The example in
vignette("piar") used parental imputation to both impute
missing price relatives when calculating the elementary indexes and to
impute missing elementary indexes during aggregation. Although parental
imputation is simple and transparent, it is not the only way to impute
missing prices or index values.
Instead of implicitly imputing missing price relatives by ignoring
missing values, a common explicit (but methodologically dubious)
imputation strategy when making elementary indexes is to carry forward
the previous price to impute for missing prices. As the
elementary_index() function accepts price relatives as its
input, any imputations can be done prior to passing price relatives to
this function. (Missing values still need to be removed in this example
because not all missing prices can be imputed.)
library(piar)
elementals <- ms_prices |>
transform(
imputed_price = impute_prices(
price,
period = period,
product = product,
method = "carry-forward"
)
) |>
elementary_index(
price_relative(imputed_price, period = period, product = product) ~
period + business,
na.rm = TRUE
)
elementals## Period-over-period price index for 4 levels over 4 time periods
## time
## levels 202001 202002 202003 202004
## B1 1 0.8949097 0.5781816 1.000000
## B2 1 1.0000000 0.1777227 2.770456
## B3 1 2.0200036 1.6353355 0.537996
## B4 NaN NaN NaN 4.576286Parental imputation is the usual way to impute missing elementary
index values during aggregation, and it is simple to do with
aggregate(). In some cases, however, an elementary index
may get imputed with the value for, say, another elementary aggregate,
rather than for an entire group of elementary aggregates. The simplest
way to do this sort of imputation is to alter the elementary indexes
prior to aggregation.
As an example, suppose that missing index values for business B4 should be imputed as 1, rather than the value for group 12. This replacement can be done as if the index was a matrix.
## Period-over-period price index for 4 levels over 4 time periods
## time
## levels 202001 202002 202003 202004
## B1 1 0.8949097 0.5781816 1.000000
## B2 1 1.0000000 0.1777227 2.770456
## B3 1 2.0200036 1.6353355 0.537996
## B4 1 1.0000000 1.0000000 4.576286Aggregating these elementary indexes now incorporates this imputation.
ms_weights[c("level1", "level2")] <-
expand_classification(ms_weights$classification)
pias <- ms_weights[c("level1", "level2", "business", "weight")] |>
as_aggregation_structure()
aggregate(elementals2, pias, na.rm = TRUE)## Period-over-period price index for 8 levels over 4 time periods
## time
## levels 202001 202002 202003 202004
## 1 1 1.1056136 0.8753168 2.3138631
## 11 1 1.1721550 0.8082981 0.8093718
## 12 1 1.0000000 1.0000000 4.5762862
## B1 1 0.8949097 0.5781816 1.0000000
## B2 1 1.0000000 0.1777227 2.7704563
## B3 1 2.0200036 1.6353355 0.5379960
## B4 1 1.0000000 1.0000000 4.5762862
## B5 1 1.0000000 1.0000000 4.5762862It is also possible to supply a function with these rules that can be
used by aggregate().
impute <- function(x, pias) {
if (is.na(x["B4"])) x["B4"] <- 1
x
}
aggregate(elementals, pias, na.rm = TRUE, impute_rules = impute)## Period-over-period price index for 8 levels over 4 time periods
## time
## levels 202001 202002 202003 202004
## 1 1 1.1056136 0.8753168 2.3138631
## 11 1 1.1721550 0.8082981 0.8093718
## 12 1 1.0000000 1.0000000 4.5762862
## B1 1 0.8949097 0.5781816 1.0000000
## B2 1 1.0000000 0.1777227 2.7704563
## B3 1 2.0200036 1.6353355 0.5379960
## B4 1 1.0000000 1.0000000 4.5762862
## B5 1 1.0000000 1.0000000 4.5762862