Producing plot "coverage - mean error" for applicability domain studies Prediction variance is taken as distance to model

cmf_ecvr_plot_coverage_mean_error(ecvr_fname = "ligands-ecvr.RData", cmep_type = "in", smoothing = TRUE, ...)

Arguments

ecvr_fname

cmep_type

smoothing

Details

Value

References

Note

See also

Examples

##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (ecvr_fname = "ligands-ecvr.RData", cmep_type = "in", smoothing = TRUE, ...) { load(ecvr_fname) y_err <- abs(ecvr$y_exp - ecvr$y_pred_mean) d2m <- ecvr$y_pred_sd cinf_plot_coverage_mean_error(y_err, d2m, cmep_type = cmep_type, smoothing = smoothing, ...) }
#> function (ecvr_fname = "ligands-ecvr.RData", cmep_type = "in", #> smoothing = TRUE, ...) #> { #> load(ecvr_fname) #> y_err <- abs(ecvr$y_exp - ecvr$y_pred_mean) #> d2m <- ecvr$y_pred_sd #> cinf_plot_coverage_mean_error(y_err, d2m, cmep_type = cmep_type, #> smoothing = smoothing, ...) #> } #> <environment: 0x108a67590>