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Create a new series with all values advanced forward one period.
The value of period 1, becomes the value at period 2,
value at 2 becomes the original value at 3, etc. The opposite
of Lag
. NA
is used to fill.
Next(x, k = 1)
## S3 method for class 'quantmod.OHLC'
Next(x,k=1)
## S3 method for class 'zoo'
Next(x,k=1)
## S3 method for class 'data.frame'
Next(x,k=1)
## S3 method for class 'numeric'
Next(x,k=1)
x |
vector or series to be advanced |
k |
periods to advance |
Shift series k-periods up, appending NA
s to end of series.
Specifically designed to handle quantmod.OHLC
and
zoo
series within the quantmod workflow.
If no S3 method is found, a call to lag
in base is made,
with the indexing reversed to shift the time series forward.
The original x
appended with k
NA
s and
missing the leading k
values.
The returned series maintains the number of obs. of the original.
Unlike Lag
, only one value for k
is allowed.
This function's purpose is to get the “next” value of
the data you hope to forecast, e.g. a stock's closing value
at t+1. Specifically to be used within
the quantmod framework of specifyModel
, as a
functional wrapper to the LHS of the model equation.
It is not magic - and thus will not get tomorrow's values...
Jeffrey A. Ryan
specifyModel
, Lag
Stock.Close <- c(102.12,102.62,100.12,103.00,103.87,103.12,105.12)
Close.Dates <- as.Date(c(10660,10661,10662,10665,10666,10667,10668),origin="1970-01-01")
Stock.Close <- zoo(Stock.Close,Close.Dates)
Next(Stock.Close) #one period ahead
Next(Stock.Close,k=1) #same
merge(Next(Stock.Close),Stock.Close)
## Not run:
# a simple way to build a model of next days
# IBM close, given todays. Technically both
# methods are equal, though the former is seen
# as more intuitive...ymmv
specifyModel(Next(Cl(IBM)) ~ Cl(IBM))
specifyModel(Cl(IBM) ~ Lag(Cl(IBM)))
## End(Not run)
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