testing methods for monotonic interpolation

monotonic_interpolation_tests.R

library(lubridate)

# options for a truly monotonic case:
set.seed(1)
values    <- runif(100) %>% cumsum() %>% '*'(100) %>% round()
all_dates <- seq(dmy("01.01.2020"),today(), by = "days")
my_dates  <- sample(all_dates, size = 100, replace = FALSE) %>% sort()

plot(my_dates, main = "irregularly spaced dates")
sum(day(my_dates) == 1) # just 4 days are 1st of month

# linear interpolation:
(first_of_month <- all_dates[day(all_dates) == 1])
# could filter down to only dates contained within the range of my_dates

# this assumes the first value is entirely contained in its month,
# possibly not true.
approx(my_dates, values, xout = first_of_month)$y %>% c(0,.) %>% diff()
# If that's bad then remove the 0 part in front.

# or a monotonic spline. This one doesn't seem to like using Date class for x,
# but it works same if you coerce to integer
splinefun(as.integer(my_dates), values, method = "monoH.FC")(as.integer(first_of_month))


# Question: if we use a monotonic spline, what happens if the data aren't monotonic?

values2 <- values
values2[9:10] 
values2[10] <- 540 
values2[30]
values2[31:34] <- 1480
splinefun(as.integer(my_dates), values2, method = "monoH.FC")(as.integer(first_of_month))

# It doesn't seem to want to spit back negatives. Are we satisifed with this?