valid_followup: Check the data for a follow up experiment.

In llrs/experDesign: Design Experiments for Batches

Check the data for a follow up experiment.

Description

Sometimes some samples are collected and analyzed, later another batch of samples is analyzed. This function tries to detect if there are problems with the data or when the data is combined in a single analysis. To know specific problems with the data you need to use check_data()

Usage

valid_followup(
  old_data = NULL,
  new_data = NULL,
  all_data = NULL,
  omit = NULL,
  column = "batch"
)

Arguments

old_data, new_data	A data.frame with the old and new data respectively.
all_data	A data.frame with all the data about the samples. Each row is a sample.
omit	Name of the columns of the pheno that will be omitted.
column	The name of the column where the old data has the batch information, or whether the data is new or not (NA) in the case of all_data.

Value

Called by its side effects of warnings, but returns a logical value if there are some issues (FALSE) or not (TRUE)

See Also

check_data()

Examples

data(survey, package = "MASS")
survey1 <- survey[1:118, ]
survey2 <- survey[119:nrow(survey), ]
valid_followup(survey1, survey2)
survey$batch <- NA
survey$batch[1:118]  <- "old"
valid_followup(all_data = survey)

llrs/experDesign documentation built on Dec. 24, 2024, 8:22 a.m.