multi.cont(verification)
multi.cont()所属R语言包：verification
Multiple Contingency Table Statistics
多列联表统计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Provides a variety of statistics for a data summarized in a contingency table. This will work for a 2 by 2
提供的各种统计数据的列联表的汇总数据。这将适用于2×2
用法----------Usage----------
multi.cont(DAT, baseline = NULL)
参数----------Arguments----------
参数：DAT
A contingency table in the form of a matrix. It is assumed that columns represent observation, rows represent forecasts.
阿列联表中以矩阵的形式。据推测，列代表观察，行表示预测。
参数：baseline
A vector indicating the baseline probabilities of each category. By default, it the baseline or naive forecasts is based on teh
一个向量，表示每个类别的基准的概率。默认情况下，它的基准或天真的预测是基于格兰
值----------Value----------
参数：pc
Percent correct - events along the diagonal.
正确 - 沿对角线的事件。
参数：bias
Bias
偏见
参数：ts
Threat score a.k.a. Critical success index (CSI)
威胁得分也称为临界成功指数（CSI）
参数：hss
Heidke Skill Score
Heidke技能分数
参数：pss
Peirce Skill Score
皮尔斯技能分数
参数：gs
Gerrity Score
Gerrity分数
参数：pc2
Percent correct by category (vector)
正确的类别百分比（矢量）
参数：h
Hit Rate by category (vector)
按类别划分的命中率（矢量）
参数：false.alarm.ratio
False alarm ratio by category (vector)
误报率按类别（矢量）
注意----------Note----------
Some verification statistics for a contingency table assume that the forecasts and observations are ordered, while others do not. An example of an ordered or ordinal forecast is "low, medium and high". An example of an unordered or nominal forecast is "snow, rain, hail, and none." If the forecasts are ordered, it is possible to account for forecasts which are close to the the observed value. For example, the Gerrity score takes this closeness into account. The Pierce Skill Score does not.
某些验证的列联表的统计假设的预测和意见是有序的，而另一些则没有。一个有序或有序预测的一个例子是“低，中，高”。无序的名义预测的一个例子是“雪，雨，冰雹，没有。”如果预测是有序的，它是可以考虑的观测值的预测。例如，Gerrity这分值亲近考虑。皮尔斯技巧得分没有。
For ordered forecast, it is assumed that the columns and rows of the input matrix are ordered sequentially.
对于排列的预测，它被假定输入矩阵的列和行的顺序进行排序。
When multiple values are returned, as in the case of pc2, h, f and false.alarm.ratio, these values are conditioned on that category having occurred. For example, in the example included in Jolliffe, given that a below average temperature was observed, the forecast had a bias of 2.3 and had a 0.47 chance of being detected.
返回多个值时，在箱子PC2，小时，f和false.alarm.ratio，这些值都取决于该类别上发生。预测乔利夫低于平均水平的温度下观察到，由于在实施例中包括，例如，有一个偏压为2.3，并有一个0.47被检测到的机会。
（作者）----------Author(s)----------
Matt Pocernich <pocernic@rap.ucar.edu>
参考文献----------References----------
score. Mon. Weather Rev., 120, 2707-2712.
practitioner's guide in atmospheric science. John Wiley and Sons. See chapter 4 concerning categorical events, written by R. E. Livezey.
参见----------See Also----------
binary.table
binary.table
实例----------Examples----------
DAT<- matrix(c(7,4,4,14,9,8,14,16,24), nrow = 3) # from p. 80 - Jolliffe[从p。 80 - 乔利夫]
multi.cont(DAT)
DAT<- matrix(c(3,8,7,8,13,14,4,18,25), ncol = 3) ## Jolliffe JJA[＃乔利夫JJA]
multi.cont(DAT)
DAT<- matrix(c(50,47,54,91,2364,205,71,170,3288), ncol = 3) # Wilks p. 245[威尔克斯。 245]
multi.cont(DAT)
DAT<- matrix(c(28, 23, 72, 2680 ), ncol = 2) ## Finley[＃芬利]
multi.cont(DAT)
## Finnish clouds[＃芬兰云]
DAT<- matrix(c(65, 10, 21, 29,17,48, 18, 10, 128), nrow = 3, ncol = 3, byrow = TRUE)
multi.cont(DAT)
### alternatively, the verify function and summary can be used.[＃＃可替代地，具有奇偶校验功能和摘要的都可以使用。]
mod <- verify(DAT, frcst.type = "cat", obs.type = "cat")
summary(mod)
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 15:22:24
