phenoAmp(wq)
phenoAmp()所属R语言包:wq
Phenological amplitude
物候振幅
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Finds various measures of the amplitude of the annual cycle, or of some specified month range.
查找的年度周期的振幅,或一些指定月份范围的各种措施。
用法----------Usage----------
## S4 method for signature 'ts'
phenoAmp(x, mon.range = c(1, 12))
## S4 method for signature 'zoo'
phenoAmp(x, mon.range = c(1, 12))
参数----------Arguments----------
参数:x
A monthly time series, or a class "zoo" object.
一个月度时间序列,或一类"zoo"对象的。
参数:mon.range
A vector of two numbers specifying the month range to be considered.
两个数字指定月份范围的向量加以考虑。
Details
详细信息----------Details----------
phenoAmp gives three measures of the amplitude of a seasonal cycle: the range, the range divided by the mean, and the standard deviation divided by the mean, i.e., the coefficient of variation.
phenoAmp给出了三个一个季节性周期的振幅的措施:的范围内,的范围内的平均值,和的标准偏差除以平均值,即,变异系数除以。
These measures can be restricted to a subset of the year by giving the desired range of month numbers. This can be useful for isolating measures of, say, the spring and autumn phytoplankton blooms in temperate waters. In the case of a monthly time series, a non-missing value is required for every month or the result will be NA, so using a period shorter than one year can also help avoid any months that are typically not covered by the sampling program. Similarly, in the case of dated observations, a shorter period can help avoid times of sparse data.
这些措施,可以被限制为一个子集的年给予所需的范围内的月份数。这可能是有用的,也就是说,在温带水域春季和秋季浮游植物大量繁殖隔离措施。非缺失值的月度时间序列的情况下,每个月需要的结果将是NA,使用期限短于一年,还可以帮助避免任何个月,通常不包括在抽样方案。同样的,过时的观察的情况下,更短的时间内可以帮助避免稀疏数据的时候。
tsMake can be used to produce "ts" and "zoo" objects suitable as arguments to this function.
tsMake可以用于生产"ts"和"zoo"对象适合作为这个函数的参数。
值----------Value----------
A data frame.
一个数据框。
参考文献----------References----------
参见----------See Also----------
phenoPhase, tsMake
phenoPhase,tsMake
实例----------Examples----------
y <- sfbayChla[, 's27']
p2 <- phenoAmp(y)
p2
apply(p2, 2, mean, na.rm=TRUE)
phenoAmp(y, c(1, 6))
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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