差异化遵循Engbert和Kliegl(2003)
此方法计算预定义时间窗口上的速度通过控制ek_速度_时间_窗口
默认为20ms。根据采样_比率
参数,并确保为古怪的等于或大于3的数字。也就是说,对于是三个样本长,速度是基于一个样本计算的电流之前和之后的一个样本,对于五个样本窗口-两个前面和后面的示例等。代码总是以请求的窗口大小,但都会迭代地将其减少两个样本以适应试验限值和缺失值。安不适用
速度是仅当最小窗口(三个样本宽度)都不能已使用。
通常,计算水平和垂直速度分量作为\[v_x[i]=\裂缝{\sum_{j=1}^{(N-1)/2}x[i+j]-x[i-j]}{\sum_{j=1}^{(N-1)/2}2j\cdot\Delta t}\]哪里\(i)是样本的索引,\(Δt=\frac{1}{sample ~ rate}\)是一个单个采样帧的持续时间,以及\(否)是一个古怪的的整数宽度用于计算速度的移动平均值。在Engbert和Kliegl(2003),\(N=5)和\(增量t=4\)(250 Hz采样率)转换为20ms移动平均窗口(在方法实现)。下面是显示等价性的推导上式至中式1Engbert和Kliegl(2003)。对于\(N=5):\[\frac{\sum_{j=1}^{(5-1)/2}x[i+j]-x[i-j]}{\sum_{j=1}^{(5-1)/2}2j\cdot\Delta t}=\] \[\frac{\sum_{j=1}^{2} x个【i+j】-x[i-j]}{\sum{j=1}^{2} 第2节\cdot\Delta t}=\]
\[\frac{x[i+1]-x[i-1]+x[i+2]-x[i-2]}{2\增量t+4\增量t}=\] \[\frac{x[i+2]+x[i+1]-x[i-1]-x[2]}{6\增量t} \]
比较两种方法
这两种方法产生了可比较但不同的速度值。然而,这对扫视检测只有很小的影响,请参阅渐晕使用样本投票.
#用两种方法计算速度
标高(_E)<-香囊::差异(_E)(x个=单列(_T)$x、,
年=单列(_T)$y中,
试验= 代表(1,nrow公司(单列)),
采样速率(_R)= 500)
级别(_N)<-香囊::差异(_N)(x个=单列(_T)$x、,
年=单列(_T)$是的,
试验= 代表(1,nrow公司(单列)),
采样速率(_R)= 500)
水平比较<- na.省略(数据帧(EK公司=标高(_E)[[“amp”]],NH公司=级别(_N)[[“amp”]]))%>%
数字播放器::滤波器(NH> 0)%>% #由于筛选,值可能为负值
数字播放器::突变(logEK(对数)= 日志(EK),对数NH= 日志(NH))
#绘制振幅比较
皮尔逊(pearson_rho)<- cor公司(水平比较[[“EK”]],速度比较[[“NH”]])
ggplot图(数据=级别比较,原子发射光谱(x个=埃克森美孚,年=NH))+
地理_阿比林()+
地理点()+
缩放_x_log10()+
缩放y_log10()+
实验室(副标题= 把格式数据写成串(“皮尔逊ρ=%.2f\n个对数转换速度的皮尔逊ρ=%.2f“,皮尔逊,cor公司(水平比较[[“logEK”]],级别比较[[“logNH”]])),
x个= “维克”,
年= “Vnh”)
![](data:image/png;base64,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)
#绘图
ggplot图(数据= 无效的,原子能机构(x个=水平比较[[“EK”]]-水平比较[[“NH”]]))+
地理直方图(垃圾箱= 50)+
xlab公司(“Vek-Vnh”)+
实验室(标题= “通过两种方法计算的速度差异”)
![](data:image/png;base64,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)
实现和使用自定义差异功能
自定义函数应如下所示
差异自定义(_C)<- 功能(x,y,试验,抽样率,选项=无效的) {
#计算垂直和水平构件
数据流<- 数据帧(x个=...,#区分x的值
年=...)#y的微分值
#计算振幅
数据流[[“amp”]]= 平方英尺(df[[“x”]]^2 +数据流[[“是”]]^2))
#返回包含列x、y和amp的data.frame
返回(df)
}
传递给函数的参数(所有向量都具有相同的长度):
x个
,年
值为x和y的向量凝视坐标或速度分量(用于加速度)。
采样速率(_R)
标量值(Hz)。
试验
每个样本的试验指数向量。
选项
带有特定方法选项的命名列表。请参见下面的示例介绍了如何使用它们。另请参见选项_默认值()
功能。
函数必须返回包含三列的data.frame:x个
(水平分量),年
(垂直组件),以及放大器
(振幅)。
下面是一个关于试探的简单速度函数示例边界
图书馆(dplyr)
差异自定义(_C)<- 功能(x,y,试验,抽样率,选项=无效的) {
#计算帧时间步长
增量_t_s<- 1 /采样速率(_R)
#---微分(计算速度或加速度)和滤波
数据帧(试验=试验,
x个=x、,
年=年)%>%
#计算每次试验的速度和加速度
分组方式(_B)(试验)%>%
突变(x个=(x)- 滞后(x) )/增量_秒,
年=(年)- 滞后(y) )/增量_秒,
放大器= 平方英尺(x)^2 +年^2))
}
这是一个类似的函数,但它使用了一个可选的自定义速度滞后
参数,以便它可以不同从1开始
差异标签n<- 功能(x,y,试验,抽样率,选项=无效的) {
#获取滞后或使用默认值
滞后<-香囊::选项_或默认(选项,“自定义速度滞后”,1)
#计算帧时间步长
增量_秒<- 1 /采样速率(_R)
#---微分(计算速度或加速度)和滤波
数据帧(试验=试验,
x个=x中,
年=年)%>%
#计算每次试验的速度和加速度
分组方式(_B)(试验)%>%
突变(x个=(x)- 滞后(x,lagn))/增量_秒,
年=(年)- 滞后(y,lagn))/增量_秒,
放大器= 平方英尺(x)^2 +年^2))
}