Extract a matrix or data frame of percent-change contributions from a price index.
Usage
contrib(x, ...)
# S3 method for class 'piar_index'
contrib(x, level = levels(x)[1L], period = time(x), ..., pad = 0)
contrib2DF(x, ...)
# S3 method for class 'piar_index'
contrib2DF(x, level = levels(x)[1L], period = time(x), ...)
contrib(x, ...) <- value
# S3 method for class 'piar_index'
contrib(x, level = levels(x)[1L], period = time(x), ...) <- value
set_contrib(x, ..., value)Arguments
- x
A price index, as made by, e.g.,
elemental_index().- ...
Further arguments passed to or used by methods.
- level
The level of an index for which percent-change contributions are desired, defaulting to the first level (usually the top-level for an aggregate index).
contrib2DF()can accept multiple levels.- period
The time periods for which percent-change contributions are desired, defaulting to all time periods.
- pad
A numeric value to pad contributions so that they fit into a rectangular array when products differ over time. The default is 0.
- value
A numeric matrix of replacement contributions with a row for each product and a column for each time period. Recycling occurs along time periods.
Value
contrib() returns a matrix of percent-change contributions with a column
for each period and a row for each product (sorted) for which there are
contributions in level. Contributions are padded with pad to fit into a
rectangular array when products differ over time. The replacement methods
returns a copy of x with contributions given by the matrix value.
(set_contrib() is an alias that's easier to use with pipes.)
contrib2DF() returns a data frame of contributions with four
columns: period, level, product, and value.
See also
Other index methods:
[.piar_index(),
aggregate.piar_index,
as.data.frame.piar_index(),
chain(),
head.piar_index(),
is.na.piar_index(),
levels.piar_index(),
mean.piar_index,
merge.piar_index(),
split.piar_index(),
stack.piar_index(),
time.piar_index(),
window.piar_index()
Examples
prices <- data.frame(
rel = 1:8,
period = rep(1:2, each = 4),
ea = rep(letters[1:2], 4)
)
index <- elemental_index(prices, rel ~ period + ea, contrib = TRUE)
pias <- aggregation_structure(
list(c("top", "top", "top"), c("a", "b", "c")), weights = 1:3
)
index <- aggregate(index, pias, na.rm = TRUE)
# Percent-change contributions for the top-level index
contrib(index)
#> 1 2
#> a.1 0.0000000 0.5081686
#> a.2 0.2440169 0.6442213
#> b.1 0.3905243 2.0513858
#> b.2 0.8284271 2.4871732
contrib2DF(index)
#> period level product value
#> 1 1 top a.1 0.0000000
#> 2 1 top a.2 0.2440169
#> 3 1 top b.1 0.3905243
#> 4 1 top b.2 0.8284271
#> 5 2 top a.1 0.5081686
#> 6 2 top a.2 0.6442213
#> 7 2 top b.1 2.0513858
#> 8 2 top b.2 2.4871732
# Calculate EA contributions for the chained index
library(gpindex)
arithmetic_contributions(
as.matrix(chain(index))[c("a", "b", "c"), 2],
weights(pias)
)
#> a b c
#> 1.541158 6.198639 7.739798