Description Usage Arguments Details Value Examples
This function uses data (sample ages and sample counts) from an
epd.entity.dfclass
object to estimate by linear
interpolation, loess or smooth splines the counts at specific time
periods defined by the user. This can be used to estimate counts for
the same time periods for multiple entities in the database,
standardizing them for integrative analysis.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  interpolate_counts(x, time, chronology = NULL, method = c("linear", "loess",
"sspline"), rep_negt = TRUE, span = 0.25, df = min(20,
nrow(x@commdf@counts) * 0.7), ...)
## S4 method for signature 'epd.entity.df,numeric'
interpolate_counts(x, time,
chronology = NULL, method = c("linear", "loess", "sspline"),
rep_negt = TRUE, span = 0.25, df = min(20, nrow(x@commdf@counts) * 0.7),
...)
## S4 method for signature 'epd.entity,numeric'
interpolate_counts(x, time, chronology = NULL,
method = c("linear", "loess", "sspline"), rep_negt = TRUE, span = 0.25,
df = min(20, nrow(x@commdf@counts) * 0.7), ...)

x 
epd.entity.df An 
time 
numeric Vector with time periods, in the same system (i.e., cal BP) than "ages" in epd.entity.df, in which counts have to be estimated. 
chronology 
numeric Number specifying the chronology from
which ages should be used to calculate the interpolations. If none is
provided the function uses the default chronology from the object (see

method 
character Interpolation method, should be an unambiguous abbreviation of either linear, loess, or sspline. See Details section. 
rep_negt 
logical logical to indicate whether or not to replace negative values with zero in the interpolated data. 
span 
numeric Span for loess, default = 0.25. 
df 
numeric Degress of freedome for smoothing spline, default is the lower of 20 or 0.7 * number of samples. 
... 
additional arguments to loess and smooth.spline. 
Data for time periods in time
but not recorded in the
entity are fill with NA
. This is convenient if analysis are
carried out with multiple entities.
Interpolation can be done using linear interpolation between
data points in the original series (default) using
approxfun
, using a fitted
loess
locally weighted regression, or by
smooth.spline
. The latter two methods
will also smooth the data and additional arguments may be passed to
these functions to control the amount of smoothing, or to force replacing
negative values with zeros.
The function returns an epd.entity.dfclass
object,
similar to x
in which ages and counts has been modified to the
time periods specified in time and the counts estimated for these periods.
1 2 3 4 5 6 7 8 9 10 11 12 13  ## Not run:
epd.connection < connect_to_epd(host="localhost", database="epd",
user="epdr", password="epdrpw")
t < c(seq(0, 21000, by = 500))
epd.1 < get_entity(1, epd.connection)
epd.1.int < interpolate_counts(epd.1, t)
epd.3 < get_entity(3, epd.connection)
epd.3.int < interpolate_counts(epd.3, t, method="linear")
epd.3.int < interpolate_counts(epd.3, t, method="loess")
epd.3.int < interpolate_counts(epd.3, t, method="sspline")
## End(Not run)

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