knitr::opts_chunk$set( collapse = TRUE, comment = "#" )
library(bootf2)
To use traditional $f_2$ method, several conditions defined in the regulatory guidelines have to be fulfilled. Different guidelines phrase the conditions differently, which led to confusion some times. The details were explained in the vignette Introduction to bootf2.
This document gives some examples of the usage of the function calcf2(). 
The complete list of arguments of the function is as follows:
calcf2(test, ref, path.in, file.in, path.out, file.out, regulation = c("EMA", "FDA", "WHO", "Canada", "ANVISA"), cv.rule = TRUE, message = FALSE, min.points = 3L, f2.type = c("est.f2", "exp.f2", "bc.f2", "vc.exp.f2", "vc.bc.f2", "all"), both.TR.85 = FALSE, digits = 2L, time.unit = c("min", "h"), plot = TRUE, plot.start.time = 0, plot.max.unit = 24L)
test, ref, path.in, file.intest and ref are data frames with
  the time as the first column, and individual units for the rest of 
  columns. In such cases, arguments path.in and file.in should not 
  be used..xlsx or
  .xls. In this case, data of test and reference should be stored in
  separate worksheets. The first column should be time, the rest columns 
  are individual units. The first row is the column head indicating the
  names, such as 'time', 'unit 01', unit 02', .... It doesn't matter what
  the names are as columns will be renamed internally by the function. The
  important point is that the first row will not be read, so do not put
  dissolution data on the first row. path.in and file.in are provided, the argument test and
  ref should be the worksheet names inside quotation mark, e.g., 
  "lot ABCD1234 pH 6.8". path.in can be an absolute path such as"/home/myname/my.project/dat/",
  or a relative path such as "../dat/" if the working directory is in the
  folder "/home/myname/my.project/analysis/" and the data file is in the
  folder "/home/myname/my.project/dat/". "C:\user\myname\my.project\dat\" cannot be the path.in, 
  you have to changed it to "C:\\user\\myname\\my.project\\dat\\", or
  to "C:/user/myname/my.project/dat/", the same format as used in Linux
  system.path.out and file.out are
   really overkill. By default the result will be printed to screen. But if 
   somehow you feel that you need it, the same principle for path.in and 
   file.in applies here.cv.rule is set to TRUE, and if argument message is also 
   TRUE, additional message will be printed to screen. One particular note is
   the rounding of CV before checking compliance. Since the guidelines does
   not specify the precision for CV, when checking if CV condition defined in
   the guidelines is fulfilled, the calculated CV values are rounded with
   digits decimal first before they were compared to the 20%/10% criterion.
   And refer to vignette Introduction to bootf2 for details of regulation
   rules. "EMA" and "FDA" 
   are the same. See vignette Introduction to bootf2 for details. The reason
   both of them are included in regulation argument is because they have
   different rules for CV.both.TR.85 = TRUE, and regulation = FDA, then the
   function will calculate $f_2$ using all time points until both test 
   and reference dissolve more than 85%. This is the wrong interpretation
   of the guidelines, as explained in the vignette Introduction to bootf2. 
   The purpose of this argument is for historical reason, in case users want
   to check the calculation published in the old articles where the wrong
   interpretation was used, or for validation purpose. It should be set to
   FALSE for the daily analysis.f2.type.
   This argument is only for bootstrap method. The default est.f2 is the
   correct one to use. See vignette Confidence Intervals of $f_2$ Using
   Bootstrap Method for details.plot.max.unit control how individual profile will be 
   represented in the plot. When the actual number of units is greater than 
   the value of plot.max.unit, the individual profile will be represented 
   as boxplots at each time points. plot.start.time = 120 (or plot.start.time = 2 if the time
   unit is "h") for example, can make plots more readable.help("calcf2") for more details on
   each argument.First, let's simulate profiles with low variability for reference and test.
# time points tp <- c(5, 10, 15, 20, 30, 45, 60) # model.par for reference with low variability par.r1.lv <- list(fmax = 100, fmax.cv = 3, mdt = 15, mdt.cv = 13, tlag = 0, tlag.cv = 0, beta = 1.5, beta.cv = 7) # simulate reference data dr1.lv <- sim.dp(tp, model.par = par.r1.lv, seed = 100, plot = FALSE, message = TRUE) # model.par for test par.t1.lv <- list(fmax = 100, fmax.cv = 3, mdt = 12.29, mdt.cv = 10, tlag = 0, tlag.cv = 0, beta = 1.727, beta.cv = 8) # simulate test data with low variability dt1.lv <- sim.dp(tp, model.par = par.t1.lv, seed = 100, plot = FALSE, message = TRUE)
Calculate f2 with default setting (following EMA's guideline)
t_vs_r_ema <- calcf2(dt1.lv$sim.disso, dr1.lv$sim.disso, message = TRUE)
calculate f2 following WHO guideline
t_vs_r_who <- calcf2(dt1.lv$sim.disso, dr1.lv$sim.disso, regulation = "WHO", message = TRUE)
Calculate f2 following FDA guidance, to confirm "old calculation" with wrong interpretation. See vignette Introduction to bootf2 for details.
t_vs_r <- calcf2(dt1.lv$sim.disso, dr1.lv$sim.disso, regulation = "FDA", both.TR.85 = TRUE, message = TRUE, plot = FALSE)
Simulate profiles with the same population parameters but with higher variability.
# model.par for reference with high variability par.r1.hv <- list(fmax = 100, fmax.cv = 3, mdt = 15, mdt.cv = 20, tlag = 0, tlag.cv = 0, beta = 1.5, beta.cv = 10) # simulate reference data dr1.hv <- sim.dp(tp, model.par = par.r1.hv, seed = 100, plot = FALSE) # model.par for test par.t1.hv <- list(fmax = 100, fmax.cv = 3, mdt = 12.29, mdt.cv = 15, tlag = 0, tlag.cv = 0, beta = 1.727, beta.cv = 12) # simulate test data with low variability dt1.hv <- sim.dp(tp, model.par = par.t1.hv, seed = 100, plot = FALSE)
When CV conditions are not fulfilled, the function will stop running.
calcf2(dt1.hv$sim.disso, dr1.hv$sim.disso, message = TRUE)
There might be a time that you just want to check the $f_2$ regardless of the
variability, option cv.rule can be set to FALSE in such case.
dt1.hv_vs_dr1.hv <- calcf2(dt1.hv$sim.disso, dr1.hv$sim.disso, cv.rule = FALSE, message = TRUE)
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