hoods2d(SpatialVx)
hoods2d()所属R语言包：SpatialVx
Calculate various neighborhood verification statistics for a gridded verification set.
各种邻里检验统计计算的网格验证集。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculates most of the various neighborhood verification statistics for a gridded verification set as reviewed in Ebert (2008).
计算的网格验证设置为审查艾伯特（2008）附近的各种检验统计。
用法----------Usage----------
hoods2d(obj, which.methods = c("mincvr", "multi.event", "fuzzy", "joint", "fss", "pragmatic"), verbose = FALSE)
## S3 method for class 'hoods2d'
plot(x, ...)
参数----------Arguments----------
参数：obj
list object of class "hoods2dPrep" as returned by the hoods2dPrep function.
列表对象类“hoods2dPrep”返回的hoods2dPrep功能的。
参数：which.methods
character vector giving the names of the methods. Default is for the entire list to be executed. See Details section for specific option information.
提供的名称字符向量的方法。默认值是要执行的整个列表。请参阅特定的选项信息的详细信息部分。
参数：verbose
logical, should progress information be printed to the screen? Will also give the amount of time (in hours, minutes, or seconds) that the function took to run.
逻辑的发展，应以信息打印到屏幕上？也会给所需的时间（以小时，分钟或秒）的功能了。
参数：x
list object output from hoods2d.
列表对象输出从hoods2d。
参数：...
not used.
不被使用。
Details
详细信息----------Details----------
hoods2d uses an object from the function 'hoods2dPrep' that includes most of the options utilized by this function, including the thresholds and neighborhood lengths (levels) to be used. The neighborhood methods (cf. Ebert 2008, 2009; Gilleland et al., 2009, 2010) apply a (kernel) smoothing filter (cf. Hastie and Tibshirani, 1990) to either the raw forecast (and possibly also the observed) field(s) or to the binary counterpart(s) determined by thresholding.
hoods2d使用对象从功能的hoods2dPrep的功能，包括要使用的阈值和邻域的长度（水平）所使用的选项，其中包括最。附近方法（参见艾伯特2008年，2009年Gilleland等。，2009年，2010年）应用（内核）平滑滤波器（参见Hastie和Tibshirani，1990）的原始预测（也可能观察到的）领域（ s）或对应的二进制（）确定的阈值。
The specific smoothing filter applied for these methods could be of any type, but those described in Ebert (2008) are generally taken to be "neighborhood" filters. In some circles, this is referred to as a convolution filter with a boxcar kernel. Because the smoothing filter can be represented this way, it is possible to use the convolution theorem with the Fast Fourier Transform (FFT) to perform the neighborhood smoothing operation very quickly. The particular approach used here "zero pads" the field, and replaces all missing values with zero as well, which is also the approach proposed in Roberts and Lean (2008). If any missing values are introduced after the convolution, they are removed.
的具体的平滑滤波器适用于这些方法，可以是任何类型的，但，埃伯特（2008年）中描述的那些，一般取为“附近”过滤器。在某些领域，这被称为具有棚车内核的卷积滤波器。由于可以表示这样的平滑化滤波器，它是能够使用与快速傅立叶变换（FFT）来执行的卷积定理周边的平滑操作非常迅速。特别的方法使用“零填充”领域，并取代所有缺失值与零，这也是罗伯茨和精益生产（2008年）中提出的方法。如果有任何遗漏值的卷积后，他们将被删除。
If zero-padding is undesirable, then two options are available: 1. Give a subset to the 'hoods2dPrep' function (e.g., some tile within the domain) so that the final statistics are calculated only on this subset, or 2. Extrapolate the fields before applying this function (and 'hoods2dPrep'). In the case of 2, you might want to also give it the subset (e.g., to give it only the original un-extrapolated fields).
如果填零是不可取的，则有两个选项可供选择：1。给的“hoods2dPrep”功能（例如，有些瓷砖在域）的一个子集，所以，最终的统计数据计算只有在这个子集，或2。推断的字段，然后再应用此功能（和hoods2dPrep“）。 2的情况下，你可能想给它的子集（例如，给它只有原来的联合国外推的字段）。
To simplify the notation for the descriptions of the specific methods employed here, the notation of Ebert (2008) is adopted. That is, if a method uses neighborhood smoothed observations (NO), then the neighborhood smoothed observed field is denoted <X>s, and the associated binary field, by <Ix>s. Otherwise, if the observation field is not smoothed (denoted by SO in Ebert, 2008), then simply X or Ix are used. Similarly, for the forecast field, <Y>s or <Iy>s are used for neighborhood smoothed forecast fields (NF). If it is the binary fields that are smoothed (e.g., the original fields are thresholded before smoothing), then the resulting fields are denoted <Px>s and <Py>s, resp. Below, NO-NF indicates that a neighborhood smoothed observed field (<Yx>s, <Ix>s, or <Px>s) is compared with a neighborhood smoothed forecast field, and SO-NF indicates that the observed field is not smoothed.
为了简化这里采用的具体方法的描述的符号时，埃伯特（2008）采用的符号。也就是说，如果一个方法使用附近平滑的观测值（“否”），然后观察字段是表示为<X> s分数平滑，并在相应的二进制字段，由<Ix> s。否则，如果观察字段是不进行平滑处理（表示由SO埃伯特，2008），然后简单地X或Ix的使用。同样，预测字段，<Y> s或<Iy> s用于附近平滑预测字段（NF）。如果它是二进制域上进行平滑处理（例如，平滑前的阈值的原始字段），然后将得到的字段表示<Px> s和<Py>，分别的。以下，NO-NF表示的一个邻域平滑邻里平滑预测字段观察字段（<Yx>，<Ix> s，或<Px>的s）进行比较，以及SO-NF表明，所观察到的字段不平滑。
Options for 'which.methods' include:
which.methods“的选项包括：
"mincvr": (NO-NF) The minimum coverage method compares <Ix>s and <Iy>s by thresholding the neighborhood smoothed fields <Px>s and <Py>s (i.e., smoothed versions of Ix and Iy) to obtain <Ix>s and <Iy>s. Indicator fields <Ix>s and <Iy>s are created by thresholding <Px>s and <Py>s by frequency threshold Pe given by the obj argument. Scores calculated between <Ix>s and <Iy>s include: probability of detecting an event (pod, also known as the hit rate), false alarm ratio (far) and ets (cf. Ebert, 2008, 2009).
“mincvr”（NO-NF）的最小覆盖的方法比较<Ix> s和<Iy>小号的阈值附近的平滑领域<Px> s和<Py> s（即，平滑版本的九和Iy），得到：<Ix> s和<Iy> s的。指示灯的领域<Ix> s和<Iy>小号是由，阈值<Px> s和<Py>小号的频率阈值Pe给obj参数的创建。成绩之间计算<Ix> s和<Iy>小号包括：检测事件（POD，也被称为命中率），误报率（FAR）和ETS（参见艾伯特，2008年，2009年）的概率。
"multi.event": (SO-NF) The Multi-event Contingency Table method compares the binary observed field Ix against the smoothed forecast indicator field, <Iy>s, which is determined similarly as for "mincvr" (i.e., using Pe as a threshold on <Py>s). The hit rate and false alarm rate (F) are calculated (cf. Atger, 2001).
“multi.event”：（SO-NF）多事件的应急表方法比较观察到的二进制九场对平滑的预测指标领域，<Iy> S，这是同样的“mincvr”（即确定，使用Pe的<Py> s作为一个阈值）。的命中率和误报率（F）（参见Atger，2001年）。
"fuzzy": (NO-NF) The fuzzy logic approach compares <Px>s to <Py>s by creating a new contingency table where hits = sum_i min(<Px>s_i,<Py>s_i), misses = sum_i min(<Px>s_i,1-<Py>s_i), false alarms = sum_i min(1-<Px>s_i,<Py>s_i), and correct negatives = sum_i min(1-<Px>s_i,1-<Py>s_i) (cf. Ebert 2008).
“模糊”（NO-NF）的模糊逻辑方法比较<Px>的s到<Py>小号的创建一个新的列联表，命中= sum_i分（<Px> S_I，<Py> S_I） ，错过= sum_i分（1 <Px> S_I， - <Py> S_I），虚假报警= sum_i分钟（1 - <Px> S_I S_I <Py>），和正确的底片=分钟sum_i（1 - <Px> S_I， - <Py> S_I）（参见艾伯特2008年）。
"joint": (NO-NF) Similar to "fuzzy" above, but hits = sum_i prod(<Px>s_i,<Py>s_i), misses = sum_i prod(<Px>s_i,1-<Py>s_i), false alarms = sum_i prod(1-<Px>s_i,<Py>s_i), and correct negatives = sum_i prod(1-<Px>s_i,1-<Py>s_i) (cf. Ebert, 2008).
“联合”（NO-NF）与“模糊”，但命中= sum_i产品（<Px> S_I S_I <Py>）的，错过= sum_i“产品（<Px> S_I 1的 - 假警报<Py> S_I），= sum_i产品（1 - <Px> S_I，<Py> S_I），和正确的底片sum_i产品（1 - <Px> S_I， - <Py> S_I ）（参见艾伯特，2008年）。
"fss": (NO-NF) Compares <Px>s and <Py>s directly using a Fractions Brier and Fractions Skill Score (FBS and FSS, resp.), where FBS is the mean square difference between <Px>s and <Py>s, and the FSS is one minus the FBS divided by a reference MSE given by the sum of the sum of squares of <Px>s and <Py>s individually, divided by the total (cf. Roberts and Lean, 2008).
“FSS”（NO-NF）比较<Px>小号和<Py>直接用分数蒺藜和分数技能分数（FBS和FSS分别），其中FBS之间的均方差< Px的> s和<Py> s，和FSS是个别<Px> s和<Py> s的平方的总和的总和除以总给出一个参考的MSE除以减去FBS （参见罗伯茨和精益生产，2008年）。
"pragmatic": (SO-NF) Compares Ix with <Py>s, calculating the Brier and Brier Skill Score (BS and BSS, resp.), where the reference forecast used for the BSS is taken to be the mean square error between the base rate and Ix (cf. Theis et al., 2005).
“务实”：（SO-NF）比较九小号与<Py>，计算石南木和石南木的技能分数（BS分别BSS），参考预测的BSS采取的均方之间的误差基准利率和Ix（参见泰斯等人，2005）。
值----------Value----------
A list object of class "hoods2d" with components determined by the which.methods argument. Each component is itself a list object containing relevant components to the given method. For example, hit rate is abbreviated pod here, and if this is an output for a method, then there will be a component named pod (all lower case). The Gilbert Skill Score is abbreviated 'ets' (equitable threat score; again all lower case here). The list components will be some or all of the following.
一个List对象类“hoods2d”，由which.methods参数。每个组件本身也是一个列表对象，其中包含相关组件的方法。例如，简称POD的命中率是在这里，如果这是一个输出的方法，那么就会有一个被命名的组件POD（全部小写）。吉尔伯特技能分数是缩写为“外星人”（公平的威胁的得分;再次全部小写）。列表组件将一些或所有以下。
参数：mincvr
list with components: pod, far and ets
吊舱，远ETS的组件列表：
参数：multi.event
list with components: pod, f and hk
与组件列表：POD，f和香港的
参数：fuzzy
list with components: pod, far and ets
吊舱，远ETS的组件列表：
参数：joint
list with components: pod, far and ets
吊舱，远ETS的组件列表：
参数：fss
list with components: fss, fss.uniform, fss.random
组件列表：FSS，fss.uniform，fss.random
参数：pragmatic
list with components: bs and bss
BS和BSS的组件列表：
注意----------Note----------
Thresholded fields are taken to be >= the threshold.
阈值的字段都采取的是> =阈值。
（作者）----------Author(s)----------
Eric Gilleland
参考文献----------References----------
参见----------See Also----------
fft, kernel2dsmooth, plot.hoods2d, vxstats
fft，kernel2dsmooth，plot.hoods2d，vxstats
实例----------Examples----------
x <- y <- matrix( 0, 50, 50)
x[ sample(1:50,10), sample(1:50,10)] <- rexp( 100, 0.25)
y[ sample(1:50,20), sample(1:50,20)] <- rexp( 400)
hold <- hoods2dPrep("y", "x", thresholds=c(0.1, 0.5), levels=c(1, 3, 20))
look <- hoods2d( hold, which.methods=c("multi.event", "fss"))
look
## Not run: [＃不运行：]
plot(look)
data(geom001)
data(geom000)
data(ICPg240Locs)
hold <- hoods2dPrep( "geom001", "geom000", thresholds=c(0.01,50.01), levels=c(1, 3, 9, 17, 33, 65, 129, 257), loc=ICPg240Locs, units="in/100")
look <- hoods2d(hold, verbose=TRUE)
plot( look) # Might want to use 'pdf' to print these out so that all of them can be observed.[可能要使用“PDF”来打印这些出来，让所有的人都可以观察到。]
data(pert004)
data(pert000)
hold <- hoods2dPrep( "pert004", "pert000", thresholds=c(1,10,50), levels=c(1, 3, 17, 33, 65, 129, 257), loc=ICPg240Locs, units="mm/h")
look <- hoods2d( hold, verbose=TRUE)
plot( look)
## End(Not run)[＃（不执行）]
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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。
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发表于 2012-9-30 12:56:17
