accuracyMeasures(WGCNA)
accuracyMeasures()所属R语言包：WGCNA
Accuracy measures for a 2x2 confusion matrix.
一个2x2的混淆矩阵精度的措施。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function calculates the error rate, accuracy, sensitivity, specificity, positive predictive value, and other accuracy measures for a 2x2 confusion matrix. The input tab must be a 2x2 dimensional matrix which contains count data.
该函数计算的错误率，准确性，敏感性，特异性，阳性预测值和一个2x2的混淆矩阵精度的措施。输入tab必须是一个2×2的二维矩阵，其中包含计数数据。
用法----------Usage----------
accuracyMeasures(tab)
参数----------Arguments----------
参数：tab
should be a 2x2 dimensional matrix (i.e. table) whose entries contain non-negative integers. The function also works if the table contains non-negative real numbers but outputs a warning if non-integers are detected.
应该是一个2×2的二维矩阵（即表），其包含非负整数的条目。也适用，如果表中包含非负实数，而输出一个警告，如果检测到非整数的函数。
Details
详细信息----------Details----------
The rows of the 2x2 table tab may correspond to a test (or predicted) outcome and the columns to a true outcome ("gold standard"). A table that relates a predicted outcome to a true test outcome is also known as confusion matrix. Warning: When it comes to estimating the sensitivity and specificity, the order of rows and columns matters. The first row corresponds to a *positive* outcome while the second row corresponds to a negative outcome. Same holds for the columns. Reversing the order of the rows (and accordingly the order of the columns) swaps the estimates of the sensitivity and specificity but has no effect on the error rate or accuracy. Specifically, the funcdtion assumes: number of true positives=TP=tab[1,1], no.false positives =FP=tab[1,2], no.false negatives=FN=tab[2,1], no.true negatives=TN=tab[2,2]. Then Specificity= TN/(FP+TN) Sensitivity= TP/(TP+FN) NegativePredictiveValue= TN/(FN + TN) PositivePredictiveValue= TP/(TP + FP) FalsePositiveRate = 1-Specificity FalseNegativeRate = 1-Sensitivity Power = Sensitivity LikelihoodRatioPositive = Sensitivity / (1-Specificity) LikelihoodRatioNegative = (1-Sensitivity)/Specificity. The naive error rate is the error rate of a constant (naive) predictor that assigns the same outcome to all samples. The prediction of the naive predictor equals the most frequenly observed outcome. Example: Assume you want to predict disease status and 70 percent of the observed samples have the disease. Then the naive predictor has an error rate of 30 percent (since it only misclassifies 30 percent of the healthy individuals).
“行的2x2表”选项卡中，可以对应一个测试（或预测）结果和列一个真实的结果（“金标准”）。甲的预测的结果的表，该表涉及到一个真正的测试结果也被称为混淆矩阵。警告：当它涉及到估计的敏感性和特异性，行和列的顺序事宜。的第一行对应于一个*阳性*结果，而第二行对应于一个负的结果。同样适用于列。反转的行的顺序（及相应的列的顺序）交换估计的敏感性和特异性，但具有对错误率没有影响或准确性。具体来说，funcdtion假设：真阳性数= TP =“选项卡[1,1]，no.false阳性= FP =”选项卡[1,2]，no.false底片= FN =选项卡上[2,1]。 = TN =真阴性标签[2,2]。特异性= TN /（FP + TN）灵敏度= TP /（TP + FN）NegativePredictiveValue = TN /（FN + TN）PositivePredictiveValue = TP /（TP + FP）FalsePositiveRate = 1特异性FalseNegativeRate的= 1灵敏度功率=灵敏度LikelihoodRatioPositive =灵敏度/（1 - 特异性）LikelihoodRatioNegative =（1 - 灵敏度）/特异性。天真的错误率是一个常数（幼稚）预测值，分配相同的结果所有样品的错误率。天真的预测的预测等于频率的观察结果。举例：假设有疾病，要预测疾病状态和所观察到的样本的70％。然后天真预测有30％的错误率（因为它仅误的健康个体的30％）。
值----------Value----------
Data frame with two columns:
有两列数据框：
参数：Measure
this column contais character strings that specify name of the accuracy measure.
这的列contais字符字符串，指定名称的精确度。
参数：Value
this column contains the numeric estimates of the corresponding accuracy measures.
此列包含的数字估计的相应精度的措施。
（作者）----------Author(s)----------
Steve Horvath
参考文献----------References----------
实例----------Examples----------
m=100
trueOutcome=sample( c(1,2),m,replace=TRUE)
predictedOutcome=trueOutcome
# now we noise half of the entries of the predicted outcome[现在，我们的噪音一半的条目预测的结果]
predictedOutcome[ 1 m/2)] =sample(predictedOutcome[ 1m/2)] )
tab=table(predictedOutcome, trueOutcome)
accuracyMeasures(tab)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 21:05:29
