dplR (version 1.7.6)

fill.internal.NA: Fill Internal NA

Description

This function fills internal NA values (i.e., those with numeric data above and below a small data gap) in each column of a data.frame such as a data set of ring widths as produced by read.rwl.

Usage

fill.internal.NA(x, fill = c("Mean", "Spline", "Linear"))

Value

A data.frame with colnames(x) and

rownames(x). Internal NAs filled as above.

Arguments

x

a data.frame of ring widths with row.names(x) containing years and names(x) containing each series ID such as produced by read.rwl

fill

a numeric value (e.g., 0) or a character string of "Mean", "Spline", or "Linear". Defaults to "Mean".

Author

Andy Bunn. Patched and improved by Mikko Korpela.

Details

There are occasionally data gaps within a tree-ring series. Some of the functions in dplR will fail when an internal NA is encountered (e.g. caps). This function fills internal NA values with either a given numeric value (e.g., 0) or through crude imputation. The latter can be calculated as the mean of the series (fill="Mean") or calculated by fitting a cubic spline (fill="Spline") using the spline function or calculated by linear approximation (fill="Linear") using the function approx.

Editorial: Having internal NA in a tree-ring series is often bad practice and filling those values should be done with caution. For instance, some users code missing rings as NA instead of 0. And missing values (i.e., NA) are sometimes present in maximum latewood density data when the rings are small. A common, but not recommended, practice is to leave stretches of NA values in places where it has been impossible to accurately measure rings (perhaps because of a break in the core). It is often better to treat that core as two separate series (e.g., "01A" and "01B" rather than have internal NA values. As with all processing, the analyst should make a decision based on their experience with the wood and not rely on software to make a choice for them!

See Also

spline, approx

Examples

library(graphics)
library(stats)
foo <- data.frame(x1=c(rnorm(5), NA, NA, rnorm(3)),
                  x2=c(rnorm(10)),
                  x3=c(NA, NA, rnorm(3), NA, rnorm(4)),
                  x4=c(NA, NA, rnorm(3), NA, rnorm(3), NA),
                  x5=c(NA, NA, rnorm(8)),
                  x6=c(NA, rnorm(9)),
                  x7=c(NA, rnorm(5), NA, rnorm(3)),
                  x8=c(rnorm(8), NA, NA),
                  x9=c(rnorm(5), NA, rnorm(3), NA))
row.names(foo) <- 1901:1910
class(foo) <- c("rwl","data.frame")
fill.internal.NA(foo, fill=0)

bar <- fill.internal.NA(foo, fill="Spline")
baz <- fill.internal.NA(foo, fill="Linear")

## note differences in method "Spline" vs. "Linear"
yrs <- time(foo)
plot(yrs, foo$x7, type="b", lwd=3)
lines(yrs, bar$x7, col="red", lwd=2)
lines(yrs, baz$x7, col="green", lwd=1)