convthresh(SpatialVx)
convthresh()所属R语言包：SpatialVx
Convolution threshold feature identification
卷积阈值特征识别
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Identify features within a field via thresholding a convolution-smoothed version of the field.
通过一个卷积平滑的领域的阈值，确定一个领域内的功能。
用法----------Usage----------
convthresh(object, smoothfun = "disk2dsmooth", smoothpar = 1, smoothfunargs = NULL, thresh = 1e-08, idfun = "disjointer", zero.down = FALSE, ...)
salIDfun(object, fac = 0.06666667, q = 0.95, wash.out = NULL, thresh = NULL, idfun = "disjointer", ...)
disjointer(x, method = "C")
参数----------Arguments----------
参数：object
list object of class "FeatureSuitePrep" as returned by FeatureSuitePrep. Not used by disjointer (there for compatibility).
列表对象类“FeatureSuitePrep”返回FeatureSuitePrep。不使用disjointer（有兼容性）。
参数：x
binary matrix identifying event points.
二进制矩阵识别事件点。
参数：smoothfun
character naming a convolution smoothing function that takes argument x (the field to be smoothed, not the same as the argument to disjointer) and a smoothing parameter as the first two arguments. Other arguments are allowed, and are included through smoothfunargs. Default is a disk kernel smoothing function.
字符命名的卷积平滑函数，它的参数x（领域进行平滑处理，不一样的参数disjointer）和平滑的前两个参数的参数。其他参数是允许的，和，包括通过smoothfunargs。默认情况下是一个磁盘核平滑功能。
参数：smoothpar
single numeric giving the smoothing parameter for smoothfun.
单数字的平滑参数smoothfun。
参数：smoothfunargs
list object with named additional arguments to smoothfun.
列表对象命名的额外的参数到smoothfun。
参数：fac
numeric giving a factor by which to multiply the R quantile in determining the threshold from the fields. Default is to multiply by ~ 1/15 as in Wernli et al (2008, 2009). This is not used if thresh is supplied.
数字的一个因素，在确定阈值乘的R分量的领域。默认值是乘以~1/15万恩利等人（2008年，2009年）。这是不使用，，如果thresh提供。
参数：thresh
numeric vector of length one or two giving the threshold over which (and including, i.e., >=) events are defined. If different thresholds are used for the forecast and verification fields, then the first element is the threshold for the forecast, and the second is that for the verification field. For salIDfun, if this value is NULL, the thresholds are determined by fac*R_q, where R_q is the q-th quantile of the field (different thresholds for each field are used).
数字矢量的长度为一个或两个给阈值以上（包括，即，> =）事件的定义。如果不同的阈值，用于预测和核查字段，然后第一个元素是为预测的阈值，和所述第二是为验证字段。对于salIDfun，如果这个值是NULL，阈值被确定FAC *，其中R_q是的q个位数的字段（每个字段的不同的阈值）R_q，。
参数：q
If thresh is NULL, then this is the quantile used to deterimne the thresholds for the two fields.
如果thresh是NULL，那么这是分位数，用于deterimne这两个领域的阈值。
参数：idfun
character naming the function used to identify (and label) individual features in the convolved and thresholded field. Must take argument x, which is the convolved and thresholded (binary) field.
字符命名的功能，用来标识（标签）的卷积和阈值的字段的各个功能。一定要带的话，x，这是卷积和阈值（二进制）字段。
参数：wash.out
(optional) numeric giving a lower threshold over which the quantile for determining the threshold should be found. If NULL, this is not performed. Only used if thresh is NULL.
（可选）数字确定阈值的位数应给予一个较低的阈值。如果为NULL，这不被执行。仅用于如果thresh是NULL。
参数：zero.down
logical, should negative values and relatively very small values be set to zero after smoothing the fields? For thresholds larger than such values, this argument is moot. zapsmall is used to set the very small positive values to zero.
逻辑，应负值和相对非常小的值被设置为零平滑后的字段？对于低于该值较大的阈值时，这种说法是毫无意义的。 zapsmall用于设置的非常小的正的值设置为零。
参数：method
character giving the connected components algorithm to be used in function connected from spatstat.
字符给算法用于连接组件的功能connectedspatstat。
参数：...
additional arguments to idfun.
附加参数到idfun。
Details
详细信息----------Details----------
The convolution threshold technique is a simple way of identifying features (referred to as objects in image analysis circles where features are the attributes thereof) within a field. First, the field is smoothed using a convolution smoother, and then it is set to a binary image where everything above a given threshold is set to one. Individual features are identified through any choice of function given by idfun. The default is to use a connected components algorithm using the spatstat function connected.
卷积的阈值的技术是在某个字段内的一个简单的方法识别的功能（称为对象在图像分析界的特点是它们的属性）。首先，该字段被平滑化使用卷积平滑，然后它被设置为一个二进制图像，其中被设置为一个给定的阈值以上的一切。通过任何功能的idfun选择个别特征识别。默认是使用一个连接组件算法，使用连接的spatstat功能的。
This is the method used by Davis et al. (2006a,b).
这是由Davis等人所使用的方法。 （2006年a，b）所示。
值----------Value----------
convthresh and salIDfun return a list object with comopnents:
convthresh和salIDfun返回一个列表对象comopnents：
参数：X.feats,Y.feats
The identified features for the verification and forecast fields as returned by the idfun function.
返回的idfun功能的验证和预报场的特征。
参数：X.labeled,Y.labeled
matrices of same dimension as the forecast and verification fields giving the images of the convolved and thresholded verification and forecast fields, but with each individually identified object labeled 1 to the number of objects in each field.
给图像卷积和阈值的验证和预测字段，预测和检验场，但与每个单独识别对象相同的尺寸的矩阵的标记为1的对象的数量，在每个字段中。
（作者）----------Author(s)----------
Eric Gilleland
参考文献----------References----------
参见----------See Also----------
connected, as.im, tess, tiles, owin, FeatureSuite, FeatureSuitePrep
connected，as.im，tess，tiles，owin，FeatureSuite，FeatureSuitePrep
实例----------Examples----------
x <- y <- matrix(0, 10, 12)
x[2:3,c(3:6, 8:10)] <- 1
y[c(4:7, 9:10),c(7:9, 11:12)] <- 1
hold <- FeatureSuitePrep("y", "x")
look <- convthresh( hold, smoothpar=0.5)
length( look$X.feats) # two objects in x.[两个物体在x。]
length( look$Y.feats) # four ovjects in y.[4 ovjects在y。]
plot( look$X.feats)
plot( look$Y.feats)
## Not run: [＃不运行：]
data(pert000)
data(pert004)
hold <- FeatureSuitePrep("pert004", "pert000")
look <- convthresh( hold, smoothpar=3.5)
length(look$X.feats)
length(look$Y.feats)
image.plot(look$sIx)
image.plot(look$sIy)
## 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。
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 2012-9-30 12:54:32
