estimate-methods(spcosa)
estimate-methods()所属R语言包：spcosa
Estimating Statistics
估计统计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Methods for estimating statistics given a spatial sample.
估计统计方法给出了空间采样。
方法----------Methods----------
statistic = "character", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame" estimates one of the following statistics, depending on the value of argument statistic: spatial mean, spatial variance, sampling variance, standard error, or scdf. See the examples below for details.
统计=“CompactStratification”=“字符”，分层，samplingPattern =“SamplingPatternRandomSamplingUnits”数据=“数据框”估计下面的统计数据之一，根据参数值的statistic：spatial mean，spatial variance，sampling variance，standard error或scdf。的详细信息，请参阅下面的例子。
statistic = "character", stratification = "CompactStratificationEqualArea", samplingPattern = "SamplingPatternRandomComposite", data = "data.frame" estimates one of the following statistics, depending on the value of argument statistic: spatial mean, sampling variance, or standard error.
统计=“字符”，分层=“CompactStratificationEqualArea”“，samplingPattern”SamplingPatternRandomComposite“数据=”数据框“估计，下面的统计数据，根据参数值的statistic：spatial mean，sampling variance或standard error。
statistic = "SamplingVariance", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame" estimates the sampling variance. See "SamplingVariance" for more details.
统计=的“SamplingVariance”，分层=“CompactStratification”，samplingPattern =“SamplingPatternRandomSamplingUnits”数据=“数据框”估计抽样方差。见"SamplingVariance"更多详情。
statistic = "StandardError", stratification = "CompactStratificationEqualArea", samplingPattern = "SamplingPatternRandomComposite", data = "data.frame" estimates the standard error of the spatial mean. See "StandardError" for more details.
统计=“的StandardError”，分层的“CompactStratificationEqualArea”，samplingPattern =“SamplingPatternRandomComposite”，数据“数据框”估计标准误差的空间平均。见"StandardError"更多详情。
statistic = "SpatialCumulativeDistributionFunction", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame" estimates the spatial cumulative distribution function (SCDF). See "SamplingPatternRandomSamplingUnits" for more details.
统计=“SpatialCumulativeDistributionFunction”的，分层=“CompactStratification”，samplingPattern =“SamplingPatternRandomSamplingUnits”，数据=“数据框”估计的空间累积分布函数（新加坡民防部队）。见"SamplingPatternRandomSamplingUnits"更多详情。
statistic = "SpatialMean", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame" estimates the spatial mean. See "SpatialMean" for more details.
统计=的“SpatialMean”，分层=“CompactStratification”，samplingPattern =“SamplingPatternRandomSamplingUnits”数据=“数据框”估计平均的空间。见"SpatialMean"更多详情。
statistic = "SpatialVariance", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame" estimates the spatial variance. See "SpatialVariance" for more details.
统计=的“SpatialVariance”，分层=“CompactStratification”，samplingPattern =“SamplingPatternRandomSamplingUnits”数据=“数据框”估计的空间变异。见"SpatialVariance"更多详情。
实例----------Examples----------
## Not run: [＃不运行：]
# read vector representation of the "Mijdrecht" area (the Netherlands)[读取向量表示的“迈德雷赫特区”（荷兰）]
shp <- readOGR(dsn = system.file("maps", package = "spcosa"), layer = "mijdrecht")
# stratify into 30 strata (set nTry to a lower value to speed-up computation)[分层为30个地层（n请尝试一个较低的值，以加速计算）]
myStratification <- stratify(shp, nStrata = 30, nTry = 10, verbose = TRUE)
# random sampling of two sampling units per stratum[每个阶层的两个抽样单位的随机抽样]
mySamplingPattern <- spsample(myStratification, n = 2)
# plot sampling pattern[图采样模式]
plot(myStratification, mySamplingPattern)
# simulate data (in real world cases these data have to be obtained by field work)[模拟数据（在现实世界的情况下，这些数据必须通过以下方式获得野外工作）]
myData <- as(mySamplingPattern, "data.frame")
myData$observation <- rnorm(n = nrow(myData), mean = 10, sd = 1)
# design-based inference[设计的推理]
estimate("spatial mean", myStratification, mySamplingPattern, myData["observation"])
estimate("sampling variance", myStratification, mySamplingPattern, myData["observation"])
estimate("standard error", myStratification, mySamplingPattern, myData["observation"])
estimate("spatial variance", myStratification, mySamplingPattern, myData["observation"])
estimate("scdf", myStratification, mySamplingPattern, myData["observation"])
## End(Not run)[＃（不执行）]
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注：
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发表于 2012-9-30 14:29:06
