Interpolate and extrapolate concentrations along the half-life fit

Concentrations are given as concentrations, not log-concentrations. Take the natural log of conc if the log-concentration is wanted.

Usage

get_halflife_curve(
  object,
  tout = NULL,
  n = 50,
  extrapolate_earlier = FALSE,
  extrapolate_later = TRUE
)

Arguments

object

A PKNCAresults or PKNCAdata object

tout

Times for output. The same times are used for every group and interval. If NULL (the default), n equally-spaced times spanning the concentrations used for the fit are used instead.

n

The number of equally-spaced times to generate when tout is NULL. It is ignored when tout is given.

extrapolate_earlier, extrapolate_later

Should concentrations be extrapolated before the first (extrapolate_earlier) or after the last (extrapolate_later) concentration used for the fit? Times outside the fit that are not extrapolated give an NA concentration.

Value

A data.frame with the grouping columns, the interval start and end times, and the columns:

• time: the time of the concentration, on the same scale as the times in the concentration data

• conc: the concentration on the half-life fit at that time

conc is NA where the half-life could not be calculated or was excluded, and where extrapolation was requested but not allowed. Groups without a fit give a single row with an NA time when tout is NULL, since no times can be generated for them.

Details

Like stats::approx(), give either tout for specific times or n for equally-spaced times. With n, the times span the concentrations used for the fit (time_first to time_last from get_halflife_fit()), so tout is required to extrapolate.

See also

get_halflife_fit() for the slope and intercept of the fit, and get_halflife_points() for the concentrations used for it

Examples

o_conc <- PKNCAconc(Theoph, conc~Time|Subject)
o_data <- PKNCAdata(o_conc, intervals = data.frame(start = 0, end = Inf, half.life = TRUE))
o_nca <- pk.nca(o_data)
# Equally-spaced times across the fit
head(get_halflife_curve(o_nca))
#>   Subject start end     time     conc
#> 1       6     0 Inf 2.030000 6.392843
#> 2       6     0 Inf 2.475306 6.147731
#> 3       6     0 Inf 2.920612 5.912017
#> 4       6     0 Inf 3.365918 5.685341
#> 5       6     0 Inf 3.811224 5.467356
#> 6       6     0 Inf 4.256531 5.257729
# Specific times, extrapolating past the last concentration used
get_halflife_curve(o_nca, tout = c(12, 24, 36))
#>    Subject start end time      conc
#> 1        6     0 Inf   12 2.6640713
#> 2        6     0 Inf   24 0.9289565
#> 3        6     0 Inf   36 0.3239253
#> 4        7     0 Inf   12 3.4161419
#> 5        7     0 Inf   24 1.1834973
#> 6        7     0 Inf   36 0.4100139
#> 7        8     0 Inf   12 3.2969449
#> 8        8     0 Inf   24 1.2405933
#> 9        8     0 Inf   36 0.4668176
#> 10      11     0 Inf   12 2.7239734
#> 11      11     0 Inf   24 0.8663978
#> 12      11     0 Inf   36 0.2755699
#> 13       3     0 Inf   12 3.6706903
#> 14       3     0 Inf   24 1.0736327
#> 15       3     0 Inf   36 0.3140246
#> 16       2     0 Inf   12 3.1969589
#> 17       2     0 Inf   24 0.9168262
#> 18       2     0 Inf   36 0.2629281
#> 19       4     0 Inf   12 4.0605207
#> 20       4     0 Inf   24 1.2335140
#> 21       4     0 Inf   36 0.3747196
#> 22       9     0 Inf   12 3.1116231
#> 23       9     0 Inf   24 1.1567807
#> 24       9     0 Inf   36 0.4300462
#> 25      12     0 Inf   12 4.4878763
#> 26      12     0 Inf   24 1.1951429
#> 27      12     0 Inf   36 0.3182722
#> 28      10     0 Inf   12 5.8019123
#> 29      10     0 Inf   24 2.3600191
#> 30      10     0 Inf   36 0.9599748
#> 31       1     0 Inf   12 5.9733095
#> 32       1     0 Inf   24 3.3394869
#> 33       1     0 Inf   36 1.8670006
#> 34       5     0 Inf   12 4.5342772
#> 35       5     0 Inf   24 1.6035807
#> 36       5     0 Inf   36 0.5671182