在不使用 Hmisc 的情况下在 R 中进行外推

Question

上一个问题的答案：extrapolate in R for a time-series data 由于 R 版本不适合我。

我有一个数据框 NEI_othertier1_long，看起来类似于：

state    pollutant    Sector       Fuel      description    year     value
AK       Ammonia      Refining     Diesel     industrial    2008      1.18
AK       Ammonia      Refining     Diesel     industrial    2009      NA
AK       Ammonia      Refining     Diesel     industrial    2010      NA
AK       Ammonia      Refining     Diesel     industrial    2011      5.76
AK       Ammonia      Refining     Diesel     industrial    2012      NA
AK       Ammonia      Refining     Diesel     industrial    2013      NA
AK       Ammonia      Refining     Diesel     industrial    2014      5.83
AK       Ammonia      Refining     Diesel     industrial    2015      NA
AK       Ammonia      Refining     Diesel     industrial    2016      NA
AK       Ammonia      Refining     Diesel     industrial    2017      8.96
AK       Ammonia      Refining     Diesel     industrial    2018      NA
AK       Ammonia      Refining     Diesel     industrial    2019      NA

我有 2008 年、2011 年、2014 年和 2017 年的值。我已经能够使用以下代码成功地对 2009-2016 年进行线性插值：

    NEI_othertier1_long %>%
  dplyr::mutate( value = na.approx(value, na.rm = FALSE, rule = 2) ) -> NEI_othertier1_interpolated

但是插值将 2017 年的值向前推到 2018 年和 2019 年。我想线性推断前几年的 2018 年和 2019 年的值。

我有 R 版本 3.5.2（无法更新），所以无法安装 latticeExtra，Hmisc 依赖于使用 approxExtrap 函数。

感谢任何帮助！

dput(head(NEI_othertier1_long)) structure(list(state = c("AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK", "AK"), pollutant = c("Ammonia", "Ammonia", "Ammonia", "Ammonia", "Ammonia", "Ammonia", "Ammonia", "Ammonia", "Ammonia", "Ammonia", "Ammonia", "Ammonia"), CEDS_Sector = c("1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining", "1A1b_Pet-refining"), CEDS_Fuel = c("diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil", "diesel_oil" ), tier1_description = c("FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL", "FUEL COMB. INDUSTRIAL"), unit = c("TON", "TON", "TON", "TON", "TON", "TON", "TON", "TON", "TON", "TON", "TON", "TON"), year = 2008:2019, emissions = c(1.18, NA, NA, 5.76, NA, NA, 5.83, NA, NA, 8.96, NA, NA)), row.names = c(NA, -12L), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), groups = structure(list(state = "AK", pollutant = "Ammonia", CEDS_Sector = "1A1b_Pet-refining", CEDS_Fuel = "diesel_oil", tier1_description = "FUEL COMB. INDUSTRIAL", unit = "TON", .rows = list(1:12)), row.names = c(NA, -1L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))

Answer 1

approxExtrap 只是 approx 的包装器，因此您只需复制函数定义并使用它即可。

NEI_othertier1_long %>% dplyr::mutate(x = approxExtrap(year, value, year, na.rm = TRUE)$y)

如果找不到，这里是approxExtrap：

approxExtrap <- function (x, y, xout, method = "linear", n = 50, rule = 2, f = 0, 
  ties = "ordered", na.rm = FALSE) 
{
  if (is.list(x)) {
    y <- x[[2]]
    x <- x[[1]]
  }
  if (na.rm) {
    d <- !is.na(x + y)
    x <- x[d]
    y <- y[d]
  }
  d <- !duplicated(x)
  x <- x[d]
  y <- y[d]
  d <- order(x)
  x <- x[d]
  y <- y[d]
  w <- approx(x, y, xout = xout, method = method, n = n, rule = 2, 
    f = f, ties = ties)$y
  r <- range(x)
  d <- xout < r[1]
  if (any(is.na(d))) 
    stop("NAs not allowed in xout")
  if (any(d)) 
    w[d] <- (y[2] - y[1])/(x[2] - x[1]) * (xout[d] - x[1]) + 
    y[1]
  d <- xout > r[2]
  n <- length(y)
  if (any(d)) 
    w[d] <- (y[n] - y[n - 1])/(x[n] - x[n - 1]) * (xout[d] - 
        x[n - 1]) + y[n - 1]
  list(x = xout, y = w)
}

在不使用 Hmisc 的情况下在 R 中进行外推

Extrapolating in R without using Hmisc

r

interpolation

data-processing

extrapolation