There is something I do not think is right in the approx() function in base R,
with method="constant" and in the presence of NA values. I have 3.6.0, but the
behavior seems to be the same in earlier versions.
My suggested fix is to add an "na.rm" argument to approx(), as in mean(). If
this argument is FALSE, then NA values should be propagated into the output
rather than being removed.
Details:
The documentation says
"f: for method = "constant" a number between 0 and 1 inclusive, indicating a
compromise between left- and right-continuous step functions. If y0 and y1 are
the values to the left and right of the point then the value is y0 if f == 0,
y1 if f == 1, and y0*(1-f)+y1*f for intermediate values. In this way the result
is right-continuous for f == 0 and left-continuous for f == 1, even for
non-finite y values."
This suggests to me that if the left value y0 is NA, and if f=0 (the default),
then the interpolated value should be NA. (Regardless of the right value y1,
see bug 15655 fixed in 2014.)
The documentation further says, below under "Details", that
"The inputs can contain missing values which are deleted."
The question is what is the appropriate behavior if one of the input values y
is NA. Currently, approx() seems to interpret NA values as faulty data points,
which should be deleted and the previous values carried forward (example below).
But in many applications, especially with "constant" interpolation, an NA value
is intended to mean that we really do not know the value in the next interval,
or explicitly that there is no value. Therefore the NA should not be removed,
but should be propagated forward into the output within the corresponding
interval.
The situation is similar with functions like mean(). The presence of an NA
value may mean either (a) we want to compute the mean without that value
(na.rm=TRUE), or (b) we really are missing important information, we cannot
determine the mean, and we should return NA (na.rm=FALSE).
Therefore, I propose that approx() also be given an na.rm argument, indicating
whether we wish to delete NA values, or treat them as actual values on the
corresponding interval. That option makes even more sense for approx() than for
mean(), since the NA values apply only on small regions of the data range.
--Robert Almgren
Example:
: R --vanilla
R version 3.6.0 (2019-04-26) -- "Planting of a Tree"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)
...
> t1 <- 1:5
> x1 <- c( 1, as.numeric(NA), 3, as.numeric(NA), 5 )
> print(data.frame(t1,x1))
t1 x1
1 1 1
2 2 NA <-- we do not know the value between t=2 and t=3
3 3 3
4 4 NA <-- we do not know the value between t=4 and t=5
5 5 5
> X <- approx( t1, x1, (1:4) + 0.5, method='constant', rule=c(1,2) )
> print(data.frame(X))
x y
1 1.5 1
2 2.5 1 <---- I believe that these two values should be NA
3 3.5 3
4 4.5 3 <---- I believe that these two values should be NA
--
Quantitative Brokers http://www.quantitativebrokers.com
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