deltamm(SpatialVx)
deltamm()所属R语言包：SpatialVx
Merge and/or match identified features within two fields
合并和/或两个领域内匹配识别功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Merge and/or match identified features within two fields using the delta metric method described in Gilleland et al. (2008), or the mathcing method of Davis et al. (2006a).
合并和/或符合确定两个领域内使用的增量度量方法的描述在Gilleland等功能。 （2008），或的mathcing Davis等人的方法。 （2006A）。
用法----------Usage----------
deltamm(x, y = NULL, object = NULL, max.delta = Inf, verbose = FALSE, ...)
## S3 method for class 'deltamm'
plot(x, ...)
centmatch(x, y = NULL, object = NULL, criteria = 1, const = 14, verbose = FALSE)
参数----------Arguments----------
参数：x,y
For deltamm, a list object with components X.feats and Y.feats, each of which are list objects containing numbered components within which are objects of class "owin" containing logical matrices that define objects for the forecast (Y) and verification (X) fields, resp. For example, as returned by the convthresh function. For plot.deltamm a list object as returned by deltamm. Argument y is used if it is not NULL, otherwise argument x is used (but only one of x or y is used).
deltamm，一个列表对象与组件X.feats和Y.feats，其中每一项都包含编号列表对象，组件内的对象含类“owin”的逻辑矩阵定义对象预测（Y）和验证（X）领域，分别。例如，如由convthresh函数返回。对于plot.deltamm的列表对象返回的deltamm。参数y使用，如果不为NULL，否则的话，x使用（但只有一个x或y使用）。
参数：object
Not used. Provided for compatibility with the FeatureSutie function.
未使用。提供兼容性FeatureSutie功能。
参数：max.delta
single numeric giving a cut-off value for delta that disallows two objects to be merged or matched if the delta between them is larger than this value.
单一的数字，截止值Delta，不允许两个对象被合并或匹配，如果它们之间的增量是大于这个值。
参数：criteria
1, 2 or 3 telling which criteria for determining a match based on centroid distance, D, to use. The first (1) is a match if D is less than the sum of the sizes of the two features in question (size is the square root of the area of the feature). The second is a match if D is less than the average size of the two features in question. The third is a match if D is less than a constant given by the argument const.
1，2或3，说明哪用于确定质心的距离D的基础上匹配的标准，为使用。的第一（1）是一个匹配，如果D是小于问题的两个特征（大小是该功能的面积的平方根）的大小的总和。第二个是一个匹配，如果D是小于的平均大小的两个特征的问题。第三是比赛，如果D是小于一个常数的参数const。
参数：const
numeric giving the number of grid squares whereby if the centroid distance (D) is less than this value, a match is declared (only used if criteria is 3.
数字给由此如果质心的距离（D）小于该值，如同被声明（如果只使用criteria3的方格的数目。
参数：verbose
logical, should progress information be printed to the screen?
逻辑的发展，应以信息打印到屏幕上？
参数：...
For deltamm: additional optional arguments to the deltametric function from package spatstat. For plot.deltamm, additional arguments to the plot function.
对于deltamm：额外的可选参数，从包装deltametricspatstat功能。对于plot.deltamm，其他参数plot功能。
Details
详细信息----------Details----------
Gilleland et al. (2008) describe a method for automatically merging, and simultaneously, matching identified objects within two fields (a verification set). The method was proposed with the general method for spatial forecast verification introduced by Davis et al. (2006 a,b) in mind. It relies heavily on use of a binary image metric introduced by Baddeley (1992a,b) for comparing binary images; henceforth referred to as the delta metric, or just delta.
gilleland等。 （2008）描述了一种方法，用于自动合并，并同时，匹配识别的对象的两个字段内（一个验证集）。的方法，提出了用一般的方法引入的Davis等人的用于空间预测的验证。 （2006）A，B的初衷。它在很大程度上依赖于使用者巴德利（1992，二）引入一个二进制图象量度用于比较二进制图像;从此被称为作为Delta度量，或者只是增量。
The procedure is as follows. Suppose there are m identified forecast features and n identified verification features.
该过程如下。假设有米的预测功能和N识别验证功能。
1. Compute delta for each feature identified in the forecast field against each feature identified in the verification field. Store these values in an m X n matrix, Upsilon.
1。计算Delta确定每个功能在验证字段中确定的预报场对每个功能。这些值存储在一个m×n矩阵，埃普西隆。
2. For each of the m rows of Upsilon, rank the values of delta to identify the features, j_1, ..., j_n that provide the lowest (best) to highest (worst) value, and do the same for each of the n columns to find the forecast features i1, ...,i_m that yield the lowest to highest values for each verification feature.
2。对于每一个m行埃普西隆，排名Delta识别的功能，J_1，...，j_n提供最低的（最好）至最高值（最差）的值，并做相同的n列预测功能，I1，...，i_m产生最低到最高值，为每个验证功能。
3. Create a new m X n matrix, Psi, whose columns contain delta computed between each of the individual features in the forecast and (first column) the corresponding j_1 feature from the verification field, and each successive column, k, has delta between the i-th forecast feature and the union of j_1, j_2, ..., j_k.
3。创建一个新的米X n的矩阵，Psi范围其列包含增量计算在预测中的每一个的个别功能（第一列）相应J_1特征从验证字段之间，和每个连续的列，K，具有Delta的i之间的个预测功能和的联合J_1，J_2，...，j_k。
4. Create a similar m X n matrix, Ksi, that has delta computed between each individual feature in the verification field and the successively bigger unions i_1, ..., i_l for the l-th column.
4。创建类似的米X n的矩阵，KSI，具有增量在验证字段中的每个单独的功能和相继更大工会I_1，...，I_L的第l列之间计算。
5. Let Q=[Upsilon, Psi, Ksi], and merge and match objects based on the rankings of delta in Q. That is, find the smallest delta in Q, and determine which mergings (if any) and matchings correspond to this value. Remove the appropriate row(s) and column(s) of Q corresponding to the already determined matchings and/or mergings. Repeat this until all features in at least one field have been exhausted.
5。 Q = [埃普西隆，PSI，KSI，合并和匹配对象的基础上的排名Delta问：也就是说，在Q发现的最小增量，并确定的这mergings（如果有的话）和匹配相对应的这个值。中删除相应的行（s）和列（）对应于已确定的匹配和/或mergings的Q。重复上述操作，直到所有的功能，至少在一个领域已经用尽。
The above algorithm suffers from two deficiencies. First, features that are merged in one field cannot be matched to merged features in another field. One possible remedy for this is to run this algorithm twice, though this is not a universally good solution. Second, features can be merged and/or matched to features that are very different from each other. A possible remedy for this is to use the cut-off argument, max.delta, to disallow mergings or matchings between features whose delta value is not <= this cut-off. In practice, these two deficiencies are not likely very problematic.
上述算法遭受两个缺点。首先，被合并在一个字段中的功能，不能合并功能在另一场相匹配。这是一个可能的补救运行此算法的两倍，虽然这不是一个普遍良好的解决方案。第二，特征可以合并和/或彼此有很大的不同的功能相匹配。这是一个可能的补救，使用截止的说法，max.delta，到的禁止mergings或匹配特征之间的delta值是不<=这个截止。在实践中，这两个不足之处是没有可能是很成问题。
值----------Value----------
A list object is returned containing several components.
返回一个List对象包含几个部分组成。
参数：X.feats, Y.feats
list objects with one component for each possibly merged feature for the each of the forecast (Y.feats) and verification (X.feats) fields. Each component of these lists contains an object of class "owin" containing binary fields indicating the whereabouts of the possibly merged features.
列表对象的一个组成部分，每一个可能的合并功能的预测（Y.feats）和验证（X.feats）的领域。这些列表中的每个组件包含一个对象的类“owin”包含二进制字段表示可能合并的功能的下落的。
参数：X.labeled,Y.labeled
matrices of dimension equal to the original fields identifying the newly merged features of each field. If l features are matched between fields, then the first l features are labeled in 1 to l such that features labeled r <= l in one field match the corresponding feature labeled r in the other field.
矩阵的维数等于原来的字段识别新合并的每个字段的功能。如果l功能字段之间的匹配，则第l功能被标记为1至l，使得功能用r <=升在一个字段中匹配的相应的特征标记r在其他字段。
参数：mm.old.labels
an l X 2 matrix indicating which features from one field match to features of the other based on the feature labels that were passed into the function (i.e., these labels have now changed).
一个l X 2的矩阵功能，从一个场比赛的其他功能的基础上的功能标签，传递到函数（即，这些标签已经改变）。
参数：mm.new.labels
list object with components mm (an l X 2 matrix showing which features from one field match to which features of the other; moot because they are labeled identically), and unmatched (list object with components fcst and vx, which identify the newly labeled features in each field that were not matched to features in the other field.
列表对象与组件毫米（为l X 2的矩阵显示功能从一个场比赛的其他功能;实际意义，因为它们都标有相同），以及无与伦比的（列表对象与组件FCST和vx，确定新标记的功能在每个字段中的不匹配的其他领域中的功能。
参数：Q
an array of dimension n X m X 3 giving all of the delta values that were computed in determining the mergings and matchings.
n维数组X M X 3给所有的增量值，计算在确定mergings和匹配。
（作者）----------Author(s)----------
Eric Gilleland
参考文献----------References----------
参见----------See Also----------
convthresh, disjointer, deltametric, FeatureSuite, owin, tess, tiles, connected
convthresh，disjointer，deltametric，FeatureSuite，owin，tess，tiles，connected
实例----------Examples----------
x <- y <- matrix(0, 100, 100)
x[2:3,c(3:6, 8:10)] <- 1
y[c(4:7, 9:10),c(7:9, 11:12)] <- 1
x[30:50,45:65] <- 1
y[c(22:24, 99:100),c(50:52, 99:100)] <- 1
hold <- FeatureSuitePrep("y", "x")
look <- convthresh( hold, smoothpar=0.5)
par( mfrow=c(1,2))
image.plot( look$X.labeled)
image.plot( look$Y.labeled)
look2 <- deltamm( look)
look3 <- centmatch(look)
## Not run: [＃不运行：]
data(pert000)
data(pert004)
hold <- FeatureSuitePrep("pert004", "pert000")
look <- convthresh( hold, smoothpar=10.5)
par( mfrow=c(1,2))
zl <- range(c(c(look$X.labeled),c(look$Y.labeled)),finite=TRUE)
image.plot(look$X.labeled, zlim=zl)
image.plot(look$Y.labeled, zlim=zl)
look2 <- deltamm( look, verbose=TRUE)
image.plot(look2$X.labeled)
image.plot(look2$Y.labeled)
## End(Not run)[＃（不执行）]
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。
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发表于 2012-9-30 12:54:42
