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Identify missing values in a price index.
# S3 method for piar_index is.na(x)# S3 method for piar_index anyNA(x, recursive = FALSE)
# S3 method for piar_index anyNA(x, recursive = FALSE)
is.na() returns a logical matrix, with a row for each level of x and a columns for each time period, that indicates which index values are missing.
is.na()
x
anyNA() returns TRUE if any index values are missing, or percent-change contributions (if recursive = TRUE).
anyNA()
TRUE
recursive = TRUE
A price index, as made by, e.g., elemental_index().
elemental_index()
Check if x also has missing percent-change contributions. By default only index values are checked for missingness.
Other index methods: [.piar_index(), aggregate.piar_index, as.data.frame.piar_index(), as.ts.piar_index(), chain(), contrib(), head.piar_index(), levels.piar_index(), mean.piar_index, merge.piar_index(), split.piar_index(), stack.piar_index(), time.piar_index(), window.piar_index()
[.piar_index()
aggregate.piar_index
as.data.frame.piar_index()
as.ts.piar_index()
chain()
contrib()
head.piar_index()
levels.piar_index()
mean.piar_index
merge.piar_index()
split.piar_index()
stack.piar_index()
time.piar_index()
window.piar_index()
index <- as_index(matrix(c(1, 2, 3, NA, 5, NA), 2)) anyNA(index) is.na(index) # Carry forward imputation index[is.na(index)] <- 1 index
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