Add specified imputation methods to the intervals in a PKNCAdata or data.frame object.

Usage

interval_add_impute(
  data,
  target_impute,
  after,
  target_params,
  target_groups,
  ...
)

Arguments

data: A PKNCAdata object containing the intervals data frame, or a data frame of intervals.
target_impute: A character string specifying the imputation method to be added.
after: Numeric value specifying the index in which the imputation will be added (optional). First is 0, last Inf. If missing, the imputation method is added at the end (Inf).
target_params: A character vector specifying the parameters to be targeted (optional). If missing, all TRUE in the intervals are taken.
target_groups: A data frame specifying the intervals to be targeted (optional). If missing, all relevant groups are considered.
...: arguments passed to interval_add_impute.

Value

A modified PKNCAdata object with specified imputation methods on the target intervals.

Details

If already present the target_impute method will be added substituting the existing one. All new intervals created will be added right after their original ones.

Examples

d_conc <- data.frame(
  conc = c(1, 0.6, 0.2, 0.1, 0.9, 0.4, 1.2, 0.8, 0.3, 0.2, 1.1, 0.5),
  time = rep(0:5, 2),
  ID = rep(1:2, each = 6),
  analyte = rep(c("Analyte1", "Analyte2"), each = 6)
)

d_dose <- data.frame(
  dose = c(100, 200),
  time = c(0, 0),
  ID = c(1, 2)
)

o_conc <- PKNCA::PKNCAconc(d_conc, conc ~ time | ID / analyte)
o_dose <- PKNCA::PKNCAdose(d_dose, dose ~ time | ID)

intervals <- data.frame(
  start = c(0, 0, 0),
  end = c(3, 5, Inf),
  half.life = c(TRUE, TRUE, TRUE),
  cmax = c(TRUE, TRUE, TRUE),
  impute = c("start_conc0,start_predose", "start_predose", "start_conc0"),
  analyte = c("Analyte1", "Analyte2", "Analyte1")
)

o_data <- PKNCA::PKNCAdata(o_conc, o_dose, intervals = intervals)

# Apply interval_add_impute function
o_data <- interval_add_impute(o_data,
                              target_impute = "start_conc0",
                              target_params = "half.life",
                              target_groups = data.frame(analyte = "Analyte1"))