View source: R/tcplfit_onerun.R
summarize_fit_output | R Documentation |
The function first extracts the activity data based on the fit the supplied input parameters. In addition, summary of activity data (e.g., confidence interval, hit confidence) can be produced.
summarize_fit_output(
d,
thr_resp = 20,
perc_resp = 10,
ci_level = 0.95,
extract_only = FALSE
)
d |
The output from the |
thr_resp |
The response cutoff to calculate the potency. Default = 20 (POD20) |
perc_resp |
The percentage cutoff to calculate the potency. Default = 10 (EC10). |
ci_level |
The confidence level for the activity metrics. Default is = 0.95. |
extract_only |
Whether act_summary data should be produced. Default = FALSE. |
A tibble, act_set is generated. When (extract_only = FALSE), a tibble, act_summary is generated with confidence intervals of the activity metrics. The quantile approach is used to calculate the confidence interval. Currently only bootstrap calculations from hill (3-parameter) can generate 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.
A list of named components: result and result_nested (and act_summary).
The result and result_nested are the copy from the output of run_fit()
.
An act_set is added under the result component.
If (extract_only = FALSE), an act_summary is added.
If the cnst is the winning model and the median of responses larger than the thr_resp, it is considered as an hit. The median of responses is reported as Emax and the lowest tested concentration is reported as EC50, POD, ECxx.
The hit (=1) is considered having POD < max tested concentration.
The hit value is from the cc2 value
output |- result (list) | |- fit_set (tibble, all output from the respective fit model included) | |- resp_set (tibble) | |- act_set (tibble, EC50, ECxx, Emax, POD, slope, hit) | |- result_nested (tibble) |- act_summary (tibble, confidence interval)
hit call, see above definition
half maximal effect concentration
effect concentration at XX percent, depending on the perc_resp
point-of-departure, depending on the thr_resp
max effect - min effect from the fit
slope factor from the fit
run_fit()
# generate some fit outputs
## fit only
fitd1 <- run_fit(zfishbeh, modls = "cc2")
## fit + bootstrap samples
fitd2 <- run_fit(zfishbeh, n_samples = 3, modls = "hill")
## fit using hill + cnst
fitd3 <- run_fit(zfishbeh, modls = c("hill", "cnst"))
# only to extract the activity data
sumd1 <- summarize_fit_output(fitd1, extract_only = TRUE)
sumd3 <- summarize_fit_output(fitd3, extract_only = TRUE)
# calculate EC20 instead of default EC10
sumd1 <- summarize_fit_output(fitd1, extract_only = TRUE, perc_resp = 20)
# calculate POD using a higher noise level (e.g., 40)
## this number depends on the response unit
sumd1 <- summarize_fit_output(fitd1, extract_only = TRUE, thr_resp = 40)
# calculate confidence intervals based on the bootstrap samples
sumd2 <- summarize_fit_output(fitd2)
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