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/dports/net-mgmt/thanos/thanos-0.11.0/vendor/github.com/aliyun/aliyun-oss-go-sdk/sample/
H A Dsample_data.csv4 …mes,59,Arthritis among adults aged >=18 Years,%,AgeAdjPrv,Age-adjusted prevalence,22.5,22.3,22.7,,…
5 …alth Outcomes,59,Arthritis among adults aged >=18 Years,%,CrdPrv,Crude prevalence,24.7,24.5,24.9,,…
7 …ehaviors,59,Binge drinking among adults aged >=18 Years,%,CrdPrv,Crude prevalence,16.3,16.1,16.5,,…
15 …Outcomes,59,Current asthma among adults aged >=18 Years,%,CrdPrv,Crude prevalence,8.8,8.6,9.0,,,30…
36 …omes,59,Diagnosed diabetes among adults aged >=18 Years,%,CrdPrv,Crude prevalence,10.4,10.3,10.6,,…
44 …vention,59,Mammography use among women aged 50–74 Years,%,CrdPrv,Crude prevalence,75.8,75.4,76.2,,…
47 …viors,59,Obesity among adults aged >=18 Years,%,AgeAdjPrv,Age-adjusted prevalence,28.7,28.4,29.0,,…
48 …althy Behaviors,59,Obesity among adults aged >=18 Years,%,CrdPrv,Crude prevalence,28.8,28.6,29.1,,…
54 …tcomes,59,Stroke among adults aged >=18 Years,%,AgeAdjPrv,Age-adjusted prevalence,2.8,2.7,2.8,,,30…
55 …,Health Outcomes,59,Stroke among adults aged >=18 Years,%,CrdPrv,Crude prevalence,3.0,3.0,3.1,,,30…
[all …]
/dports/devel/R-cran-caret/caret/R/
H A DposPredValue.R11 function(data, reference, positive = levels(reference)[1], prevalence = NULL, ...) argument
20 if(is.null(prevalence)) prevalence <- mean(reference == positive)
23 (sens * prevalence)/((sens*prevalence) + ((1-spec)*(1-prevalence)))
30 function(data, positive = rownames(data)[1], prevalence = NULL, ...) argument
55 if(is.null(prevalence)) prevalence <- sum(data[, positive])/sum(data)
59 (sens * prevalence)/((sens*prevalence) + ((1-spec)*(1-prevalence)))
66 function(data, positive = rownames(data)[1], prevalence = NULL, ...) argument
69 posPredValue.table(data, prevalence = prevalence)
H A DnegPredValue.R11 function(data, reference, negative = levels(reference)[2], prevalence = NULL, ...) argument
20 if(is.null(prevalence)) prevalence <- mean(reference == lvls[lvls != negative])
23 (spec * (1-prevalence))/(((1-sens)*prevalence) + ((spec)*(1-prevalence)))
29 function(data, negative = rownames(data)[-1], prevalence = NULL, ...) argument
54 if(is.null(prevalence)) prevalence <- sum(data[, positive])/sum(data)
58 (spec * (1-prevalence))/(((1-sens)*prevalence) + ((spec)*(1-prevalence)))
65 function(data, negative = rownames(data)[-1], prevalence = NULL, ...) argument
68 negPredValue.table(data, prevalence = prevalence)
H A DconfusionMatrix.R184 …getFromNamespace("confusionMatrix.table", "caret")(classTable, positive, prevalence = prevalence, …
193 prevalence = NULL, argument
200 confusionMatrix(classTable, positive, prevalence = prevalence, mode = mode)
223 if(numLevels == 2 & !is.null(prevalence) && length(prevalence) != 1)
226 if(numLevels > 2 & !is.null(prevalence) && length(prevalence) != numLevels)
229 if(numLevels > 2 & !is.null(prevalence) && is.null(names(prevalence)))
261 if(is.null(prevalence)) prevalence <- sum(data[, positive])/sum(data)
265 posPredValue.table(data, positive, prevalence = prevalence),
266 negPredValue.table(data, negative, prevalence = prevalence),
270 prevalence,
[all …]
/dports/devel/R-cran-caret/caret/man/
H A DconfusionMatrix.Rd17 prevalence = NULL,
25 prevalence = NULL,
33 prevalence = NULL,
70 value, negative predictive value, precision, recall, F1, prevalence,
84 argument. Also, the prevalence of the "event" is computed from the data
86 events also predicted to be events) and the detection prevalence (the
87 prevalence of predicted events).
96 prevalence)/((sensitivity*prevalence) + ((1-specificity)*(1-prevalence)))}
97 \deqn{NPV = (specificity * (1-prevalence))/(((1-sensitivity)*prevalence) +
98 ((specificity)*(1-prevalence)))} \deqn{Detection Rate = A/(A+B+C+D)}
[all …]
H A Dsensitivity.Rd29 prevalence = NULL,
33 \method{negPredValue}{table}(data, negative = rownames(data)[-1], prevalence = NULL, ...)
35 \method{negPredValue}{matrix}(data, negative = rownames(data)[-1], prevalence = NULL, ...)
43 prevalence = NULL,
47 \method{posPredValue}{table}(data, positive = rownames(data)[1], prevalence = NULL, ...)
49 \method{posPredValue}{matrix}(data, positive = rownames(data)[1], prevalence = NULL, ...)
77 \item{prevalence}{a numeric value for the rate of the "positive" class of
140 posPredValue(pred, truth, prevalence = 0.25)
145 negPredValue(pred, truth, prevalence = 0.25)
152 ppvVals[i] <- posPredValue(pred, truth, prevalence = prev[i])
[all …]
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/
H A D_base.py180 prevalence = np.empty(n_pairs) if is_weighted else None
190 prevalence[ix] = np.average(ab_mask)
199 return np.average(pair_scores, weights=prevalence)
/dports/www/chromium-legacy/chromium-88.0.4324.182/docs/process/
H A Drelease_blockers.md11 **prevalence**.
46 matrix based on the issue's severity and prevalence:
84 Note that prevalence should be evaluated based on the population of users they
92 In practice, the data available for assessing severity and prevalence of bugs is
97 bug which might have much wider severity and prevalence. The evaluation isn't
111 recent regressions should have an upward bias in severity/prevalence assessment,
121 for severity and feature usage for prevalence.
135 Including your rationale around impact and prevalence will make it much
174 prevalence and uncertainty than longstanding bugs.
/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/docs/process/
H A Drelease_blockers.md11 **prevalence**.
46 matrix based on the issue's severity and prevalence:
84 Note that prevalence should be evaluated based on the population of users they
92 In practice, the data available for assessing severity and prevalence of bugs is
97 bug which might have much wider severity and prevalence. The evaluation isn't
111 recent regressions should have an upward bias in severity/prevalence assessment,
121 for severity and feature usage for prevalence.
135 Including your rationale around impact and prevalence will make it much
174 prevalence and uncertainty than longstanding bugs.
/dports/biology/gcta/gcta_1.26.0_src/
H A Dest_hsq.cpp422 …reml(pred_rand_eff, est_fix_eff, reml_priors, reml_priors_var, prevalence, -2.0, no_constrain, no_… in fit_reml()
701 if ((_flag_CC && prevalence>-1) || (_flag_CC2 && prevalence2>-1)) { in reml()
707 …< "_L\t" << transform_hsq_L(_ncase, prevalence, Hsq[_bivar_pos[0][i]]) << "\t" << transform_hsq_L(… in reml()
711 … the sample = " << _ncase << "; User-specified disease prevalence = " << prevalence << ")" << endl; in reml()
712 …ame[i] << "_L\t" << transform_hsq_L(_ncase, prevalence, Hsq[i]) << "\t" << transform_hsq_L(_ncase,… in reml()
713 …of V(G)_L/Vp\t" << transform_hsq_L(_ncase, prevalence, sum_hsq) << "\t" << transform_hsq_L(_ncase,… in reml()
760 if (_flag_CC && prevalence>-1) { in reml()
765 …< "_L\t" << transform_hsq_L(_ncase, prevalence, Hsq[_bivar_pos[0][i]]) << "\t" << transform_hsq_L(… in reml()
769 … the sample = " << _ncase << "; User-specified disease prevalence = " << prevalence << ")" << endl; in reml()
770 …ame[i] << "_L\t" << transform_hsq_L(_ncase, prevalence, Hsq[i]) << "\t" << transform_hsq_L(_ncase,… in reml()
[all …]
H A Doption.cpp98 double prevalence = -2.0, prevalence2 = -2.0; in option() local
553 prevalence = atof(argv[++i]); in option()
554 cout << "--prevalence " << prevalence << endl; in option()
555 …if (prevalence <= 0 || prevalence >= 1) throw ("\nError: --prevalence should be between 0 to 1.\n"… in option()
707 prevalence = K_buf[0]; in option()
712 prevalence = prevalence2 = K_buf[0]; in option()
1118 …f, reml_mtd, MaxIter, reml_priors, reml_priors_var, reml_drop, no_lrt, prevalence, prevalence2, no… in option()
1120 …f, reml_mtd, MaxIter, reml_priors, reml_priors_var, reml_drop, no_lrt, prevalence, no_constrain, m… in option()
H A Dbivar_reml.cpp15 … vector<double> reml_priors_var, vector<int> drop, bool no_lrt, double prevalence, double prevalen… in fit_bivar_reml() argument
134 else prevalence = -1.0; in fit_bivar_reml()
139 …if ((_flag_CC && prevalence<-1) || (_flag_CC2 && prevalence2<-1)) cout << "Note: we can specify th… in fit_bivar_reml()
288 …reml(pred_rand_eff, est_fix_eff, reml_priors, reml_priors_var, prevalence, prevalence2, no_constra… in fit_bivar_reml()
/dports/devel/R-cran-caret/caret/tests/testthat/
H A Dtest_resamples.R44 prevalence <- seq(.1, .9, length = 26) functionVar
45 dat <- sample(letters, size = n, replace = TRUE, prob = sample(prevalence))
/dports/math/jags/classic-bugs/vol2/pigs/
H A Dpigs.bug6 q ~ dunif(0,1); # prevalence of a1
7 p <- 1 - q; # prevalence of a2
/dports/science/R-cran-epicalc/epicalc/man/
H A DtableStack.rd10 prevalence = FALSE, percent = c("column", "row", "none"), frequency=TRUE,
33 …\item{prevalence}{for logical variable, whether prevalence of the dichotomous row variable in each…
50 … "none" (FALSE). For a dichotomous row variable, if set to 'TRUE', the prevalence of row variable …
85 tableStack(bakedham:fruitsalad, by= ill, prevalence=TRUE)
122 tableStack(vars=3:4, by=outc, prevalence = TRUE)
H A Dsampsize.rd24 …\item{delta}{difference between the estimated prevalence and one side of the 95 percent confidence…
97 # an estimated prevalence of 70 percent, design effect is assumed to be 2.
101 # To see the effect of prevalence on delta and sample size
128 # volunteers would result in reduction of prevalence of a disease from 50 percent
H A DHW93.Rd4 \title{Dataset from a study on hookworm prevalence and intensity in 1993}
/dports/math/R-cran-lava/lava/man/
H A Dzibreg.Rd21 \item{formula.p}{Formula for model of disease prevalence}
64 prev <- summary(e,pr.contrast=B)$prevalence
/dports/science/R-cran-Epi/Epi/man/
H A Dpr.Rd7 Diabetes prevalence as of 2010-01-01 in Denmark in 1-year age classes by sex.
/dports/lang/v8/v8-9.6.180.12/tools/clang/scripts/
H A Danalyze_includes.py354 prevalence = {name: 0 for name in includes}
357 prevalence[n] += 1
420 'prevalence': [prevalence[n] for n in names],
/dports/math/R-cran-spData/spData/man/
H A Dhuddersfield.Rd15 Martuzzi M, Elliott P (1996) Empirical Bayes estimation of small area prevalence of non-rare condit…
/dports/devel/libbson/libbson-1.8.1/build/autotools/m4/
H A Dac_compile_check_sizeof.m410 [for ac_size in 4 8 1 2 16 $2 ; do # List sizes in rough order of prevalence.
/dports/devel/mongo-c-driver/mongo-c-driver-1.8.1/build/autotools/m4/
H A Dac_compile_check_sizeof.m410 [for ac_size in 4 8 1 2 16 $2 ; do # List sizes in rough order of prevalence.
/dports/devel/libmatheval/libmatheval-1.1.11/
H A Dacinclude.m413 [for ac_size in 4 8 1 2 16 $2 ; do # List sizes in rough order of prevalence.
/dports/graphics/R-cran-pROC/pROC/man/
H A Dcoords.Rd139 …\item the prevalence, or the proportion of cases in the population (\eqn{\frac{n_{cases}}{n_{contr…
154 \deqn{r = \frac{1 - prevalence}{cost * prevalence}}{r = (1 - prevalence) / (cost * prevalence)}
156 By default, prevalence is 0.5 and cost is 1 so that no weight is

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