*! Part of package matrixtools v. 0.31 *! Support: Niels Henrik Bruun, niels.henrik.bruun@gmail.com * 2023-01-07 Created * see https://en.wikipedia.org/wiki/Sensitivity_and_specificity /* mata mata clear mata mata set matalnum on */ mata: struct nx { string scalar name1, name2 // name1 = rowname, name2 = roweq real scalar n, x } struct nx scalar nx_fill(real scalar n, x, |string scalar nm2, nm1) { struct nx scalar nx nx.n = n nx.x = x nx.name2 = nm2 nx.name1 = nm1 return(nx) } struct nx rowvector nx2rowvector(struct nx rowvector nx) return(nx) end /* mata: // test struct nx and functions nx = nx_fill(10, 5, "rowname", "roweq") liststruct(nx) eltype(nx), orgtype(nx) nxv = nx2rowvector((nx, nx)) eltype(nxv), orgtype(nxv) end exit */ mata: class confusion_matrix { real scalar tp, fp real scalar fn, tn void scalar_fill(), matrix_fill() real scalar n_c_p(), n_c_n(), n_p_p(), n_p_n(), n() struct nx scalar sensitivity(), specificity(), accuracy(), ppv(), npv(), prevalence() //, roc_area() struct nx rowvector get_all_nx() } void confusion_matrix::scalar_fill(real scalar tp, real scalar fp, real scalar fn, real scalar tn) /* tp, fp, fn, tn */ { this.tp = tp this.fp = fp this.fn = fn this.tn = tn } void confusion_matrix::matrix_fill(real matrix m) /* tn, fp \ fn, tp (input from -tab condition(0/1) predicted(0/1)-, 1=+) */ { this.tp = m[2,2] this.fn = m[2,1] this.fp = m[1,2] this.tn = m[1,1] } real scalar confusion_matrix::n_c_p() return(this.tp + this.fn) // n condition positive real scalar confusion_matrix::n_c_n() return(this.fp + this.tn) // n condition negative real scalar confusion_matrix::n_p_p() return(this.tp + this.fp) // n predicted condition positive real scalar confusion_matrix::n_p_n() return(this.fn + this.tn) // n predicted condition negative real scalar confusion_matrix::n() return(this.n_c_p() + this.n_c_n()) // n total struct nx scalar confusion_matrix::sensitivity() { return( nx_fill(this.n_c_p(), this.tp, "P(TP|C+)", "Sensitivity") ) } struct nx scalar confusion_matrix::specificity() { return( nx_fill(this.n_c_n(), this.tn, "P(TN|C-)", "Specificity") ) } struct nx scalar confusion_matrix::prevalence() { return( nx_fill(this.n(), this.tp + this.fn, "P(C+)", "Prevalence") ) } struct nx scalar confusion_matrix::accuracy() { return( nx_fill(this.n(), this.tp + this.tn, "P(TP + TN)", "Accuracy") ) } struct nx scalar confusion_matrix::ppv() /* Positive predicted value */ { return( nx_fill(this.n_p_p(), this.tp, "P(TP|P+)", "PPV") ) } struct nx scalar confusion_matrix::npv() /* Negative predicted value */ { return( nx_fill(this.n_p_n(), this.tn, "P(TN|P-)", "NPV") ) } struct nx rowvector confusion_matrix::get_all_nx() { return( nx2rowvector( (this.sensitivity(), this.specificity(), this.prevalence(), this.accuracy(), this.ppv(), this.npv()) ) ) } end /* mata: // test confusion_matrix() cmm = confusion_matrix() /* tn, fn \ fp, tp (input from -tab condition(0/1) predicted(0/1)-, 1=+) */ m = 1820, 180 \ 10, 20 cmm.matrix_fill(m) liststruct(cmm.get_all_nx()) cms = confusion_matrix() /* tp, fp, fn, tn */ cms.scalar_fill(20, 10, 180, 1820) liststruct(cms.get_all_nx()) //cmm == cms end exit */ mata: class binci { private: real scalar scale, level, showcode, addquietly string scalar citype class nhb_mt_labelmatrix scalar lm public: void set(), to_lm() class nhb_mt_labelmatrix table() } void binci::set( |real scalar scale, real scalar level, string scalar citype, // exact wald wilson agresti jeffreys real scalar showcode, real scalar addquietly ) { this.scale = scale < . & scale > 0 ? scale : 1 this.citype = citype == "" ? "exact" : citype this.level = level == . ? c("level") : level this.showcode = showcode this.addquietly = addquietly } void binci::to_lm(struct nx rowvector nx) { real scalar rc, c, C real matrix values string scalar statacode, per class nhb_mt_labelmatrix row this.lm.clear() C = cols(nx) for(c=1;c<=C;c++) { statacode = sprintf("cii proportions %f %f, level(%f) %s", nx[c].n, nx[c].x, this.level, this.citype) rc = nhb_sae_logstatacode(statacode, this.showcode, this.addquietly) row = nhb_sae_stored_scalars() row = row.regex_select("^N|^p|^.b") values = row.values() values[2..4] = scale * values[2..4] row.values(values) row.column_equations(nx[c].name1) if (scale == 1 ) per = "" else if (scale == 100 ) per = "(%)" else per = sprintf(" (/%f)", scale) row.row_names(("N", sprintf("p%s", per), sprintf("[%f%% Conf.", level), "interval]")') row.column_names(nx[c].name2) row = row.transposed() this.lm.append(row) } } class nhb_mt_labelmatrix binci::table( |string scalar coleq, real rowvector decimals, real scalar show ) { decimals = decimals == J(1,0,.) ? (0,4) : decimals if ( coleq != "" ) this.lm.column_equations(coleq) if ( show != 0 ) this.lm.print("", decimals) else return(this.lm) } end /* test binci() mata: nx1 = nx_fill(96, 5, "rowname1", "roweq1") nx2 = nx_fill(196, 15, "rowname2", "roweq2") bci = binci() bci.set() bci.to_lm((nx1, nx2)) bci.table() bci.set(100) bci.to_lm((nx1, nx2)) bci.table() bci.set(100, 95, "wald", 1, 1) bci.to_lm((nx1, nx2)) bci.table("Wald CI", (0,2)) bci.set(100, 90, "wald", 1, 0) bci.to_lm((nx1, nx2)) bci.table("Wald CI", (0,2)) end exit */ mata: class bincmp { private: real scalar level, showcode, addquietly string scalar exact class nhb_mt_labelmatrix scalar lm public: void set(), to_lm() class nhb_mt_labelmatrix table() } void bincmp::set( |real scalar level, string scalar exact, real scalar showcode, real scalar addquietly ) { this.exact = exact != "exact" ? "" : exact this.level = level == . ? c("level") : level this.showcode = showcode this.addquietly = addquietly } void bincmp::to_lm( struct nx rowvector nx1, struct nx rowvector nx2, |string scalar cmptype // rr, rd, or "" ) { string scalar statacode real scalar rc, c, C string scalar test class nhb_mt_labelmatrix row C = cols(nx1) for(c=1;c<=C;c++) { statacode = sprintf("csi %f %f %f %f , %s level(%f)", nx1[c].x, nx2[c].x, nx1[c].n - nx1[c].x, nx2[c].n - nx2[c].x, this.exact, this.level) rc = nhb_sae_logstatacode(statacode, this.showcode, this.addquietly) test = exact == "" ? "P(Chisquare)" : "P(Fisher exact)" if ( cmptype == "rr" | cmptype == "rd" ) { row = nhb_sae_stored_scalars(cmptype + "|^p\$|^p_exact\$") row.values(row.values()[(3,2,1,4)]) row.column_names(sprintf("%s vs %s", nx1[c].name2, nx2[c].name2)) row.row_names((strupper(cmptype), sprintf("[%f%% Conf.", level), "interval]", test)') row = row.transposed() } else { row = nhb_sae_stored_scalars("^p\$|^p_exact\$") row.column_names(test) } this.lm.append(row) } } class nhb_mt_labelmatrix bincmp::table(|string scalar coleq, real scalar show) { if ( coleq != "" ) this.lm.column_equations(coleq) if ( show != 0 ) this.lm.print("", (2)) else return(this.lm) } end /* test bincmp() mata: nx1 = nx_fill(96, 5, "rowname1", "roweq1") nx2 = nx_fill(196, 15, "rowname2", "roweq2") bcm = bincmp() bcm.set(., "", 1, 0) bcm.to_lm(nx1, nx2) bcm.table() bcm = bincmp() bcm.set(., "", 1, 0) bcm.to_lm(nx1, nx2, "rr") bcm.table("test") bcm = bincmp() bcm.set(., "exact", 0, 1) bcm.to_lm(nx1, nx2, "rd") bcm.table("test") end exit */