Question

我正在尝试使用Chebyshev过滤器来平滑时间序列，但不幸的是，数据系列中有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Chebyshev过滤器：cf1 = cheby1(5, 3, 1/44, type = "low")

我正在尝试过滤排除NAs的时间序列，但不会搞砸订单/位置。所以，我已经尝试了na.rm=T，但似乎没有这样的论点。然后

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功能预先删除NA。您也可以考虑输入缺失的数据。查看mtsdi或Amelia II软件包。

修改

这是Rcpp的解决方案。这可能有用，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对象是否可以包含缺失值，但如果您只想重新插入NA值，则可以使用?insert中的R.utils }。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

这是一个可用于过滤带有NA的信号的函数。 NAs被忽略而不是被零替换。

然后，您可以指定NA在过滤后的信号的任何点可以采取的最大权重百分比。如果特定点的NA（实际数据太少）过多，则过滤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)
}

R filter（）处理NA

4 个答案: