365f268366p-1, 0x1.268365f268366p-1, 0x1.06cbe4d06cbe5p-2, +0x1.0fdbc090fdbc1p-2), tn = c(0x1p+6, 0x1p+6, 0x1.cp+3, 0x0p+0 +), tp = c(0x1p+0, 0x1p+0, 0x1.ep+3, 0x1.ep+4), fn = c(0x1.4p+5, +0x1.4p+5, 0x1.ap+4, 0x1.6p+3), fp = c(0x1p+3, 0x1p+3, 0x1.dp+5, +0x1.2p+6), npv = c(0x1.3b13b13b13b14p-1, 0x1.3b13b13b13b14p-1, +0x1.6666666666666p-2, 0x0p+0), ppv = c(0x1.c71c71c71c71cp-4, +0x1.c71c71c71c71cp-4, 0x1.a4d269349a4d2p-3, 0x1.2d2d2d2d2d2d3p-2 +), fdr = c(0x1.c71c71c71c71cp-1, 0x1.c71c71c71c71cp-1, 0x1.96cb65b2d96cbp-1, +0x1.6969696969697p-1), fpr = c(0x1.c71c71c71c72p-4, 0x1.c71c71c71c72p-4, +0x1.9c71c71c71c72p-1, 0x1p+0), tpr = c(0x1.8f9c18f9c18fap-6, +0x1.8f9c18f9c18fap-6, 0x1.76a2576a2576ap-2, 0x1.76a2576a2576ap-1 +), tnr = c(0x1.c71c71c71c71cp-1, 0x1.c71c71c71c71cp-1, 0x1.8e38e38e38e39p-3, +0x0p+0), fnr = c(0x1.f3831f3831f38p-1, 0x1.f3831f3831f38p-1, +0x1.44aed44aed44bp-1, 0x1.12bb512bb512cp-2), `1-specificity` = c(0x1.c71c71c71c72p-4, +0x1.c71c71c71c72p-4, 0x1.9c71c71c71c72p-1, 0x1p+0), `1-sensitivity` = c(0x1.f3831f3831f38p-1, +0x1.f3831f3831f38p-1, 0x1.44aed44aed44bp-1, 0x1.12bb512bb512cp-2 +), `1-accuracy` = c(0x1.b2f9341b2f934p-2, 0x1.b2f9341b2f934p-2, +0x1.7c9a0d97c9a0ep-1, 0x1.78121fb78122p-1), `1-npv` = c(0x1.89d89d89d89d8p-2, +0x1.89d89d89d89d8p-2, 0x1.4cccccccccccdp-1, 0x1p+0), `1-ppv` = c(0x1.c71c71c71c71cp-1, +0x1.c71c71c71c71cp-1, 0x1.96cb65b2d96ccp-1, 0x1.6969696969696p-1 +), precision = c(0x1.c71c71c71c71cp-4, 0x1.c71c71c71c71cp-4, +0x1.a4d269349a4d2p-3, 0x1.2d2d2d2d2d2d3p-2), recall = c(0x1.8f9c18f9c18fap-6, +0x1.8f9c18f9c18fap-6, 0x1.76a2576a2576ap-2, 0x1.76a2576a2576ap-1 +), lr_pos = c(0x1.c18f9c18f9c16p-3, 0x1.c18f9c18f9c16p-3, 0x1.d11025e4df0edp-2, +0x1.76a2576a2576ap-1), lr_neg = c(0x1.18f9c18f9c19p+0, 0x1.18f9c18f9c19p+0, +0x1.a17310f29ec6p+1, Inf), youden = c(0x1.d399528eea7e4p-1, 0x1.d399528eea7e4p-1, +0x1.1edf6498a0f43p-1, 0x1.76a2576a2576ap-1), closest.topleft = c(0x1.eda663ae3ac1p-1, +0x1.eda663ae3ac1p-1, 0x1.0d125b0df7abdp+0, 0x1.126d582d13f33p+0 +)), class = "data.frame", row.names = c(NA, -4L)) diff --git a/tests/testthat/helper-roc.utils-expected.R b/tests/testthat/helper-roc.utils-expected.R index 4f3b3e8..02929fc 100644 --- a/tests/testthat/helper-roc.utils-expected.R +++ b/tests/testthat/helper-roc.utils-expected.R @@ -1,37 +1,46 @@ expected_roc_utils_calc_coords <- -structure(c(-1, -2, -3, -4, 1, 0.5, 0.1, 0, 0, 0.5, 0.9, 1, 0.36283185840708, -0.5, 0.609734513274336, 0.63716814159292, 0, 36, 64.8, 72, 41, -20.5, 4.1, 0, 0, 20.5, 36.9, 41, 72, 36, 7.2, 0, NaN, 0.63716814159292, -0.63716814159292, 0.63716814159292, 0.36283185840708, 0.36283185840708, -0.36283185840708, NaN, 1, 0.5, 0.1, 0, 0, 0.5, 0.9, 1, 1, 0.5, -0.1, 0, 0, 0.5, 0.9, 1, 0.63716814159292, 0.63716814159292, 0.63716814159292, -NaN, 1, 0.5, 0.1, 0, 0, 0.5, 0.9, 1, 0.63716814159292, 0.5, 0.390265486725664, -0.36283185840708, NaN, 0.36283185840708, 0.36283185840708, 0.36283185840708, -0.63716814159292, 0.63716814159292, 0.63716814159292, NaN, 0.36283185840708, -0.36283185840708, 0.36283185840708, NaN, 1, 0.5, 0.1, 0, 1, 0.50462962962963, -0.108333333333333, 0.00925925925925926, 0.00925925925925926, -0.252314814814815, 0.810092592592593, 1), .Dim = c(4L, 24L), .Dimnames = list( -NULL, c("threshold", "sensitivity", "specificity", "accuracy", -"tn", "tp", "fn", "fp", "npv", "ppv", "tpr", "tnr", "fpr", -"fnr", "fdr", "1-specificity", "1-sensitivity", "1-accuracy", -"1-npv", "1-ppv", "precision", "recall", "youden", "closest.topleft" -))) +structure(c(-1, -2, -3, -4, 1, 0.5, 0.10000000000000001, 0, 0, +0.5, 0.90000000000000002, 1, 0.36283185840707965, 0.5, 0.60973451327433625, +0.63716814159292035, 0, 36, 64.799999999999997, 72, 41, 20.5, +4.1000000000000005, 0, 0, 20.5, 36.899999999999999, 41, 72, 36, +7.2000000000000028, 0, NaN, 0.63716814159292035, 0.63716814159292035, +0.63716814159292035, 0.36283185840707965, 0.36283185840707965, +0.36283185840707954, NaN, 1, 0.5, 0.10000000000000001, 0, 0, +0.5, 0.90000000000000002, 1, 1, 0.5, 0.099999999999999978, 0, +0, 0.5, 0.89999999999999991, 1, 0.63716814159292035, 0.63716814159292035, +0.63716814159292035, NaN, 1, 0.5, 0.099999999999999978, 0, 0, +0.5, 0.90000000000000002, 1, 0.63716814159292035, 0.5, 0.39026548672566375, +0.36283185840707965, NaN, 0.36283185840707965, 0.36283185840707965, +0.36283185840707965, 0.63716814159292035, 0.63716814159292035, +0.63716814159292046, NaN, 0.36283185840707965, 0.36283185840707965, +0.36283185840707954, NaN, 1, 0.5, 0.10000000000000001, 0, 1, +1, 1.0000000000000002, NaN, NaN, 1, 1, 1, 1, 0.50462962962962965, +0.10833333333333334, 0.009259259259259257, 0.009259259259259257, +0.25231481481481483, 0.8100925925925927, 1), .Dim = c(4L, 26L +), .Dimnames = list(NULL, c("threshold", "sensitivity", "specificity", +"accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "tpr", "tnr", +"fpr", "fnr", "fdr", "1-specificity", "1-sensitivity", "1-accuracy", +"1-npv", "1-ppv", "precision", "recall", "lr_pos", "lr_neg", +"youden", "closest.topleft"))) expected_roc_utils_calc_coords.percent <- -structure(c(-1, -2, -3, -4, 100, 50, 10, 0, 0, 50, 90, 100, 36.283185840708, -50, 60.9734513274336, 63.716814159292, 0, 36, 64.8, 72, 41, 20.5, -4.1, 0, 0, 20.5, 36.9, 41, 72, 36, 7.2, 0, NaN, 63.716814159292, -63.716814159292, 63.716814159292, 36.283185840708, 36.283185840708, -36.283185840708, NaN, 100, 50, 10, 0, 0, 50, 90, 100, 100, 50, -10, 0, 0, 50, 90, 100, 63.716814159292, 63.716814159292, 63.716814159292, -NaN, 100, 50, 10, 0, 0, 50, 90, 100, 63.716814159292, 50, 39.0265486725664, -36.283185840708, NaN, 36.283185840708, 36.283185840708, 36.283185840708, -63.716814159292, 63.716814159292, 63.716814159292, NaN, 36.283185840708, -36.283185840708, 36.283185840708, NaN, 100, 50, 10, 0, 100, 50.462962962963, -10.8333333333333, 0.925925925925926, 0.925925925925926, 25.2314814814815, -81.0092592592593, 100), .Dim = c(4L, 24L), .Dimnames = list(NULL, -c("threshold", "sensitivity", "specificity", "accuracy", -"tn", "tp", "fn", "fp", "npv", "ppv", "tpr", "tnr", "fpr", -"fnr", "fdr", "1-specificity", "1-sensitivity", "1-accuracy", -"1-npv", "1-ppv", "precision", "recall", "youden", "closest.topleft" -))) \ No newline at end of file +structure(c(-1, -2, -3, -4, 100, 50, 10, 0, 0, 50, 90, 100, 36.283185840707965, +50, 60.973451327433622, 63.716814159292035, 0, 36, 64.799999999999997, +72, 41, 20.5, 4.0999999999999996, 0, 0, 20.5, 36.899999999999999, +41, 72, 36, 7.2000000000000028, 0, NaN, 63.716814159292035, 63.716814159292042, +63.716814159292035, 36.283185840707965, 36.283185840707965, 36.283185840707951, +NaN, 100, 50, 10, 0, 0, 50, 90, 100, 100, 50, 10, 0, 0, 50, 90, +100, 63.716814159292035, 63.716814159292035, 63.716814159292042, +NaN, 100, 50, 10, 0, 0, 50, 90, 100, 63.716814159292035, 50, +39.026548672566378, 36.283185840707965, NaN, 36.283185840707965, +36.283185840707958, 36.283185840707965, 63.716814159292035, 63.716814159292035, +63.716814159292049, NaN, 36.283185840707965, 36.283185840707965, +36.283185840707951, NaN, 100, 50, 10, 0, 1, 1, 1.0000000000000002, +NaN, NaN, 1, 1, 1, 100, 50.462962962962962, 10.833333333333334, +0.92592592592592571, 0.92592592592592571, 25.231481481481481, +81.009259259259267, 100), .Dim = c(4L, 26L), .Dimnames = list( + NULL, c("threshold", "sensitivity", "specificity", "accuracy", + "tn", "tp", "fn", "fp", "npv", "ppv", "tpr", "tnr", "fpr", + "fnr", "fdr", "1-specificity", "1-sensitivity", "1-accuracy", + "1-npv", "1-ppv", "precision", "recall", "lr_pos", "lr_neg", + "youden", "closest.topleft"))) diff --git a/tests/testthat/test-coords.R b/tests/testthat/test-coords.R index 1dda2f9..e523159 100644 --- a/tests/testthat/test-coords.R +++ b/tests/testthat/test-coords.R @@ -4,14 +4,14 @@ data(aSAH) context("coords") test_that("coords with thresholds works", { - return.rows <- c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv", "youden", "closest.topleft") - obtained <- coords(r.s100b, "all", ret = return.rows, transpose=TRUE) - expect_equal(obtained, expected.coords[return.rows,]) + return.rows <- c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv", "lr_pos", "lr_neg", "youden", "closest.topleft") + obtained <- coords(r.s100b, "all", ret = return.rows) + expect_equal(obtained, expected.coords[,return.rows]) }) test_that("coords returns all thresholds by default", { - obtained <- coords(r.s100b, transpose=TRUE) - expect_equal(obtained, expected.coords[c("threshold", "specificity", "sensitivity"),]) + obtained <- coords(r.s100b) + expect_equal(obtained, expected.coords[,c("threshold", "specificity", "sensitivity")]) # but not if it's an empty numeric, as this might be indicative of user error expect_error(coords(r.s100b, numeric(0)), "length") }) @@ -31,29 +31,29 @@ test_that("coords returns all columns with ret = 'all' with smooth.roc", { }) -test_that("coords with transpose = FALSE works", { +test_that("coords with transpose = TRUE works", { return.rows <- c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv", "youden", "closest.topleft") - obtained <- coords(r.s100b, "all", ret = return.rows, transpose = FALSE) - expect_equal(obtained, as.data.frame(t(expected.coords[return.rows,]))) - obtained <- coords(r.s100b, transpose = FALSE) - expect_equal(obtained, as.data.frame(t(expected.coords[c("threshold", "specificity", "sensitivity"),]))) + obtained <- coords(r.s100b, "all", ret = return.rows, transpose = TRUE) + expect_equal(obtained, t(expected.coords[,return.rows])) + obtained <- coords(r.s100b, transpose = TRUE) + expect_equal(obtained, t(expected.coords[,c("threshold", "specificity", "sensitivity")])) # With drop=TRUE - obtained <- coords(r.s100b, "all", ret = "se", transpose = FALSE, drop=TRUE) + obtained <- coords(r.s100b, "all", ret = "se", transpose = TRUE, drop=TRUE) expect_is(obtained, "numeric") # Not why drop.data.frame returns a list, skipping # obtained <- coords(r.s100b, "best", ret = "all", transpose = FALSE, drop=TRUE) # With drop=FALSE - obtained <- coords(r.s100b, "all", ret = "se", transpose = FALSE, drop=FALSE) - expect_is(obtained, "data.frame") + obtained <- coords(r.s100b, "all", ret = "se", transpose = TRUE, drop=FALSE) + expect_is(obtained, "matrix") }) test_that("coords with ret='all' works", { - obtained <- coords(r.s100b, "all", ret = "all", transpose=TRUE) - expect_equal(dim(obtained), c(24, 51)) - expect_equal(obtained[rownames(expected.coords),], expected.coords) + obtained <- coords(r.s100b, "all", ret = "all", transpose=FALSE) + expect_equal(dim(obtained), c(51, 26)) + expect_equal(obtained[,colnames(expected.coords)], expected.coords) }) @@ -64,117 +64,125 @@ test_that("coords with ret='all' doesn't accept additional options", { test_that("coords with percent works", { return.rows <- "all" - percent.rows <- c("specificity", "sensitivity", "accuracy", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv", "youden", "closest.topleft", "fdr", "fpr", "tpr", "tnr", "fnr", "precision", "recall") - obtained.percent <- coords(r.s100b.percent, "all", ret = return.rows, transpose=TRUE) + percent.cols <- c("specificity", "sensitivity", "accuracy", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv", "youden", "closest.topleft", "fdr", "fpr", "tpr", "tnr", "fnr", "precision", "recall") + obtained.percent <- coords(r.s100b.percent, "all", ret = return.rows, transpose=FALSE) # Adjust for percent - obtained.percent[percent.rows,] <- obtained.percent[percent.rows,] / 100 + obtained.percent[,percent.cols] <- obtained.percent[,percent.cols] / 100 expect_equal(obtained.percent, expected.coords) }) test_that("coords with local maximas thresholds works", { return.rows <- "all" - obtained <- coords(r.s100b, "local maximas", ret = return.rows, transpose=TRUE) - expected.thresholds <- c(-Inf, 0.065, 0.075, 0.085, 0.095, 0.105, 0.115, 0.135, 0.155, 0.205, 0.245, 0.29, 0.325, 0.345, 0.395, 0.435, 0.475, 0.485, 0.51) - expect_equal(as.vector(obtained["threshold",]), expected.thresholds) - expect_equivalent(obtained, expected.coords[,expected.coords["threshold",] %in% expected.thresholds]) + obtained <- coords(r.s100b, "local maximas", ret = return.rows) + #expected.thresholds <- c(-Inf, 0.065, 0.075, 0.085, 0.095, 0.105, 0.115, 0.135, 0.155, 0.205, 0.245, 0.29, 0.325, 0.345, 0.395, 0.435, 0.475, 0.485, 0.51) + expected.thresholds = c(-Inf, 0x1.0a3d70a3d70a4p-4, 0x1.3333333333334p-4, + 0x1.5c28f5c28f5c2p-4, 0x1.851eb851eb852p-4, 0x1.ae147ae147ae2p-4, + 0x1.d70a3d70a3d7p-4, 0x1.147ae147ae148p-3, 0x1.3d70a3d70a3d7p-3, + 0x1.a3d70a3d70a3ep-3, 0x1.f5c28f5c28f5cp-3, 0x1.28f5c28f5c29p-2, + 0x1.4cccccccccccdp-2, 0x1.6147ae147ae14p-2, 0x1.947ae147ae148p-2, + 0x1.bd70a3d70a3d7p-2, 0x1.e666666666666p-2, 0x1.f0a3d70a3d70ap-2, + 0x1.051eb851eb852p-1) + expect_equal(as.vector(obtained[,"threshold"]), expected.thresholds) + expect_equivalent(obtained, expected.coords[expected.coords[,"threshold"] %in% expected.thresholds,]) }) test_that("coords with best threshold works", { return.rows <- "all" - obtained <- coords(r.s100b, "best", ret = return.rows, transpose=TRUE) - expect_equal(obtained, expected.coords[,expected.coords["threshold",] == 0.205]) + obtained <- coords(r.s100b, "best", ret = return.rows) + expect_equivalent(obtained, expected.coords[abs(expected.coords[,"threshold"] - 0.205) < 0.001, , drop=FALSE]) }) test_that("coords with arbitrary thresholds works", { return.rows <- c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv", "youden", "closest.topleft") - obtained <- coords(r.s100b, c(0.205, 0.055), input = "threshold", ret = return.rows, transpose=TRUE) - expect_equivalent(obtained, expected.coords[return.rows, c(18, 4)]) + obtained <- coords(r.s100b, c(0.205, 0.055), input = "threshold", ret = return.rows) + expect_equivalent(obtained, expected.coords[c(18, 4), return.rows]) }) test_that("coords with arbitrary thresholds at exact data point works", { return.rows <- "all" expect_equal(sum(aSAH$s100b == 0.05), 3) expect_equal(sum(aSAH$s100b == 0.52), 1) - obtained <- coords(r.s100b, c(0.05, 0.52), input = "threshold", ret = return.rows, transpose=TRUE) - expect_equivalent(obtained[-1,], expected.coords[-1, c(3, 40)]) + obtained <- coords(r.s100b, c(0.05, 0.52), input = "threshold", ret = return.rows) + expect_equivalent(obtained[,-1], expected.coords[c(3, 40), -1]) }) test_that("coords with arbitrary thresholds works with direction=>", { - obtained <- coords(r.100b.reversed, c(0.05, 0.055, 0.205, 0.52), input = "threshold", ret = c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv"), transpose=TRUE) + obtained <- coords(r.s100b.reversed, c(0.05, 0.055, 0.205, 0.52), input = "threshold", ret = "all") expect_equivalent(obtained, expected.coords.reverse) }) test_that("coords with single arbitrary threshold works", { return.rows <- "all" - obtained <- coords(r.s100b, c(0.205), input = "threshold", ret = return.rows, transpose=TRUE) - expect_equal(obtained, expected.coords[, c(18), drop=T]) + obtained <- coords(r.s100b, c(0.205), input = "threshold", ret = return.rows) + expect_equivalent(obtained, expected.coords[18, , drop=FALSE]) }) test_that("coords with arbitrary thresholds at exact data point works", { expect_equal(sum(aSAH$s100b == 0.05), 3) expect_equal(sum(aSAH$s100b == 0.52), 1) - obtained <- coords(r.s100b, c(0.05), input = "threshold", ret = "all", transpose=TRUE) - expect_equal(obtained[-1], expected.coords[-1, 3]) - obtained <- coords(r.s100b, c(0.52), input = "threshold", ret = "all", transpose=TRUE) - expect_equal(obtained[-1], expected.coords[-1, 40]) + obtained <- coords(r.s100b, c(0.05), input = "threshold", ret = "all") + expect_equivalent(obtained[,-1], expected.coords[3,-1]) + obtained <- coords(r.s100b, c(0.52), input = "threshold", ret = "all") + expect_equivalent(obtained[,-1], expected.coords[40, -1]) }) test_that("coords with arbitrary thresholds works with direction=>", { - obtained <- coords(r.100b.reversed, c(0.05), input = "threshold", ret = c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv"), transpose=TRUE) - expect_equal(obtained, expected.coords.reverse[, 1]) - obtained <- coords(r.100b.reversed, c(0.055), input = "threshold", ret = c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv"), transpose=TRUE) - expect_equal(obtained, expected.coords.reverse[, 2]) - obtained <- coords(r.100b.reversed, c(0.205), input = "threshold", ret = c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv"), transpose=TRUE) - expect_equal(obtained, expected.coords.reverse[, 3]) - obtained <- coords(r.100b.reversed, c(0.52), input = "threshold", ret = c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv"), transpose=TRUE) - expect_equal(obtained, expected.coords.reverse[, 4]) + obtained <- coords(r.s100b.reversed, c(0.05), input = "threshold", ret = "all") + expect_equivalent(obtained, expected.coords.reverse[1,]) + obtained <- coords(r.s100b.reversed, c(0.055), input = "threshold", ret = "all") + expect_equivalent(obtained, expected.coords.reverse[2,]) + obtained <- coords(r.s100b.reversed, c(0.205), input = "threshold", ret = "all") + expect_equivalent(obtained, expected.coords.reverse[3, ]) + obtained <- coords(r.s100b.reversed, c(0.52), input = "threshold", ret = "all") + expect_equivalent(obtained, expected.coords.reverse[4,]) }) test_that("coords with sensitivity works", { - obtained <- coords(r.s100b, seq(0, 1, .1), input = "sensitivity", ret = c("threshold", "specificity", "sensitivity"), transpose=TRUE) - expect_equal(unname(obtained["threshold",]), c(Inf, rep(NA, 9), -Inf)) - expect_equal(unname(obtained["sensitivity",]), seq(0, 1, .1)) - expect_equal(unname(obtained["specificity",]), c(1, 1, 1, 0.972222222222222, 0.888888888888889, 0.833333333333333, 0.805555555555556, 0.56875, 0.447222222222222, 0.230555555555556, 0)) + obtained <- coords(r.s100b, seq(0, 1, .1), input = "sensitivity", ret = c("threshold", "specificity", "sensitivity")) + expect_equal(obtained[,"threshold"], c(Inf, rep(NA, 9), -Inf)) + expect_equal(obtained[,"sensitivity"], seq(0, 1, .1)) + expect_equal(obtained[,"specificity"], c(1, 1, 1, 0.972222222222222, 0.888888888888889, 0.833333333333333, 0.805555555555556, 0.56875, 0.447222222222222, 0.230555555555556, 0)) }) test_that("coords with sensitivity works with percent", { - obtained <- coords(r.s100b.percent, seq(0, 100, 10), input = "sensitivity", ret = c("threshold", "specificity", "sensitivity"), transpose=TRUE) - expect_equal(unname(obtained["threshold",]), c(Inf, rep(NA, 9), -Inf)) - expect_equal(unname(obtained["sensitivity",]), seq(0, 100, 10)) - expect_equal(unname(obtained["specificity",]), c(1, 1, 1, 0.972222222222222, 0.888888888888889, 0.833333333333333, 0.805555555555556, 0.56875, 0.447222222222222, 0.230555555555556, 0) * 100) + obtained <- coords(r.s100b.percent, seq(0, 100, 10), input = "sensitivity", ret = c("threshold", "specificity", "sensitivity")) + expect_equal(obtained[,"threshold"], c(Inf, rep(NA, 9), -Inf)) + expect_equal(obtained[,"sensitivity"], seq(0, 100, 10)) + expect_equal(obtained[,"specificity"], c(1, 1, 1, 0.972222222222222, 0.888888888888889, 0.833333333333333, 0.805555555555556, 0.56875, 0.447222222222222, 0.230555555555556, 0) * 100) }) test_that("coords with specificity works", { - obtained <- coords(r.s100b, seq(0, 1, .1), input = "specificity", ret = c("threshold", "specificity", "sensitivity"), transpose=TRUE) - expect_equal(unname(obtained["threshold",]), c(-Inf, rep(NA, 9), 0.51)) - expect_equal(unname(obtained["specificity",]), seq(0, 1, .1)) - expect_equal(unname(obtained["sensitivity",]), c(1, 0.975609756097561, 0.921951219512195, 0.879674796747967, 0.823693379790941, 0.774390243902439, 0.675609756097561, 0.655284552845528, 0.634146341463415, 0.390243902439024, 0.292682926829268)) + obtained <- coords(r.s100b, seq(0, 1, .1), input = "specificity", ret = c("threshold", "specificity", "sensitivity")) + expect_equal(obtained[,"threshold"], c(-Inf, rep(NA, 9), 0.51)) + expect_equal(obtained[,"specificity"], seq(0, 1, .1)) + expect_equal(obtained[,"sensitivity"], c(1, 0.975609756097561, 0.921951219512195, 0.879674796747967, 0.823693379790941, 0.774390243902439, 0.675609756097561, 0.655284552845528, 0.634146341463415, 0.390243902439024, 0.292682926829268)) }) test_that("coords with specificity works with percent", { - obtained <- coords(r.s100b.percent, seq(0, 100, 10), input = "specificity", ret = c("threshold", "specificity", "sensitivity"), transpose=TRUE) - expect_equal(unname(obtained["threshold",]), c(-Inf, rep(NA, 9), 0.51)) - expect_equal(unname(obtained["specificity",]), seq(0, 100, 10)) - expect_equal(unname(obtained["sensitivity",]), c(1, 0.975609756097561, 0.921951219512195, 0.879674796747967, 0.823693379790941, 0.774390243902439, 0.675609756097561, 0.655284552845528, 0.634146341463415, 0.390243902439024, 0.292682926829268) * 100) + obtained <- coords(r.s100b.percent, seq(0, 100, 10), input = "specificity", ret = c("threshold", "specificity", "sensitivity")) + expect_equal(obtained[,"threshold"], c(-Inf, rep(NA, 9), 0.51)) + expect_equal(obtained[,"specificity"], seq(0, 100, 10)) + expect_equal(obtained[,"sensitivity"], c(1, 0.975609756097561, 0.921951219512195, 0.879674796747967, 0.823693379790941, 0.774390243902439, 0.675609756097561, 0.655284552845528, 0.634146341463415, 0.390243902439024, 0.292682926829268) * 100) }) + test_that("coords with specificity works with as.list", { expect_warning(obtained <- coords(r.s100b.percent, "best", ret = c("threshold", "specificity", "accuracy"), as.list = TRUE), "as.list") expect_equal(obtained, list( threshold = 0.205, - specificity = unname(expected.coords["specificity", 18]) * 100, - accuracy = unname(expected.coords["accuracy", 18]) * 100 + specificity = unname(expected.coords[18, "specificity"]) * 100, + accuracy = unname(expected.coords[18, "accuracy"]) * 100 )) }) @@ -184,8 +192,8 @@ test_that("coords with specificity works with as.list and drop=FALSE", { as.list = TRUE, drop = FALSE), "as.list") expect_equal(obtained[[1]], list( threshold = 0.205, - specificity = unname(expected.coords["specificity", 18]) * 100, - accuracy = unname(expected.coords["accuracy", 18]) * 100 + specificity = unname(expected.coords[18, "specificity"]) * 100, + accuracy = unname(expected.coords[18, "accuracy"]) * 100 )) }) @@ -196,13 +204,13 @@ test_that("coords with specificity works with as.list and several thresholds", { as.list = TRUE, drop = FALSE), "as.list") expect_equal(obtained[[1]], list( threshold = 0.205, - specificity = unname(expected.coords["specificity", 18]) * 100, - accuracy = unname(expected.coords["accuracy", 18]) * 100 + specificity = unname(expected.coords[18, "specificity"]) * 100, + accuracy = unname(expected.coords[18, "accuracy"]) * 100 )) expect_equal(obtained[[2]], list( threshold = 0.51, - specificity = unname(expected.coords["specificity", 40]) * 100, - accuracy = unname(expected.coords["accuracy", 40]) * 100 + specificity = unname(expected.coords[40, "specificity"]) * 100, + accuracy = unname(expected.coords[40, "accuracy"]) * 100 )) }) @@ -223,13 +231,13 @@ test_that("drop works", { test_that("as.matrix works", { obtained <- coords(r.s100b, c(0.51, 0.205), ret="sensitivity", transpose = FALSE, as.matrix = TRUE) - expect_equal(obtained, t(expected.coords["sensitivity", c(40, 18), drop = FALSE])) + expect_equivalent(obtained, as.matrix(expected.coords[c(40, 18), "sensitivity", drop = FALSE])) }) test_that("as.matrix works with drop=TRUE", { obtained <- coords(r.s100b, c(0.51, 0.205), ret="sensitivity", transpose = FALSE, as.matrix = TRUE, drop = TRUE) - expect_equal(obtained, expected.coords["sensitivity", c(40, 18), drop = TRUE]) + expect_equal(obtained, expected.coords[c(40, 18), "sensitivity", drop = TRUE]) }) @@ -238,21 +246,19 @@ test_that("coords returns the correct basic values ", { ret = c("t", "tp", "fp", "tn", "fn", "sp", "se", "acc", "npv", "ppv", "precision", "recall", - "tpr", "fpr", "tnr", "fnr", "fdr"), - transpose=TRUE) + "tpr", "fpr", "tnr", "fnr", "fdr")) obtained.percent <- coords(r.s100b.percent, 0.205, ret = c("t", "tp", "fp", "tn", "fn", "sp", "se", "acc", "npv", "ppv", "precision", "recall", - "tpr", "fpr", "tnr", "fnr", "fdr"), - transpose=TRUE) + "tpr", "fpr", "tnr", "fnr", "fdr")) # We assume the following values: # tp fp tn fn N # 26 14 58 15 113 - expected <- c( + expected <- data.frame( threshold = 0.205, tp = 26, fp = 14, @@ -272,24 +278,26 @@ test_that("coords returns the correct basic values ", { fdr = 14 / (26 + 14) ) - expect_equal(obtained, expected) - expect_equal(obtained.percent[1:5], expected[1:5]) - expect_equal(obtained.percent[6:17], expected[6:17]*100) + expect_equivalent(obtained, expected) + expect_equivalent(obtained.percent[,1:5], expected[,1:5]) + expect_equivalent(obtained.percent[,6:17], expected[,6:17]*100) }) -test_that("coords works with smooth.roc and x = 'best'", { +test_that("coords works with smooth.roc and x = 'best' and transpose=TRUE", { smooth.s100b <- smooth(r.s100b) expect <- structure(c(0.750857175922901, 0.608610567514677, 0.699245574642041, 54.0617166664488, 24.9530332681018, 16.0469667318982, 17.9382833335512, 0.771112992655678, 0.581773544045047, 0.418226455954953, 0.249142824077099, 0.608610567514677, 0.750857175922901, 0.391389432485323, 0.249142824077099, 0.391389432485323, 0.300754425357959, 0.228887007344322, 0.418226455954953, - 0.581773544045047, 0.608610567514677, 1.35946774343758, 0.215257834650296 - ), .Dim = c(23L, 1L), .Dimnames = list(c("specificity", "sensitivity", + 0.581773544045047, 0.608610567514677, 2.4428179690470926, 0.52125683157286817, + 1.35946774343758, 0.215257834650296 + ), .Dim = c(25L, 1L), .Dimnames = list(c("specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "fdr", "fpr", "tpr", "tnr", "fnr", "1-specificity", "1-sensitivity", "1-accuracy", - "1-npv", "1-ppv", "precision", "recall", "youden", "closest.topleft" + "1-npv", "1-ppv", "precision", "recall", "lr_pos", "lr_neg", + "youden", "closest.topleft" ), NULL)) @@ -335,11 +343,13 @@ test_that("coords works with smooth.roc and transpose = FALSE", { 0.771112992655678, 0.581773544045047, 0.418226455954953, 0.249142824077099, 0.608610567514677, 0.750857175922901, 0.391389432485323, 0.249142824077099, 0.391389432485323, 0.300754425357959, 0.228887007344322, 0.418226455954953, - 0.581773544045047, 0.608610567514677, 1.35946774343758, 0.215257834650296 - ), .Dim = c(23L, 1L), .Dimnames = list(c("specificity", "sensitivity", + 0.581773544045047, 0.608610567514677, 2.4428179690470926, 0.52125683157286817, + 1.35946774343758, 0.215257834650296 + ), .Dim = c(25L, 1L), .Dimnames = list(c("specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", "fdr", "fpr", "tpr", "tnr", "fnr", "1-specificity", "1-sensitivity", "1-accuracy", - "1-npv", "1-ppv", "precision", "recall", "youden", "closest.topleft" + "1-npv", "1-ppv", "precision", "recall", "lr_pos", "lr_neg", + "youden", "closest.topleft" ), NULL)) @@ -381,121 +391,79 @@ test_that("coords works with smooth.roc and transpose = FALSE", { test_that("coords works with smooth.roc and x = numeric", { smooth.s100b <- smooth(r.s100b) - expect <- structure(c(0.5, 0.797749392103789, 0.608032965276596, 36, 32.7077250762554, - 8.29227492374464, 36, 0.812782817364406, 0.476041450069183, 0.523958549930817, - 0.5, 0.797749392103789, 0.5, 0.202250607896211, 0.5, 0.202250607896211, - 0.391967034723404, 0.187217182635594, 0.523958549930817, 0.476041450069183, - 0.797749392103789, 1.29774939210379, 0.290905308394387, 0.9, - 0.412071871553968, 0.722964130386838, 64.8, 16.8949467337127, - 24.1050532662873, 7.2, 0.728867456002887, 0.701182157421994, - 0.298817842578006, 0.1, 0.412071871553968, 0.9, 0.587928128446032, - 0.1, 0.587928128446032, 0.277035869613162, 0.271132543997113, - 0.298817842578006, 0.701182157421994, 0.412071871553968, 1.31207187155397, - 0.355659484218054), .Dim = c(23L, 2L), .Dimnames = list(c("specificity", - "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", "ppv", - "fdr", "fpr", "tpr", "tnr", "fnr", "1-specificity", "1-sensitivity", - "1-accuracy", "1-npv", "1-ppv", "precision", "recall", "youden", - "closest.topleft"), NULL)) + expect <- structure(list(specificity = c(0.5, 0.90000000000000002), sensitivity = c(0.79774939210378937, +0.41207187155396763), accuracy = c(0.60803296527659623, 0.72296413038683782 +), tn = c(36, 64.799999999999997), tp = c(32.707725076255365, +16.894946733712672), fn = c(8.2922749237446354, 24.105053266287328 +), fp = c(36, 7.2000000000000028), npv = c(0.81278281736440605, +0.72886745600288694), ppv = c(0.47604145006918286, 0.70118215742199363 +), fdr = c(0.52395854993081703, 0.29881784257800637), fpr = c(0.5, +0.099999999999999978), tpr = c(0.79774939210378937, 0.41207187155396763 +), tnr = c(0.5, 0.90000000000000002), fnr = c(0.20225060789621063, +0.58792812844603237), `1-specificity` = c(0.5, 0.099999999999999978 +), `1-sensitivity` = c(0.20225060789621063, 0.58792812844603237 +), `1-accuracy` = c(0.39196703472340377, 0.27703586961316218), + `1-npv` = c(0.18721718263559395, 0.27113254399711306), `1-ppv` = c(0.52395854993081714, + 0.29881784257800637), precision = c(0.47604145006918286, + 0.70118215742199363), recall = c(0.79774939210378937, 0.41207187155396763 + ), lr_pos = c(1.5954987842075787, 4.1207187155396774), lr_neg = c(0.40450121579242126, + 0.65325347605114703), youden = c(1.2977493921037895, 1.3120718715539677 + ), closest.topleft = c(0.29090530839438672, 0.35565948421805432 + )), class = "data.frame", row.names = c(NA, -2L)) reduced.cols <- c("specificity", "sensitivity", "youden") - obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "sp", ret="all", transpose=TRUE) + obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "sp", ret="all") expect_equal(obtained, expect) - obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "spe", ret=reduced.cols, transpose=TRUE) - expect_equal(obtained, expect[reduced.cols,]) - - obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret="all", drop = TRUE, transpose=TRUE) - expect_equal(obtained, expect[, 2]) - - obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret=reduced.cols, drop = TRUE, transpose=TRUE) - expect_equal(obtained, expect[reduced.cols, 2]) - - obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret="all", drop = FALSE, transpose=TRUE) - expect_equal(obtained, expect[, 2, drop=FALSE]) - - obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret=reduced.cols, drop = FALSE, transpose=TRUE) - expect_equal(obtained, expect[reduced.cols, 2, drop=FALSE]) - - expect_warning(obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "specificity", ret="all", as.list = TRUE, drop = TRUE), "as.list") - expect_equal(obtained[[1]], as.list(expect[, 1])) - expect_equal(obtained[[2]], as.list(expect[, 2])) - - expect_warning(obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "specificity", ret=reduced.cols, as.list = TRUE, drop = TRUE), "as.list") - expect_equal(obtained[[1]], as.list(expect[reduced.cols, 1])) - expect_equal(obtained[[2]], as.list(expect[reduced.cols, 2])) - - expect_warning(obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret="all", as.list = TRUE, drop = TRUE), "as.list") - expect_equal(obtained, as.list(expect[, 2])) + obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "spe", ret=reduced.cols) + expect_equal(obtained, expect[,reduced.cols]) - expect_warning(obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret=reduced.cols, as.list = TRUE, drop = TRUE), "as.list") - expect_equal(obtained, as.list(expect[reduced.cols, 2])) + obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret="all") + expect_equivalent(obtained, expect[2,, drop=FALSE]) - expect_warning(obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret="all", as.list = TRUE, drop = FALSE), "as.list") - expect_equal(obtained[[1]], as.list(expect[, 2])) + obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret=reduced.cols) + expect_equivalent(obtained, expect[2, reduced.cols]) - expect_warning(obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret=reduced.cols, as.list = TRUE, drop = FALSE), "as.list") - expect_equal(obtained[[1]], as.list(expect[reduced.cols, 2])) + obtained <- coords(smooth.s100b, 0.9, input = "specificity", ret=reduced.cols) + expect_equivalent(obtained, expect[2, reduced.cols, drop=FALSE]) }) test_that("coords works with smooth.roc and x = numeric and input = 'se'", { smooth.s100b <- smooth(r.s100b) - expect <- structure(c(0.844189345484777, 0.5, 0.719306485618619, 60.781632874904, - 20.5, 20.5, 11.218367125096, 0.747790499834687, 0.646313220322748, - 0.353686779677252, 0.155810654515223, 0.5, 0.844189345484777, - 0.5, 0.155810654515223, 0.5, 0.280693514381381, 0.252209500165313, - 0.353686779677252, 0.646313220322748, 0.5, 1.34418934548478, - 0.274276960060462, 0.293322024198721, 0.9, 0.513444121613345, - 21.1191857423079, 36.9, 4.1, 50.8808142576921, 0.837425361710953, - 0.420365205222125, 0.579634794777875, 0.706677975801279, 0.9, - 0.293322024198721, 0.1, 0.706677975801279, 0.1, 0.486555878386655, - 0.162574638289047, 0.579634794777875, 0.420365205222125, 0.9, - 1.19332202419872, 0.509393761482593), .Dim = c(23L, 2L), .Dimnames = list( - c("specificity", "sensitivity", "accuracy", "tn", "tp", "fn", - "fp", "npv", "ppv", "fdr", "fpr", "tpr", "tnr", "fnr", "1-specificity", - "1-sensitivity", "1-accuracy", "1-npv", "1-ppv", "precision", - "recall", "youden", "closest.topleft"), NULL)) + expect <- structure(list(specificity = c(0.84418934548477731, 0.29332202419872122 +), sensitivity = c(0.5, 0.90000000000000002), accuracy = c(0.7193064856186191, +0.51344412161334452), tn = c(60.781632874903963, 21.119185742307927 +), tp = c(20.5, 36.899999999999999), fn = c(20.5, 4.1000000000000014 +), fp = c(11.218367125096037, 50.880814257692073), npv = c(0.74779049983468704, +0.83742536171095305), ppv = c(0.64631322032274796, 0.42036520522212539 +), fdr = c(0.35368677967725209, 0.57963479477787461), fpr = c(0.15581065451522269, +0.70667797580127878), tpr = c(0.5, 0.90000000000000002), tnr = c(0.84418934548477731, +0.29332202419872122), fnr = c(0.5, 0.10000000000000003), `1-specificity` = c(0.15581065451522269, +0.70667797580127878), `1-sensitivity` = c(0.5, 0.099999999999999978 +), `1-accuracy` = c(0.2806935143813809, 0.48655587838665548), + `1-npv` = c(0.25220950016531296, 0.16257463828904695), `1-ppv` = c(0.35368677967725204, + 0.57963479477787461), precision = c(0.64631322032274796, + 0.42036520522212539), recall = c(0.5, 0.90000000000000002 + ), lr_pos = c(3.2090231669693039, 1.2735645241802247), lr_neg = c(0.59228418680512263, + 0.34092223477992739), youden = c(1.3441893454847773, 1.1933220241987212 + ), closest.topleft = c(0.27427696006046209, 0.50939376148259274 + )), class = "data.frame", row.names = c(NA, -2L)) reduced.cols <- c("specificity", "sensitivity", "youden") - obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "se", ret="all", transpose=TRUE) + obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "se", ret="all") expect_equal(obtained, expect) - obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "se", ret=reduced.cols, transpose=TRUE) - expect_equal(obtained, expect[reduced.cols,]) - - obtained <- coords(smooth.s100b, 0.9, input = "se", ret="all", drop = TRUE, transpose=TRUE) - expect_equal(obtained, expect[, 2]) - - obtained <- coords(smooth.s100b, 0.9, input = "se", ret=reduced.cols, drop = TRUE, transpose=TRUE) - expect_equal(obtained, expect[reduced.cols, 2]) - - obtained <- coords(smooth.s100b, 0.9, input = "se", ret="all", drop = FALSE, transpose=TRUE) - expect_equal(obtained, expect[, 2, drop=FALSE]) - - obtained <- coords(smooth.s100b, 0.9, input = "se", ret=reduced.cols, drop = FALSE, transpose=TRUE) - expect_equal(obtained, expect[reduced.cols, 2, drop=FALSE]) + obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "se", ret=reduced.cols) + expect_equal(obtained, expect[,reduced.cols]) - expect_warning(obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "se", ret="all", as.list = TRUE, drop = TRUE), "as.list") - expect_equal(obtained[[1]], as.list(expect[, 1])) - expect_equal(obtained[[2]], as.list(expect[, 2])) + obtained <- coords(smooth.s100b, 0.9, input = "se", ret="all") + expect_equivalent(obtained, expect[2,, drop=FALSE]) - expect_warning(obtained <- coords(smooth.s100b, c(0.5, 0.9), input = "se", ret=reduced.cols, as.list = TRUE, drop = TRUE), "as.list") - expect_equal(obtained[[1]], as.list(expect[reduced.cols, 1])) - expect_equal(obtained[[2]], as.list(expect[reduced.cols, 2])) - - expect_warning(obtained <- coords(smooth.s100b, 0.9, input = "se", ret="all", as.list = TRUE, drop = TRUE), "as.list") - expect_equal(obtained, as.list(expect[, 2])) - - expect_warning(obtained <- coords(smooth.s100b, 0.9, input = "se", ret=reduced.cols, as.list = TRUE, drop = TRUE), "as.list") - expect_equal(obtained, as.list(expect[reduced.cols, 2])) - - expect_warning(obtained <- coords(smooth.s100b, 0.9, input = "se", ret="all", as.list = TRUE, drop = FALSE), "as.list") - expect_equal(obtained[[1]], as.list(expect[, 2])) - - expect_warning(obtained <- coords(smooth.s100b, 0.9, input = "se", ret=reduced.cols, as.list = TRUE, drop = FALSE), "as.list") - expect_equal(obtained[[1]], as.list(expect[reduced.cols, 2])) + obtained <- coords(smooth.s100b, 0.9, input = "se", ret=reduced.cols) + expect_equivalent(obtained, expect[2, reduced.cols, drop=FALSE]) }) @@ -637,3 +605,4 @@ test_that("Coords pick the right end of 'flat' bits of the curve, according to d 0.2926829268292683 # and not 0 ) }) +