ClassStat(SDMTools)
ClassStat()所属R语言包：SDMTools
Landscape Class Statistics
景观类统计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
ClassStat calculates the class statistics for patch types identified in a matrix of data or in a raster of class 'asc' (SDMTools & adehabitat packages), 'RasterLayer' (raster package) or 'SpatialGridDataFrame' (sp package).
ClassStat计算斑块类型中确定的矩阵的数据，或在类递增（SDMTools＆adehabitat软件包），RasterLayer（光栅包）或SpatialGridDataFrame栅格（藻的包类的统计信息） 。
用法----------Usage----------
ClassStat(mat,cellsize=1,bkgd=NA,latlon=FALSE)
参数----------Arguments----------
参数：mat
a matrix of data with patches identified as classes (unique integer values) as e.g., a binary lanscape of a species distribution or a vegetation map. Matrix can be a raster of class 'asc' (adehabitat package), 'RasterLayer' (raster package) or 'SpatialGridDataFrame' (sp package)
类（唯一的整数值），例如，一个二进制园境的物种分布或植被图确定为补丁的数据的矩阵。基质可以是类递增（adehabitat包），的RasterLayer（栅格包）或SpatialGridDataFrame（藻封装栅格）
参数：cellsize
cell size (in meters) is a single value representing the width/height of cell edges (assuming square cells)
单元尺寸（米）的是一个单一的值，该值表示小区边缘的宽度/高度（假设正方形单元）
参数：bkgd
the background value for which statistics will not be calculated
背景值的统计信息将不会被计算
参数：latlon
boolean value representing if the data is geographic. If latlon == TRUE, matrix must be of class 'asc', 'RasterLayer' or 'SpatialGridDataFrame'
布尔值，表示如果数据是GEO。如果latlon == TRUE，矩阵必须是类ASC，RasterLayer“或”SpatialGridDataFrame的“
Details
详细信息----------Details----------
The class statistics are based on statistics calculated by fragstats http://www.umass.edu/landeco/research/fragstats/fragstats.html.
类统计数据是根据的统计计算FRAGSTATS http://www.umass.edu/landeco/research/fragstats/fragstats.html。
值----------Value----------
a data.frame listing
数据框上市
参数：class
a particular patch type from the original input matrix (mat).
一个特定的接插型从原始输入矩阵（mat）。
参数：n.patches
the number of patches of a particular patch type or in a class.
一些特定的修补程序类型或类的补丁。
参数：total.area
the sum of the areas (m2) of all patches of the corresponding patch type.
所有修补程序的相应的补丁类型的面积（m2）的总和。
参数：prop.landscape
the proportion of the total lanscape represented by this class
由这个类表示的总园境的比例
参数：patch.density
the numbers of patches of the corresponding patch type divided by total landscape area (m2).
总景观面积（平方米）除以相应的补丁类型的补丁的数量。
参数：total.edge
the total edge length of a particular patch type.
一个特定的接插型的总边缘长度。
参数：edge.density
edge length on a per unit area basis that facilitates comparison among landscapes of varying size.
边长上每单位面积的基础上，有利于比较不同大小之间的景观。
参数：landscape.shape.index
a standardized measure of total edge or edge density that adjusts for the size of the landscape.
一个标准化的措施的总的边缘或边缘密度，调整的大小的风景。
参数：largest.patch.index
largest patch index quantifies the percentage of total landscape area comprised by the largest patch.
最大斑块指数量化的百分比由总景观面积最大的补丁。
参数：mean.patch.area
average area of patches.
平均面积的补丁。
参数：sd.patch.area
standard deviation of patch areas.
斑块面积的标准偏差。
参数：min.patch.area
the minimum patch area of the total patch areas.
最低修补程序补丁区域的总面积。
参数：max.patch.area
the maximum patch area of the total patch areas.
最大斑块面积的总斑块面积。
参数：perimeter.area.frac.dim
perimeter-area fractal dimension equals 2 divided by the slope of regression line obtained by regressing the logarithm of patch area (m2) against the logarithm of patch perimeter (m).
周边区域的分形维数等于2除以斑块面积（平方米）的斑块周长（米）的对数的对数进行回归得到的回归直线的斜率。
参数：mean.perim.area.ratio
the mean of the ratio patch perimeter. The perimeter-area ratio is equal to the ratio of the patch perimeter (m) to area (m2).
的平均值的比率斑块周长。的周长面积比等于斑块周长（m）至面积（m2）的比率。
参数：sd.perim.area.ratio
standard deviation of the ratio patch perimeter.
标准偏差的比率斑块周长。
参数：min.perim.area.ratio
minimum perimeter area ratio
最小周长面积比
参数：max.perim.area.ratio
maximum perimeter area ratio.
最大周长面积比。
参数：mean.shape.index
mean of shape index
平均形状指数
参数：sd.shape.index
standard deviation of shape index.
形状指数的标准差。
参数：min.shape.index
the minimum shape index.
最低形状指数。
参数：max.shape.index
the maximum shape index.
的最大形状指数。
参数：mean.frac.dim.index
mean of fractal dimension index.
平均分形维数。
参数：sd.frac.dim.index
standard deviation of fractal dimension index.
分形维数的标准差。
参数：min.frac.dim.index
the minimum fractal dimension index.
最低分形维数。
参数：max.frac.dim.index
the maximum fractal dimension index.
最大的分形维数。
参数：total.core.area
the sum of the core areas of the patches (m2).
核心领域的补丁（M2）的总和。
参数：prop.landscape.core
proportional landscape core
比例景观核心
参数：mean.patch.core.area
mean patch core area.
平均斑块核心区。
参数：sd.patch.core.area
standard deviation of patch core area.
标准偏差的补丁核心区。
参数：min.patch.core.area
the minimum patch core area.
最低修补程序的核心区。
参数：max.patch.core.area
the maximum patch core area.
最大的补丁核心区。
参数：prop.like.adjacencies
calculated from the adjacency matrix, which shows the frequency with which different pairs of patch types (including like adjacencies between the same patch type) appear side-by-side on the map (measures the degree of aggregation of patch types).
计算出的邻接矩阵，其中显示斑块类型的不同的对（包括类似之间邻接关系相同的接插型）出现的频率，在图上（测量的修补程序类型的聚集度）侧由侧。
参数：aggregation.index
computed simply as an area-weighted mean class aggregation index, where each class is weighted by its proportional area in the landscape.
简单计算的面积加权平均类聚集指数，其中每类的加权比例区的景观。
参数：lanscape.division.index
based on the cumulative patch area distribution and is interpreted as the probability that two randomly chosen pixels in the landscape are not situated in the same patch
的累积斑块面积分布的基础上，被解释为两个随机选择的像素的景观位于相同的修补程序的可能性
参数：splitting.index
based on the cumulative patch area distribution and is interpreted as the effective mesh number, or number of patches with a constant patch size when the landscape is subdivided into S patches, where S is the value of the splitting index.
的累积斑块面积分布的基础上，被解释为有效的目数，或数量的补丁时的恒定补丁大小的风景细分的补丁，其中S是分裂指数的价值。
参数：effective.mesh.size
equals 1 divided by the total landscape area (m2) multiplied by the sum of patch area (m2) squared, summed across all patches in the landscape.
等于1除以总景观面积（m2）乘以斑块面积的总和（M2）的平方，加总所有修补程序的景观。
参数：patch.cohesion.index
measures the physical connectedness of the corresponding patch type.
测量相应的补丁类型的物理连通性。
----------Author(s)----------
Jeremy VanDerWal <a href="mailto:jjvanderwal@gmail.com">jjvanderwal@gmail.com</a>
参考文献----------References----------
<h3>See Also</h3>
实例----------Examples----------
#define a simple binary matrix[定义一个简单的二进制矩阵]
tmat = { matrix(c( 0,0,0,1,0,0,1,1,0,1,
0,0,1,0,1,0,0,0,0,0,
0,1,NA,1,0,1,0,0,0,1,
1,0,1,1,1,0,1,0,0,1,
0,1,0,1,0,1,0,0,0,1,
0,0,1,0,1,0,0,1,1,0,
1,0,0,1,0,0,1,0,0,1,
0,1,0,0,0,1,0,0,0,1,
0,0,1,1,1,0,0,0,0,1,
1,1,1,0,0,0,0,0,0,1),nr=10,byrow=TRUE) }
#do the connected component labelling[做连接的组件标签]
ccl.mat = ConnCompLabel(tmat)
ccl.mat
image(t(ccl.mat[10:1,]),col=c('grey',rainbow(length(unique(ccl.mat))-1)))
#calculate the patch statistics[计算的补丁统计]
ps.data = PatchStat(ccl.mat)
ps.data
#calculate the class statistics[计算类统计]
cl.data = ClassStat(tmat)
cl.data
#identify background data is 0[确定背景数据是0]
cl.data = ClassStat(tmat,bkgd=0)
cl.data
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。
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发表于 2012-9-29 23:34:14
