R filter() 处理 NA

Question

我正在尝试实现 Chebyshev 滤波器来平滑时间序列，但不幸的是，数据序列中有 NA。

例如，

t <- seq(0, 1, len = 100)                     
x <- c(sin(2*pi*t*2.3) + 0.25*rnorm(length(t)),NA, cos(2*pi*t*2.3) + 0.25*rnorm(length(t)))

我正在使用切比雪夫过滤器：cf1 = cheby1(5, 3, 1/44, type = "low")

我正在尝试过滤排除 NA 的时间序列，但不会弄乱订单/仓位。所以，我已经尝试过na.rm=T，但似乎没有这样的论点。 那么

z <- filter(cf1, x)   # apply filter

谢谢你们。

Answer 1

尝试使用x <- x[!is.na(x)] 删除 NA，然后运行过滤器。

Answer 2

您可以使用 compelete.cases 函数预先删除 NA。您也可以考虑估算缺失的数据。查看 mtsdi 或 Amelia II 软件包。

编辑：

这是一个使用 Rcpp 的解决方案。这可能会有所帮助，因为速度很重要：

require(inline)
require(Rcpp)
t <- seq(0, 1, len = 100)
set.seed(7337)
x <- c(sin(2*pi*t*2.3) + 0.25*rnorm(length(t)),NA, cos(2*pi*t*2.3) + 0.25*rnorm(length(t)))
NAs <- x
x2 <- x[!is.na(x)]
#do something to x2
src <- '
Rcpp::NumericVector vecX(vx);
Rcpp::NumericVector vecNA(vNA);
int j = 0;   //counter for vx
for (int i=0;i<vecNA.size();i++) {
  if (!(R_IsNA(vecNA[i]))) {
    //replace and update j
    vecNA[i] = vecX[j];
    j++;
  }
 }
return Rcpp::wrap(vecNA);
'
fun <- cxxfunction(signature(vx="numeric",
                             vNA="numeric"),
                   src,plugin="Rcpp")
if (identical(x,fun(x2,NAs)))
    print("worked")
# [1] "worked"

Answer 3

我不知道ts 对象是否可以有缺失值，但如果您只想重新插入NA 值，您可以使用R.utils 中的?insert。可能有更好的方法来做到这一点。

install.packages(c('R.utils', 'signal'))
require(R.utils)
require(signal)
t <- seq(0, 1, len = 100)                     
set.seed(7337)
x <- c(sin(2*pi*t*2.3) + 0.25*rnorm(length(t)), NA, NA, cos(2*pi*t*2.3) + 0.25*rnorm(length(t)), NA)
cf1 = cheby1(5, 3, 1/44, type = "low")
xex <- na.omit(x)
z <- filter(cf1, xex)   # apply
z <- as.numeric(z)
for (m in attributes(xex)$na.action) {
  z <- insert(z, ats = m, values = NA)
}
all.equal(is.na(z), is.na(x))
?insert

Answer 4

这是一个函数，您可以使用它来过滤带有 NA 的信号。 NA 被忽略而不是被零替换。

然后，您可以指定 NA 在滤波信号的任何点上可以采用的最大权重百分比。如果某个特定点的 NA 太多（而实际数据太少），则滤波后的信号本身将设置为 NA。

# This function applies a filter to a time series with potentially missing data 

filter_with_NA <- function(x,
                           window_length=12,                            # will be applied centrally
                           myfilter=rep(1/window_length,window_length), # a boxcar filter by default
                           max_percentage_NA=25)                        # which percentage of weight created by NA should not be exceeded
{
  # make the signal longer at both sides
  signal <- c(rep(NA,window_length),x,rep(NA,window_length))
  # see where data are present and not NA
  present <- is.finite(signal)

  # replace the NA values by zero
  signal[!is.finite(signal)] <- 0
  # apply the filter
  filtered_signal <- as.numeric(filter(signal,myfilter, sides=2))

  # find out which percentage of the filtered signal was created by non-NA values
  # this is easy because the filter is linear
  original_weight <- as.numeric(filter(present,myfilter, sides=2))
  # where this is lower than one, the signal is now artificially smaller 
  # because we added zeros - compensate that
  filtered_signal <- filtered_signal / original_weight
  # but where there are too few values present, discard the signal
  filtered_signal[100*(1-original_weight) > max_percentage_NA] <- NA

  # cut away the padding to left and right which we previously inserted
  filtered_signal <- filtered_signal[((window_length+1):(window_length+length(x)))]
  return(filtered_signal)
}