bootstrap.tcr: Bootstrap for data frames in package tcR.
In imminfo/tcr: Advanced Data Analysis of Immune Receptor Repertoires

Description Usage Arguments Details Value Examples

Resample rows (i.e., clones) in the given data frame and apply the given function to them.

bootstrap.tcr(
  .data,
  .fun = entropy.seg,
  .n = 1000,
  .size = nrow(.data),
  .sim = c("uniform", "percentage"),
  .postfun = function(x) {     unlist(x) },
  .verbose = T,
  .prop.col = "Read.proportion",
  ...
)

`.data`	Data frame.
`.fun`	Function applied to each sample.
`.n`	Number of iterations (i.e., size of a resulting distribution).
`.size`	Size of samples. For `.sim` == "uniform" stands for number of rows to take. For `.sim` == "percentage" stands for number of UMIs / read counts to take.
`.sim`	A character string indicating the type of simulation required. Possible values are "uniform" or "percentage". See "Details" for more details of type of simulation.
`.postfun`	Function applied to the resulting list: list of results from each processed sample.
`.verbose`	if T then show progress bar.
`.prop.col`	Column with proportions for each clonotype.
`...`	Further values passed to `.fun`.

Argument .sim can take two possible values: "uniform" (for uniform distribution), when each row can be taken with equal probability, and "perccentage" when each row can be taken with probability equal to its "Read.proportion" column.

Either result from .postfun or list of length .n with values of .fun.

## Not run: 
# Apply entropy.seg function to samples of size 20000 from immdata$B data frame for 100 iterations.
bootstrap.tcr(immdata[[2]], .fun = entropy.seg, .n = 100, .size = 20000, .sim = 'uniform')

## End(Not run)