/dports/net-mgmt/thanos/thanos-0.11.0/vendor/github.com/aliyun/aliyun-oss-go-sdk/sample/ |
H A D | sample_data.csv | 4 …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 …]
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/dports/devel/R-cran-caret/caret/R/ |
H A D | posPredValue.R | 11 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)
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H A D | negPredValue.R | 11 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)
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H A D | confusionMatrix.R | 184 …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 …]
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/dports/devel/R-cran-caret/caret/man/ |
H A D | confusionMatrix.Rd | 17 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 …]
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H A D | sensitivity.Rd | 29 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 …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/ |
H A D | _base.py | 180 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)
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/dports/www/chromium-legacy/chromium-88.0.4324.182/docs/process/ |
H A D | release_blockers.md | 11 **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.
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/dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/docs/process/ |
H A D | release_blockers.md | 11 **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.
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/dports/biology/gcta/gcta_1.26.0_src/ |
H A D | est_hsq.cpp | 422 …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 …]
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H A D | option.cpp | 98 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()
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H A D | bivar_reml.cpp | 15 … 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()
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/dports/devel/R-cran-caret/caret/tests/testthat/ |
H A D | test_resamples.R | 44 prevalence <- seq(.1, .9, length = 26) functionVar 45 dat <- sample(letters, size = n, replace = TRUE, prob = sample(prevalence))
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/dports/math/jags/classic-bugs/vol2/pigs/ |
H A D | pigs.bug | 6 q ~ dunif(0,1); # prevalence of a1 7 p <- 1 - q; # prevalence of a2
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/dports/science/R-cran-epicalc/epicalc/man/ |
H A D | tableStack.rd | 10 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)
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H A D | sampsize.rd | 24 …\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
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H A D | HW93.Rd | 4 \title{Dataset from a study on hookworm prevalence and intensity in 1993}
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/dports/math/R-cran-lava/lava/man/ |
H A D | zibreg.Rd | 21 \item{formula.p}{Formula for model of disease prevalence} 64 prev <- summary(e,pr.contrast=B)$prevalence
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/dports/science/R-cran-Epi/Epi/man/ |
H A D | pr.Rd | 7 Diabetes prevalence as of 2010-01-01 in Denmark in 1-year age classes by sex.
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/dports/lang/v8/v8-9.6.180.12/tools/clang/scripts/ |
H A D | analyze_includes.py | 354 prevalence = {name: 0 for name in includes} 357 prevalence[n] += 1 420 'prevalence': [prevalence[n] for n in names],
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/dports/math/R-cran-spData/spData/man/ |
H A D | huddersfield.Rd | 15 Martuzzi M, Elliott P (1996) Empirical Bayes estimation of small area prevalence of non-rare condit…
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/dports/devel/libbson/libbson-1.8.1/build/autotools/m4/ |
H A D | ac_compile_check_sizeof.m4 | 10 [for ac_size in 4 8 1 2 16 $2 ; do # List sizes in rough order of prevalence.
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/dports/devel/mongo-c-driver/mongo-c-driver-1.8.1/build/autotools/m4/ |
H A D | ac_compile_check_sizeof.m4 | 10 [for ac_size in 4 8 1 2 16 $2 ; do # List sizes in rough order of prevalence.
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/dports/devel/libmatheval/libmatheval-1.1.11/ |
H A D | acinclude.m4 | 13 [for ac_size in 4 8 1 2 16 $2 ; do # List sizes in rough order of prevalence.
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/dports/graphics/R-cran-pROC/pROC/man/ |
H A D | coords.Rd | 139 …\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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