SpatialVx-package(SpatialVx)
SpatialVx-package()所属R语言包：SpatialVx
Spatial Forecast Verification
空间预测验证
译者：生物统计家园网 机器人LoveR
描述----------Description----------
SpatialVx contains functions to perform several spatial forecast verification methods. this release has functionality to do most of the traditional and neighborhood smoothing methods, as well as FQI and several binary image measures/metrics. Some feature-based methods functions are also included.
SpatialVx包含函数来执行一些空间预测的验证方法。此版本的功能做最重要的传统和附近的平滑方法，以及FQI和几个二进制图像措施/指标的。一些基于特征的方法的功能也包括在内。
Details
详细信息----------Details----------
Primary functions include:
主要职能包括：
1. Filter Methods:
1。筛选方法：
1a. Neighbordoohd Methods:
1A。 Neighbordoohd方法：
Neighborhood methods generally apply a convolution kernel smoother to one or both of the fields in the verificaiton set, and then apply the traditional scores. Most of the methods reviewe in Ebert (2008, 2009) are included in this package. The main functions are:
邻居的方法一般适用于一个卷积核平滑，以一个或两个领域在verificaiton集，然后采用传统的分数。 reviewe在艾伯特的方法（2008年，2009年）都包含在这个套件。其主要功能是：
hoods2dPrep, hoods2d, pphindcast2d, kernel2dsmooth, and plot.hoods2d.
hoods2dPrep，hoods2d，pphindcast2d，kernel2dsmooth和plot.hoods2d。
1b. Scale Separation Methods:
1B。标分离的方法：
Scale separation refers to the idea of applying a band-pass filter (and/or doing a multi-resolution analysis, MRA) to the verification set. Typically, skill is assessed on a scale-by-scale basis. However, other techniques are also applied. For example, denoising the field before applying traditional statistics, using the variogram, or applying a statistical test based on the variogram (these last are less similar to the spirit of the scale separation idea, but are at least somewhat related).
标分离是指应用的带通滤波器（和/或执行的多分辨率分析，MRA），核实集的想法。通常情况下，技能的规模，通过规模的基础上进行评估。然而，其他技术也有应用。例如，应用传统的统计去噪前场，利用变差函数，应用统计检验的基础上的变异函数（这些最后都是差不多的精神规模分离的想法，但至少有一定的关系）。
There is functionality to do the wavelet methods proposed in Briggs and Levine (1997). In particular, to simply denoise the fields before applying traditional verification statistics, use wavePurifyVx. To apply verification statistics to detail fields (in either the wavelet or field space), use waverify2d (dyadic fields) or mowaverify2d (non-dyadic or dyadic) fields.
有功能做布里格斯和Levine（1997）中提出的小波方法。尤其是，简单地去噪等领域，在提出申请前传统的验证统计，使用wavePurifyVx。为了检验统计细节场（小波或场空间），使用waverify2d（矢场）或mowaverify2d（非二进或二进）领域。
The intensity-scale technique introduced in Casati et al. (2004) and the new developments of the technique proposed in Casati (2010) can be performed with waveIS.
引入在卡萨蒂等中的强度尺度技术。 （2004年），在CASATI（2010）提出的技术的新发展，，可以进行waveIS。
Although not strictly a "scale separation" method, the structure function (for which the variogram is a special case) is in the same spirit in the sense that it analyzes the field for different separation distances, and these "scales" are separate from each other (i.e., the score does not necessarily improve or decline as the scale increases). This package contains essentially wrapper functions to the vgram.matrix and plot.vgram.matrix functions from teh fields package. It also has a function (variogram.matrix) that is a modification of vgram.matrix that allows for missing values. The primary function for this is called griddedVgram, and it has a plot method function.
虽然没有严格的“尺度分离”的方法，结构功能（变差函数是一个特殊的情况下）以同样的精神在这个意义上，分析了不同的分离距离的领域，这些“鳞片”是分开的从对方（即，比分并不一定能改善随着规模的增加或下降）。这个软件包包含基本包装函数来vgram.matrix和plot.vgram.matrix从格兰领域的功能包。它也有一个功能（variogram.matrix）是一个变形vgram.matrix缺失值，允许。的主要功能，这被称为griddedVgram，它有一个plot方法的功能。
2. Displacement Methods:
2。位移的方法：
In Gilleland et al (2009), this category was broken into two main types as field deformation and features-based. The former lumped together binary image measures/metrics with field deformation techniques because the binary image measures inform about the "similarity" (or dissimilarity) between the spatial extent of two fields (across the entire field). Here, they are broken down further into those that yield a only a single (or small vector of) metric(s) or measure(s) (location measures), and those that have mechanisms for moving grid-point locations to match the fields better spatially (field deformation).
在Gilleland等人（2009年），这一类被打破场变形和功能分为两个主要类型。前者混为一谈的二进制影像措施/指标与现场变形技术，因为二值图像的措施通知有关的“相似性”（或不同）的两个字段（在整个视场）之间的空间范围。在这里，他们都分解到那些产生一个只有单一的（或小）指标（S）或措施（S）（位置测量）向量，和那些有移动的格点位置的机制相匹配的领域进一步更好的空间（场变形）。
2a. Location Measures:
2A。地点措施：
Included here are the well-known Hausdorff metric, the partial-Hausdorff measure, FQI (Venugopal et al., 2005), Baddeley's delta metric (Gilleland, 2011; Schwedler et al., 2011), metrV (Zhu et al., 2011), as well as the localization performance measures described in Baddeley, 1992: mean error distance, mean square error distance, and Pratt's Figure of Merit (FOM).
包括这里是著名的豪斯多夫度量，局部的豪斯多夫的措施，FQI（Venugopal等人。，2005），灸手可热的Delta公吨（2011年Gilleland，施威德勒等人。，2011年），metrV（Zhu等人，2011 ），以及定位性能测量中描述的巴德利，1992年平均误差距离，均方误差距离，和普拉特的品质因数（FOM）。
locmeasures2dPrep, locmeasures2d, metrV, distob, locperf
locmeasures2dPrep，locmeasures2d，metrV，distob，locperf
2b. Field deformation:
2B。现场变形：
coming soon. An image warping pacakge is in the works, and will be released as a separate package, but included as a dependency by this package potentially with some wrapper functions specific to spatial forecast verification needs. Other field deformation aproaches are also in the hopper, and will be made available as soon as they are ready.
即将到来。一个图像变形，线路咨询的作品，是作为一个单独的包将被释放，但作为一个依赖这个包可能有一些包装空间预测的验证需求的特定功能。其他的现场变形aproaches也料斗，将可尽快为他们准备好。
2c. Features-based methods: These methods are also sometimes called object-based methods, and have many similarities to techniques used in Object-Based Image Analysis (OBIA), a relatively new research area that has emerged primarily as a result of advances in earth observations sensors and GIScience (Blaschke et al., 2008). It is attempted to identify individual features within a field, and subsequently analyze the fields on a feature-by-feature basis. This may involve intensity error information in addition to location-specific error information. Additionally, contingency table verifcation statistics can be found using new definitions for hits, misses and false alarms (correct negatives are more difficult to asses, but can also be done).
2C。特点为基础的方法：这些方法有时也被称为基于对象的方法，在基于对象的图像分析（OBIA）的一个相对较新的研究领域已经出现，主要是因为使用了先进的地球观测所用的技术有很多相似之处传感器和GEO信息科学（布拉施克等，2008）。它试图找出一个领域内的个人特征，并随后分析功能一个功能的基础上的字段。除了特定位置的错误信息，这可能涉及到强度的错误信息。此外，列联表verifcation统计可以发现使用新的定义命中，遗漏和虚假报警（正确的底片是更难以评估，但也可以做）。
Currently, there is some functionality for performing the analyses introduced in Davis et al. (2006,2009), including the merge/match algorithm of Gilleland et al (2008), as well as the SAL technique of Wernli et al (2008, 2009).
目前，有用于执行在Davis等人的分析引入的一些功能。 （2006,2009），包括合并/匹配算法的Gilleland等人（2008年），以及SAL的：万恩利等人（2008年，2009年）的技术。
FeatureSuitePrep, FeatureFunPrep, FeatureSuite, saller, deltamm, convthresh, FeatureMatchAnalyzer
FeatureSuitePrep，FeatureFunPrep，FeatureSuite，saller，deltamm，convthresh，FeatureMatchAnalyzer
2d. Geometrical characterization measures:
2D。几何特性措施：
Perhaps the measures in this sub-heading are best described as part-and-parcel of 2c. They are certainly useful in that domain, but have been proposed also for entire fields by AghaKouchak et al. (2011); though similar measures have been applied in, e.g., MODE. The measures introduced in AghaKouchak et al. (2011) available here are: connectivity index (Cindex), shape index (Sindex), and area index (Aindex). See the help files for each to learn more.
也许在这个子标题是最好的措施的一部分和包裹为2c。他们肯定是有用的在该域中，但也已经提出的AghaKouchak等整个领域。 （2011年），虽然类似的措施已经得到了应用，例如，MODE。推出的措施在AghaKouchak等。 （2011）是：连接性指数（CINDEX），形状指数（Sindex），面积指数（Aindex）。每个了解更多，请参阅帮助文件。
3. Statistical inferences for spatial (and/or spatiotemporal) fields:
3。空间（和/或时空）字段的统计推论：
In addition to the methods categorized in Gilleland et al. (2009), there are also functions for making comparisons between two spatial fields. Currently, this entails only one such method (more to come), whcih requires temporal information as well. It is the field significance approach detailed in Elmore et al. (2006). It involves using a circular block bootstrap (CBB) algorithm (usually for the mean error) at each grid point (or location) individually to determine grid-point significance (null hypothesis that the mean error is zero), and then a semi-parametric Monte Carlo method viz. Livezey and Chen (1983) to determine field significance.
除了分类在Gilleland等方法。 （2009），也有两个空间字段之间进行比较的功能。目前，这要求只有一个这样的方法（来），克服了要求和时间信息。这是埃尔莫尔等领域意义的做法详细介绍。 （2006年）。它采用了圆形的块引导（CBB）算法（通常为平均误差），在每个格点（或位置），以便确定格点的意义（原假设的平均误差为零），然后一个半参数蒙特卡罗方法，即。 Livezey和Chen（1983），以确定领域的意义。
spatbiasFS, LocSig, MCdof
spatbiasFS，LocSig，MCdof
In addition, the mean loss differential approach of Hering and Genton (2011) is included via the function, spatMLD, including the loss functions: absolute error (abserrloss), square error (sqerrloss) and correlation skill (corrskill), as well as the distance map loss function (distmaploss) introduced in Gilleland (2012).
此外，平均损失差的方法，赫林和Genton先生（2011）的通过的功能，spatMLD，包括损失函数：绝对误差（abserrloss），误差平方（sqerrloss ）和相关技能（corrskill）的距离，以及图损失函数（distmaploss）介绍，Gilleland（2012年）。
4. Other:
4。其他：
The bias corrected TS and ETS (or TS dHdA and ETS dHdA) introduced in Mesinger (2008) are now included within the vxstats function.
的偏压校正，TS和介绍在Mesinger（2008年）的ETS（或TS dHdA的ETS dHdA）现在包含在vxstats功能。
The 2-d Gaussian Mixture Model (GMM) approach introduced in Lakshmanan and Kain (2010) can be carried out using the gmm2d function (to estimate the GMM) and the associated summary function calculates the parameter comparisons. Also available is a plot and predict function, but it can be very slow to run. The gmm2d employs an initialization function that takes the K largest object areas (connected components) and uses their centroids as initial estimates for the means, and uses the axes as initial guesses for the standard deviations. The user may supply their own initial estimate function.
Lakshmanan提供和Kain（2010年）的2-D高斯混合模型（GMM）的方法介绍，可以使用gmm2d函数（估计的GMM），并进行相关的summary函数计算参数比较。同时还提供一个plot和predict功能，但它可以是非常缓慢的运行。 gmm2d采用了初始化函数，它的k个最大的对象区域（连接组件），并使用他们的质心作为初始估计为手段，使用的轴的标准差作为初始猜测。用户可以提供自己的初步估计功能。
The S1 score and anomaly correlation (ACC) are available through the functions S1 and ACC. See Brown et al. (2012) and Thompson and Carter (1972) for more on these statistics.
S1得分和异常的相关性（ACC）可通过的功能S1和ACC。见Brown等人的。 （2012年）和汤普森和卡特（1972年），这些统计。
Also included is a function to do the geographic box-plot of Willmott et al. (2007). See the help file for GeoBoxPlot.
还包括一个功能做威尔莫特等人的GEO箱图。 （2007年）。请参阅帮助文件GeoBoxPlot。
Datasets:
数据集：
All of the initial Spatial Forecast Verification Inter-Comparison Project (ICP, http://www.ral.ucar.edu/projects/icp) data sets used in the special collection of the Weather and Forecasting journal are included. See the help file for 'obs0426', which will give information on all of the datasets included, as well as an example for plotting the data.
所有的的的初始空间预报检验间比较项目（ICP，http://www.ral.ucar.edu/projects/icp）数据集的特备收集的天气和预报杂志。请参阅帮助文件obs0426“，这将给包括所有的数据集上的信息，以及为数据作图的例子。
Ebert (2008) provides a nice review of these methods. Roberts and Lean (2008) describes one of the methods, as well as the primary boxcar kernel smoothing method used throughout this package. Gilleland et al. (2009, 2010) provides an overview of most of the various recently proposed methods, and Ahijevych et al. (2009) describes the data sets included in this package. Some of these have been applied to the ICP test cases in Ebert (2009).
艾伯特（2008年）这些方法提供了一个很好的回顾。罗伯茨和精益生产（2008）描述的方法，以及整个包的主要棚车核平滑方法。 gilleland等。 （2009年，2010年）规定的概述大部分的最近提出的各种方法，和Ahijevych等。 （2009）描述了数据集包含在这个套件。其中有些已被施加到ICP测试用例在埃伯特（2009）。
Additionally, one of the NIMROD cases (as provided by the UK Met Office) analyzed in Casati et al (2004) (case 6) is included along with approximate lon/lat locations. See the help file for UKobs6 more information.
此外，在，卡萨蒂等（2004）（案例6）分析的的尼姆罗德情况下（如英国气象局提供）包括沿与近似的经度/纬度位置。 UKobs6更多信息，请参阅帮助文件。
A spatio-temporal verification dataset is also included for testing the method of Elmore et al. (2006). See the help file for 'GFSNAMfcstEx' for more information.
甲时空验证数据集还包括用于测试埃尔莫尔等人的方法。 （2006年）。为“GFSNAMfcstEx的更多信息，请参阅帮助文件。
（作者）----------Author(s)----------
Eric Gilleland
参考文献----------References----------
实例----------Examples----------
## The example below may take a few minutes to run,[＃下面的例子可能需要几分钟的时间运行，]
## and therefore is commented out in order for R's[＃因此被注释掉，以便R的]
## package checks to run in a shorter amount of time.[＃包检查运行在一个较短的时间量。]
## Compare the results to Ebert (2009) Fig. 5.[艾伯特（2009）＃的结果进行比较。 5。]
##[＃]
## Note that 'levels' refer to number of grid points,[＃需要注意的是“水平”是指网格点的数量，]
## which for this example are ~4 km, so that the[＃这个例子~4公里，从而使]
## plot labels differ by a factor of 4 from Ebert (2009).[＃图标签不同了4倍，从艾伯特（2009年）。]
## Future versions of this package will allow for different[＃这个包的未来版本将允许不同]
## labelling.[＃标签。]
## Not run: [＃不运行：]
data( pert004)
data(pert000)
hold <- hoods2dPrep( "pert004", "pert000", thresholds=c(1, 2, 5, 10, 20, 50), levels=c(1, 3, 5, 9, 17, 33, 65, 129, 257), units="mm/h")
look <- hoods2d( hold, which.methods=c("fss", "multi.event"), verbose=TRUE)
plot( look)
look2 <- pphindcast2d( hold, verbose=TRUE)
look2
# Location measures.[地点措施。]
data( geom000)
data(geom001)
hold <- locmeasures2dPrep("geom001", "geom000", thresholds=c(0.1,50.1), k=c(4,0.975), alpha=c(0.1,0.9), units="in/100")
hold2 <- locmeasures2d( hold)
summary( hold2)
## End(Not run)[＃（不执行）]
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注：
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发表于 2012-9-30 12:58:31
