Clean and summarize the output of rcurvep object

Clean and summarize the output of rcurvep object

summarize_rcurvep_output(
  d,
  inactivate = NULL,
  ci_level = 0.95,
  clean_only = FALSE
)

Arguments

d

The rcurvep object from combi_run_rcurvep() and run_rcurvep().
inactivate

A character string, default = NULL, to make the curve with this string in the Comments column as inactive. or a vector of index for the rows in the act_set that needs to be inactive
ci_level

Default = 0.95 (95 percent of confidence interval).
clean_only

Default = FALSE, only the 1st, 2nd task will be performed (see Details).

Value

A list of named components: result and config (and act_summary). The result and config are the copy of the input d (but with modifications if inactivate is not NULL). If (clean_only = FALSE), an act_summary is added.

Suffix meaning in column names in act_summary: med (median), cil (lower end confidence interval), ciu (higher end confidence interval) Often used columns in act_summary: n_curves (number of curves used in summary), hit_confidence (fraction of active in n_curves)

Details

The function can perform the following tasks:

  1. add an column, hit, in the act_set

  2. unhit (make result as inactive) if the Comments column contains a certain string

  3. summarize the results

The curve is considered as "hit" if its responses are monotonic after processing by Curvep. However, often, if the curve is "INVERSE" (yet monotonic) is not considered as an active curve. By using the information in the Comments column, we can "unhit" these cases.

When (clean_only = FALSE, default), a tibble, act_summary is generated with confidence intervals of the activity metrics. The quantile approach is used to calculate the confidence interval. For potency activity metrics, if value is NA, highest tested concentration is used in the summary. For other activity metrics, if value is NA, 0 is used in the summary.

See also

combi_run_rcurvep(), run_rcurvep()

Examples


data(zfishbeh)

# original datasets
out <- combi_run_rcurvep(zfishbeh, n_samples = NULL, TRSH = c(5, 10))
out_res <- summarize_rcurvep_output(out)

# \donttest{
# unhit when comment has "INVERSE"
out <- summarize_rcurvep_output(out, inactivate = "INVERSE")

# unhit for certain rows in act_set
out <- summarize_rcurvep_output(out, inactivate = c(2,3))

# simulated datasets
out <- combi_run_rcurvep(zfishbeh, n_samples = 3, TRSH = c(5, 10))
out_res <- summarize_rcurvep_output(out)
# }