| Title: | Fast Analysis of ROC Curves |
| Version: | 0.1.0 |
| Description: | A toolkit for analyzing classifier performance by using receiver operating characteristic (ROC) curves. Performance may be assessed on a single classifier or multiple ones simultaneously, making it suitable for comparisons. In addition, different metrics allow the evaluation of local performance when working within restricted ranges of sensitivity and specificity. For details on the different implementations, see McClish D. K. (1989) <doi:10.1177/0272989X8900900307>, Vivo J.-M., Franco M. and Vicari D. (2018) <doi:10.1007/S11634-017-0295-9>, Jiang Y., et al (1996) <doi:10.1148/radiology.201.3.8939225>, Franco M. and Vivo J.-M. (2021) <doi:10.3390/math9212826> and Carrington, André M., et al (2020) <doi:10.1186/s12911-019-1014-6>. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | cli, dplyr, forcats, ggplot2, magrittr, purrr, rlang, stringr, SummarizedExperiment, tibble, tidyr |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| URL: | https://pablopnc.github.io/ROCnGO/, https://github.com/pabloPNC/ROCnGO |
| Depends: | R (≥ 4.1.0) |
| BugReports: | https://github.com/pabloPNC/ROCnGO/issues |
| NeedsCompilation: | no |
| Packaged: | 2025-07-14 16:59:55 UTC; infer |
| Author: | Pablo Navarro [aut, cre, cph], Juana-María Vivo [aut], Manuel Franco [aut] |
| Maintainer: | Pablo Navarro <pablo.navarrocarpio@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-07-17 20:30:18 UTC |
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
The result of calling rhs(lhs).
Show chance line in a ROC plot
Description
Plot chance line in a ROC plot.
Usage
add_chance_line()
Value
A ggplot layer instance object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_chance_line()
Add FpAUC lower bound to a ROC plot
Description
Calculate and plot lower bound defined by FpAUC sensitivity index.
-
add_fpauc_lower_bound()provides an upper level function which automatically calculates curve shape and plots a lower bound that better fits it. -
add_fpauc_partially_proper_lower_bound()andadd_fpauc_concave_lower_bound()are lower level functions that enforce the plot of specific bounds.
First one plots lower bound when curve shape is partially proper (presents some kind of hook). Second one plots lower bound when curve shape is concave in the region of interest.
Usage
add_fpauc_partially_proper_lower_bound(
data,
response = NULL,
predictor = NULL,
threshold,
.condition = NULL,
.label = NULL
)
add_fpauc_concave_lower_bound(
data,
response = NULL,
predictor = NULL,
threshold,
.condition = NULL,
.label = NULL
)
add_fpauc_lower_bound(
data,
response = NULL,
predictor = NULL,
threshold,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
threshold |
A number between 0 and 1, inclusive. This number represents the lower value of TPR for the region where to calculate and plot lower bound. Because of definition of |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot layer instance object.
Examples
# Add lower bound based on curve shape (Concave)
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_fpauc_lower_bound(
data = iris,
response = Species,
predictor = Sepal.Width,
threshold = 0.9
)
Add a threshold line to a ROC plot
Description
Include a threshold line on an specified axis.
Usage
add_fpr_threshold_line(threshold)
add_tpr_threshold_line(threshold)
add_threshold_line(threshold, ratio = NULL)
Arguments
threshold |
A number between 0 and 1, both inclusive, which represents the region bound where to calculate partial area under curve. If If |
ratio |
Ratio in which to display threshold.
|
Value
A ggplot layer instance object.
Examples
# Add two threshold line in TPR = 0.9 and FPR = 0.1
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_threshold_line(threshold = 0.9, ratio = "tpr") +
add_threshold_line(threshold = 0.1, ratio = "fpr")
# Add threshold line in TPR = 0.9
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_tpr_threshold_line(threshold = 0.9)
# Add threshold line in FPR = 0.1
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_fpr_threshold_line(threshold = 0.1)
Add a section of a ROC curve to an existing one
Description
Add an specific region of a ROC curve to an existing ROC plot.
Usage
add_partial_roc_curve(
data,
response = NULL,
predictor = NULL,
ratio,
threshold,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
ratio |
Ratio or axis where to apply calculations.
|
threshold |
A number between 0 and 1, both inclusive, which represents the region bound where to calculate partial area under curve. If If |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot layer instance object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_partial_roc_curve(
iris,
response = Species,
predictor = Sepal.Length,
ratio = "tpr",
threshold = 0.9
)
Add points in a section of a ROC curve to an existing plot
Description
Add points in a specific ROC region to an existing ROC plot.
Usage
add_partial_roc_points(
data,
response = NULL,
predictor = NULL,
ratio,
threshold,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
ratio |
Ratio or axis where to apply calculations.
|
threshold |
A number between 0 and 1, both inclusive, which represents the region bound where to calculate partial area under curve. If If |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot layer instance object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_partial_roc_points(
iris,
response = Species,
predictor = Sepal.Length,
ratio = "tpr",
threshold = 0.9
)
Add a ROC curve plot to an existing one
Description
Add a ROC curve to an existing ROC plot.
Usage
add_roc_curve(
data,
response = NULL,
predictor = NULL,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot layer instance object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_roc_curve(iris, response = Species, predictor = Sepal.Length)
Add ROC points plot to an existing one
Description
Add ROC points to an existing ROC plot.
Usage
add_roc_points(
data,
response = NULL,
predictor = NULL,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot layer instance object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_roc_points(iris, response = Species, predictor = Sepal.Length)
Add TpAUC lower bound to a ROC plot
Description
Calculate and plot lower bound defined by TpAUC specificity index.
-
add_tpauc_lower_bound()provides a upper level function which automatically calculates curve shape and plots a lower bound that better fits it.
Additionally, several lower level functions are provided to plot specific lower bounds:
-
add_tpauc_concave_lower_bound(). Plot lower bound corresponding to a ROC curve with concave shape in selected region. -
add_tpauc_partially_proper_lower_bound. Plot lower bound corresponding to a ROC curve with partially proper (presence of some hook) in selected region. -
add_tpauc_under_chance_lower_bound. Plot lower bound corresponding to a ROC curve with a hook under chance line in selected region.
Usage
add_tpauc_concave_lower_bound(
data,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
.condition = NULL,
.label = NULL
)
add_tpauc_partially_proper_lower_bound(
data,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
.condition = NULL,
.label = NULL
)
add_tpauc_under_chance_lower_bound(
data,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
.condition = NULL,
.label = NULL
)
add_tpauc_lower_bound(
data,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
lower_threshold, upper_threshold |
Two numbers between 0 and 1, inclusive. These numbers represent lower and upper values of FPR region where to calculate and plot lower bound. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot layer instance object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_tpauc_lower_bound(
data = iris,
response = Species,
predictor = Sepal.Width,
upper_threshold = 0.1,
lower_threshold = 0
)
Calculate area under ROC curve
Description
Calculates area under curve (AUC) of a predictor's ROC curve.
Usage
auc(data = NULL, response, predictor, .condition = NULL)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
Value
A numerical value representing the area under ROC curve.
Examples
# Calc AUC of Sepal.Width as a classifier of setosa species
auc(iris, Species, Sepal.Width)
# Change class to predict to virginica
auc(iris, Species, Sepal.Width, .condition = "virginica")
Calculate curve shape over an specific region
Description
calc_curve_shape() calculates ROC curve shape over a specified region.
Usage
calc_curve_shape(
data = NULL,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
ratio,
.condition = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
lower_threshold, upper_threshold |
Two numbers between 0 and 1, inclusive. These numbers represent lower and upper bounds of the region where to apply calculations. |
ratio |
Ratio or axis where to apply calculations.
|
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
Value
A string indicating ROC curve shape in the specified region. Result can take any of the following values:
-
"Concave". ROC curve is concave over the entire specified region. -
"Partially proper". ROC curve loses concavity at some point of the specified region. -
"Hook under chance". ROC curve loses concavity at some point of the region and it lies below chance line.
Examples
# Calc ROC curve shape of Sepal.Width as a classifier of setosa species
# in TPR = (0.9, 1)
calc_curve_shape(iris, Species, Sepal.Width, 0.9, 1, "tpr")
# Change class to virginica
calc_curve_shape(iris, Species, Sepal.Width, 0.9, 1, "tpr", .condition = "virginica")
Calculate ROC curve partial points
Description
Calculates a series pairs of (FPR, TPR) which correspond to ROC curve points in a specified region.
Usage
calc_partial_roc_points(
data = NULL,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
ratio,
.condition = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
lower_threshold, upper_threshold |
Two numbers between 0 and 1, inclusive. These numbers represent lower and upper bounds of the region where to apply calculations. |
ratio |
Ratio or axis where to apply calculations.
|
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
Value
A tibble with two columns:
"tpr". Containing "true positive ratio", or y, values of points within the specified region.
"fpr". Containing "false positive ratio", or x, values of points within the specified region.
Examples
# Calc ROC points of Sepal.Width as a classifier of setosa species
# in TPR = (0.9, 1)
calc_partial_roc_points(
iris,
response = Species,
predictor = Sepal.Width,
lower_threshold = 0.9,
upper_threshold = 1,
ratio = "tpr"
)
# Change class to virginica
calc_partial_roc_points(
iris,
response = Species,
predictor = Sepal.Width,
lower_threshold = 0.9,
upper_threshold = 1,
ratio = "tpr",
.condition = "virginica"
)
Concordance indexes
Description
Concordance derived indexes allow calculation and explanation of area under ROC curve in a specific region. They use a dual perspective since they consider both TPR and FPR ranges which enclose the region of interest.
cp_auc() applies concordan partial area under curve (CpAUC), while
ncp_auc() applies its normalized version by dividing by the total area.
Usage
cp_auc(
data = NULL,
response,
predictor,
lower_threshold,
upper_threshold,
ratio,
.condition = NULL
)
ncp_auc(
data = NULL,
response,
predictor,
lower_threshold,
upper_threshold,
ratio,
.condition = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
lower_threshold, upper_threshold |
Two numbers between 0 and 1, inclusive. These numbers represent lower and upper bounds of the region where to apply calculations. |
ratio |
Ratio or axis where to apply calculations.
|
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
Value
A numeric value representing index score for the partial area under ROC curve.
References
Carrington, André M., et al. A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms. BMC medical informatics and decision making 20 (2020): 1-12.
Examples
# Calculate cp_auc of Sepal.Width as a classifier of setosa especies in
# FPR = (0, 0.1)
cp_auc(
iris,
response = Species,
predictor = Sepal.Width,
lower_threshold = 0,
upper_threshold = 0.1,
ratio = "fpr"
)
# Calculate ncp_auc of Sepal.Width as a classifier of setosa especies in
# FPR = (0, 0.1)
ncp_auc(
iris,
response = Species,
predictor = Sepal.Width,
lower_threshold = 0,
upper_threshold = 0.1,
ratio = "fpr"
)
Hide legend in a ROC plot
Description
Hide legend showing name of ploted classifiers and bounds in a ROC curve plot.
Usage
hide_legend()
Value
A ggplot theme object.
Add NpAUC lower bound to a ROC plot
Description
Calculate and plot lower bound defined by NpAUC specificity index.
-
add_npauc_normalized_lower_bound()allows to plot normalized lower bound, which is used to formally calculate NpAUC. -
add_npauc_lower_bound()is a lower level function providing a way to plot lower bound previous to normalization.
Usage
add_npauc_lower_bound(
data,
response = NULL,
predictor = NULL,
threshold,
.condition = NULL,
.label = NULL
)
add_npauc_normalized_lower_bound(
data,
response = NULL,
predictor = NULL,
threshold,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
threshold |
A number between 0 and 1, inclusive. This number represents the lower value of TPR for the region where to calculate and plot lower bound. Because of definition of |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot layer instance object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_npauc_lower_bound(
iris,
response = Species,
predictor = Sepal.Width,
threshold = 0.9
)
Calculate partial area under curve
Description
Calculates area under curve curve in an specific TPR or FPR region.
Usage
pauc(
data = NULL,
response,
predictor,
ratio,
lower_threshold,
upper_threshold,
.condition = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
ratio |
Ratio or axis where to apply calculations.
|
lower_threshold, upper_threshold |
Two numbers between 0 and 1, inclusive. These numbers represent lower and upper bounds of the region where to apply calculations. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
Value
A numeric value representing the area under ROC curve in the specified region.
Examples
# Calculate pauc of Sepal.Width as a classifier of setosa species in
# in TPR = (0.9, 1)
pauc(
iris,
response = Species,
predictor = Sepal.Width,
ratio = "tpr",
lower_threshold = 0.9,
upper_threshold = 1
)
# Calculate pauc of Sepal.Width as a classifier of setosa species in
# in FPR = (0, 0.1)
pauc(
iris,
response = Species,
predictor = Sepal.Width,
ratio = "fpr",
lower_threshold = 0,
upper_threshold = 0.1
)
Plot a section of a classifier ROC curve
Description
Create a curve plot using points in an specific region of ROC curve.
Usage
plot_partial_roc_curve(
data,
response = NULL,
predictor = NULL,
ratio,
threshold,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
ratio |
Ratio or axis where to apply calculations.
|
threshold |
A number between 0 and 1, both inclusive, which represents the region bound where to calculate partial area under curve. If If |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot object.
Examples
plot_partial_roc_curve(
iris,
response = Species,
predictor = Sepal.Width,
ratio = "tpr",
threshold = 0.9
)
Plot points in a region of a ROC curve
Description
Create an scatter plot using points in an specific region of ROC curve.
Usage
plot_partial_roc_points(
data,
response = NULL,
predictor = NULL,
ratio,
threshold,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
ratio |
Ratio or axis where to apply calculations.
|
threshold |
A number between 0 and 1, both inclusive, which represents the region bound where to calculate partial area under curve. If If |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot object.
Examples
plot_partial_roc_points(
iris,
response = Species,
predictor = Sepal.Width,
ratio = "tpr",
threshold = 0.9
)
Plot a classifier ROC curve
Description
Create a curve plot using ROC curve points.
Usage
plot_roc_curve(
data,
response = NULL,
predictor = NULL,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width)
Plot classifier points of a ROC curve
Description
Create an scatter plot using ROC curve points.
Usage
plot_roc_points(
data,
response = NULL,
predictor = NULL,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Value
A ggplot object.
Examples
plot_roc_points(iris, response = Species, predictor = Sepal.Width)
Establish condition of interest as 1 and absence as 0.
Description
Transforms levels in a factor to 1 if they match condition of interest (
condition) or 0 otherwise (absent) or 0 otherwise (absent).
Usage
reorder_response_factor(response_fct, condition, absent)
Arguments
response_fct |
A factor with different categories ( |
condition |
Name of category being the condition of interest. |
absent |
Character vector of categories not corresponding to the condition of interest. |
Value
factorwith values (0, 1) where 1 matches condition of interest.
Calculate ROC curve points
Description
Calculates a series pairs of (FPR, TPR) which correspond to points displayed by ROC curve. "false positive ratio" will be represented on x axis, while "true positive ratio" on y one.
Usage
roc_points(data = NULL, response, predictor, .condition = NULL)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
Value
A tibble with two columns:
"tpr". Containing values for "true positive ratio", or y axis.
"fpr". Containing values for "false positive ratio", or x axis.
Examples
# Calc ROC points of Sepal.Width as a classifier of setosa species
roc_points(iris, Species, Sepal.Width)
# Change class to predict to virginica
roc_points(iris, Species, Sepal.Width, .condition = "virginica")
Sensitivity indexes
Description
Sensitivity indexes provide different ways of calculating area under ROC curve in a specific TPR region. Two different approaches to calculate this area are available:
-
fp_auc()applies fitted partial area under curve index (FpAUC). This one calculates area under curve adjusting to points defined by the curve in the selected region. -
np_auc()applies normalized partial area under curve index (NpAUC), which calculates area under curve over the whole specified region.
Usage
fp_auc(data = NULL, response, predictor, lower_tpr, .condition = NULL)
np_auc(data, response, predictor, lower_tpr, .condition = NULL)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
lower_tpr |
A numeric value between 0 and 1, inclusive, which represents lower value of TPR for the region where to calculate the partial area under curve. Because of definition of sensitivity indexes, upper bound of the region will be established as 1. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
Value
A numeric value representing the index score for the partial area under ROC curve.
References
Franco M. y Vivo J.-M. Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity. Mathematics 9, 2826 (2021).
Jiang Y., Metz C. E. y Nishikawa R. M. A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology 201, 745-750 (1996).
Examples
# Calculate fp_auc of Sepal.Width as a classifier of setosa species
# in TPR = (0.9, 1)
fp_auc(iris, response = Species, predictor = Sepal.Width, lower_tpr = 0.9)
# Calculate np_auc of Sepal.Width as a classifier of setosa species
# in TPR = (0.9, 1)
np_auc(iris, response = Species, predictor = Sepal.Width, lower_tpr = 0.9)
Specificity indexes
Description
Specificity indexes provide different ways of calculating area under ROC curve in a specific FPR region. Two different approaches to calculate this area are available:
-
tp_auc()applies tighter partial area under curve index (SpAUC). This one calculates area under curve adjusting to points defined by the curve in the selected region. -
sp_auc()applies standardized partial area under curve index (TpAUC), which calculates area under curve over the whole specified region.
Usage
sp_auc(
data = NULL,
response,
predictor,
lower_fpr,
upper_fpr,
.condition = NULL,
.invalid = FALSE
)
tp_auc(
data = NULL,
response,
predictor,
lower_fpr,
upper_fpr,
.condition = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
lower_fpr, upper_fpr |
Two numbers between 0 and 1, inclusive. These numbers represent lower and upper values of FPR region where to calculate partial area under curve. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.invalid |
If |
Value
A numeric value representing the index score for the partial area under ROC curve.
References
McClish D. K. Analyzing a Portion of the ROC Curve. Medical Decision Making 9, 190-195 (1989).
Vivo J.-M., Franco M. y Vicari D. Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range. Advances in Data Analysis and Classification 12, 683-704 (2018).
Examples
# Calculate sp_auc of Sepal.Width as a classifier of setosa species
# in FPR = (0.9, 1)
sp_auc(
iris,
response = Species,
predictor = Sepal.Width,
lower_fpr = 0,
upper_fpr = 0.1
)
# Calculate tp_auc of Sepal.Width as a classifier of setosa species
# in FPR = (0.9, 1)
tp_auc(
iris,
response = Species,
predictor = Sepal.Width,
lower_fpr = 0,
upper_fpr = 0.1
)
Add SpAUC lower bound to a ROC plot
Description
Calculate and plot lower bound defined by SpAUC specificity index.
Usage
add_spauc_lower_bound(
data,
response = NULL,
predictor = NULL,
lower_threshold,
upper_threshold,
.condition = NULL,
.label = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
lower_threshold, upper_threshold |
Two numbers between 0 and 1, inclusive. These numbers represent lower and upper bounds of the region where to apply calculations. |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.label |
A string representing the name used in labels. If |
Details
SpAUC presents some limitations regarding its lower bound. Lower bound defined by this index cannot be applied to sections where ROC curve is defined under chance line.
add_spauc_lower_bound() doesn't make any check to ensure the index can be
safely applied. Consequently, it allows to enforce the representation even
though SpAUC cound't be calculated in the region.
Value
A ggplot layer instance object.
Examples
plot_roc_curve(iris, response = Species, predictor = Sepal.Width) +
add_spauc_lower_bound(
iris,
response = Species,
predictor = Sepal.Width,
lower_threshold = 0,
upper_threshold = 0.1
)
Transform data in a SummarizedExperiment to a data.frame
Description
Transforms a SummarizedExperiment into a data.frame which can be used as input for other functions.
Usage
sumexp_to_df(se, .n = NULL)
Arguments
se |
A SummarizedExperiment object. |
.n |
An integer or string, representing the index or name of the assay
to use. Same as By default, function combines every assay in |
Value
A data.frame created from combining assays and colData in a SummarizedExperiment.
Summarize classifiers performance in a dataset
Description
Calculate a series of metrics describing global and local performance for selected classifiers in a dataset.
Usage
summarize_dataset(
data,
predictors = NULL,
response,
ratio,
threshold,
.condition = NULL,
.progress = FALSE
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
predictors |
A vector of numeric data variables which represents the different classifiers or predictors in data to be summarized. If |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
ratio |
Ratio or axis where to apply calculations.
|
threshold |
A number between 0 and 1, both inclusive, which represents the region bound where to calculate partial area under curve. If If |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
.progress |
If |
Value
A list with different elements:
Performance metrics for each of evaluated classifiers.
Overall description of performance metrics in the dataset.
Examples
summarize_dataset(iris, response = Species, ratio = "tpr", threshold = 0.9)
Summarize classifier performance
Description
Calculates a series of metrics describing global and local classifier performance.
Usage
summarize_predictor(
data = NULL,
predictor,
response,
ratio,
threshold,
.condition = NULL
)
Arguments
data |
A data.frame or extension (e.g. a tibble) containing values for predictors and response variables. |
predictor |
A data variable which must be numeric, representing values of a classifier or predictor for each observation. |
response |
A data variable which must be a factor, integer or character vector representing the prediction outcome on each observation (Gold Standard). If the variable presents more than two possible outcomes, classes or categories:
New combined category represents the "absence" of the condition to predict.
See |
ratio |
Ratio or axis where to apply calculations.
|
threshold |
A number between 0 and 1, both inclusive, which represents the region bound where to calculate partial area under curve. If If |
.condition |
A value from response that represents class, category or condition of interest which wants to be predicted. If Once the class of interest is selected, rest of them will be collapsed in a common category, representing the "absence" of the condition to be predicted. See |
Value
A single row tibble with different predictor with following metrics as columns:
Area under curve (AUC) as a metric of global performance.
Partial are under curve (pAUC) as a metric of local performance.
Indexes derived from pAUC, depending on the selected ratio. Sensitivity indexes will be used for TPR and specificity indexes for FPR.
-
Curve shape in the specified region.
Examples
# Summarize Sepal.Width as a classifier of setosa species
# and local performance in TPR (0.9, 1)
summarize_predictor(
data = iris,
predictor = Sepal.Width,
response = Species,
ratio = "tpr",
threshold = 0.9
)
# Summarize Sepal.Width as a classifier of setosa species
# and local performance in FPR (0, 0.1)
summarize_predictor(
data = iris,
predictor = Sepal.Width,
response = Species,
ratio = "fpr",
threshold = 0.1
)
Transforms a response variable into a valid factor that can be processed downstream.
Description
transform_response transforms response so that it can be processed in
further steps. Function transforms input into a factor of values 1 and 0
corresponding to the condition of interest and absence of it respectively.
Usage
transform_response(response, .condition = NULL)
Arguments
response |
A factor, integer or character vector of categories. |
Details
By default function takes some assumption on how to make transformation,
depending on the class of response:
factor. Function considers the condition of interest first level in factor.
integer. Function considers the condition of interest the
minvalue of\ response.character. Function considers the condition of interest the first value in
unique(response)after usingsort.
Value
factor of levels (0,1), where 1 represents the condition of
interest and 0 absence of it.