Description Usage Arguments Details Value Author(s) Examples
Do the ROC analysis (roc_analysis
) for each column of x
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## S3 method for class 'manyroc_result'
print(x, ..., show_all = FALSE, perf_digits = 2, fmt = "%.3g")
roc_manyroc(x, gr = NULL, optimize_by = "bac", ...)
## S3 method for class 'matrix'
roc_manyroc(x, gr = NULL, optimize_by = "bac", ..., gr_sep = " vs. ")
## S3 method for class 'numeric'
roc_manyroc(x, gr = NULL, optimize_by = "bac", ...)
## S3 method for class 'data.frame'
roc_manyroc(x, gr = NULL, optimize_by = "bac", ...)
## S3 method for class 'hyperSpec'
roc_manyroc(x, gr = NULL, optimize_by = "bac", ...)

x 
A numeric matrix, a data frame, a 
... 
Arguments passed for further methods. 
show_all 
( 
perf_digits 
( 
fmt 
( 
gr 
Either a string (scalar, 
optimize_by 
(

gr_sep 
Group separator used to paste the names of groups. Default is

Consider ordering of factor gr
levels before the analysis, as the
first level will always be treated as negative and the last as positive.
E.g., if we have factor with 3 levels in this particular order
"A", "B", "C", then "A" will always be negative, "C" always positive and
"B" positive, when compared to "A" and negative, when compared to "C".
The same principle applies if there are more than 3 levels.
This is important determining what specificity and sensitivity, etc., means
in the context of group names: if positive is "A" then sensitivity
will be related to group "A", but if "A" is negative, then specificity
will be related to this group, and sensitivity to the other group.
For spectroscopic data: compare spectra of each pair of indicated groups at each wavelength.
Object of classes manyroc_result
and data.frame
with
columns:
compared_groups
Names of compared groups (separated by
gr_sep
with default value " vs. "
);
feature
names of numeric features used in analysis;
median_neg
median value of negatives group;
cutoff
for optimal threshold/cutoff values and
corresponding performance measures;
median_pos
median value positives group;
TP
number of true positives;
FN
number of false negatives;
FP
number of false positives;
TN
number of true negatives;
sens
sensitivity (true positive rate, recall);
spec
specificity (true negative rate);
PPV
positive predictive value (precision);
NPV
negative predictive value;
BAC
balanced accuracy;
Youden
Youdenâ€™s J index;
Kappa
Cohen's kappa;
AUC
area under the ROC curve;
Vilmantas Gegzna
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  library(manyROC)
#  For numeric vectors objects 
data(PlantGrowth)
roc_manyroc(x = PlantGrowth$weight, gr = PlantGrowth$group)
#  For dataframes objects 
data(CO2)
roc_manyroc(CO2[, c("conc", "uptake")], CO2$Type)
data(OrchardSprays)
roc_manyroc(OrchardSprays$decrease, OrchardSprays$treatment)
#  For hyperSpec objects 
library(hyperSpec)
fluorescence
roc_manyroc(fluorescence[, , 500 ~ 502], fluorescence$gr)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.