Package 'evabic'

Add names to a vector

Description

Add names to a vector, with default names.

Usage

add_names(x, names = NULL, prefix = "x")

Arguments

x	A vector.
names	Vector of names to add. If NULL, default names are added.
prefix	The prefix to add before default names. Useful only if names is set to NULL.

Value

A named vector

Examples

add_names(month.name)

---

Available measures

Description

Available measures in evabic

Usage

ebc_allmeasures

Format

An object of class character of length 18.

Details

TP

True Positive

FP

False Positive

FN

False Negative

TN

True Negative

TPR

True Positive Rate or Sensitivity or Recall or Power

$TPR = \frac{TP}{TP + FN} = 1 - FNR$

TNR

True Negative Rate or Specificity

$TNR = \frac{TN}{FP + TN} = 1 - FPR$

PPV

Positive Predictive Value or Precision

$PPV = \frac{TP}{TP + FP} = 1 - FDR$

NPV

Negative Predictive Value

$NPV = \frac{TN}{TN + FN} = 1 - FOR$

FNR

False Negative Rate or Type II Error Rate or Miss Rate

$FNR = \frac{FN}{TP + FN} = 1 - TPR$

FPR

False Positive Rate or Type I Errors Rate or Fall-out

$FPR = \frac{FP}{FP + TN} = 1 - TNR$

FDR

False Discovery Rate

$FDR = \frac{FP}{FP + TP} = 1 - PPV$

FOR

False Omission Rate

$FOR = \frac{FN}{TN + FN} = 1 - NPV$

ACC

Accuracy

$ACC = \frac{TP + TN}{TP + FP + FN + TN}$

BACC

Balanced Accuracy

$BACC = \frac{\frac{TP}{TP + FN} + \frac{TN}{FP + TN}}{2}$

F1

F1 Score

$F1 = \frac{2 TP}{2TP + FP + FN} = \frac{2}{\frac{1}{TPR} + \frac{1}{PPV}}$

PLR

Positive Likelihood Ratio or LR+ or Likelihood Ratio for Positive Results

$PLR = \frac{TPR}{1 - TNR}$

NLR

Negative Likelihood Ratio or LR- or Likelihood Ratio for Negative Results

$NLR = \frac{1 - TPR}{TNR}$

DOR

Diagnostic Odds Ratio

$DOR = \frac{\frac{TP}{FP}}{\frac{FN}{TN}} = \frac{PLR}{NLR}$

References

https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers

Examples

ebc_allmeasures

---

Area under the curve

Description

Compute the Area Under the Curve for a classification.

Usage

ebc_AUC(
  detection_values,
  true,
  all,
  m = length(all),
  direction = c("<", ">", "<=", ">=")
)

ebc_AUC_from_measures(df_measures)

Arguments

detection_values	Values corresponding to elements that are detected. Must be named.
true	Vector of element that are supposed to be detected.
all	Vector of all elements.
m	Total number of elements.
direction	With < (default), detected elements are those which are strictly less than the threshold. Could be change to ">", <= or >=.
df_measures	A dataframe with TPR and FRP columns. E.g. the output of ebc_tidy_by_threshold.

Value

A numeric.

Examples

set.seed(42)
X1 <- rnorm(50)
X2 <- rnorm(50)
X3 <- rnorm(50)
predictors <- paste0("X", 1:3)
df_lm <- data.frame(X1 = X1, X2 = X2, X3 = X3,
                    X4 = X1 + X2 + X3 + rnorm(50, sd = 0.5),
                    X5 = X1 + 3 * X3 + rnorm(50, sd = 0.5),
                    X6 = X2 - 2 * X3 + rnorm(50, sd = 0.5),
                    X7 = X1 - X2 + rnorm(50, sd = 2),
                    Y  = X1 - X2 + 3 * X3 + rnorm(50))
model <- lm(Y ~ ., data = df_lm)
pvalues <- summary(model)$coefficients[-1, 4]
ebc_AUC(pvalues, predictors, m = 7)

df_measures <- ebc_tidy_by_threshold(pvalues, predictors, m = 7)
ebc_AUC_from_measures(df_measures)

---

Confusion matrix

Description

Compute the the confusion matrix

Usage

ebc_confusion(detected, true, all, m = length(all), prop = FALSE)

Arguments

detected	Vector of elements that are detected.
true	Vector of element that are supposed to be detected.
all	Vector of all elements.
m	Total number of elements.
prop	Logical, default to FALSE. Should the matrix sum to one?

Details

See ebc_allmeasures for the description of the measures.

Value

A 2*2 named matrix.

Examples

ebc_confusion(detected = c("A", "C", "D"), true = c("A", "B", "C"), m = 6)

---

Tidy output for measures

Description

Construct a single row summary of the classifier.

Usage

ebc_tidy(
  detected,
  true,
  all,
  m = length(all),
  measures = c("TPR", "FPR", "FDR", "ACC", "F1")
)

Arguments

detected	Vector of elements that are detected.
true	Vector of element that are supposed to be detected.
all	Vector of all elements.
m	Total number of elements.
measures	Desired measures of performance.

Details

See ebc_allmeasures for the available measures and their descriptions.

Value

A single-row data.frame with one column per element in measures.

See Also

ebc_TP, ebc_TPR, ebc_allmeasures

Examples

ebc_tidy(detected = c("A", "C", "D"), true = c("A", "B", "C"),
         all = LETTERS[1:6], measures = c("ACC", "FDR"))

---

Measures by threshold

Description

Computes measures according to a moving threshold.

Usage

ebc_tidy_by_threshold(
  detection_values,
  true,
  all,
  m = length(all),
  measures = c("TPR", "FPR", "FDR", "ACC", "F1"),
  direction = c("<", ">", "<=", ">=")
)

Arguments

detection_values	Values corresponding to elements that are detected. Must be named.
true	Vector of element that are supposed to be detected.
all	Vector of all elements.
m	Total number of elements.
measures	Desired measures of performance.
direction	With < (default), detected elements are those which are strictly less than the threshold. Could be change to ">", <= or >=.

Details

See ebc_allmeasures for the available measures and their descriptions.

Value

A dataframe with one column called threshold and other corresponding to those specified in measures.

Examples

set.seed(42)
X1 <- rnorm(50)
X2 <- rnorm(50)
X3 <- rnorm(50)
predictors <- paste0("X", 1:3)
df_lm <- data.frame(X1 = X1, X2 = X2, X3 = X3,
                    X4 = X1 + X2 + X3 + rnorm(50, sd = 0.5),
                    X5 = X1 + 3 * X3 + rnorm(50, sd = 0.5),
                    X6 = X2 - 2 * X3 + rnorm(50, sd = 0.5),
                    X7 = X1 - X2 + rnorm(50, sd = 2),
                    Y  = X1 - X2 + 3 * X3 + rnorm(50))
model <- lm(Y ~ ., data = df_lm)
pvalues <- summary(model)$coefficients[-1, 4]
ebc_tidy_by_threshold(pvalues, predictors, m = 7)

---

Confusion measures.

Description

Basic measures from the confusion matrix.

Usage

ebc_TP(detected, true)

ebc_FP(detected, true)

ebc_FN(detected, true)

ebc_TN(detected, true, all, m = length(all))

Arguments

detected	Vector of elements that are detected.
true	Vector of element that are supposed to be detected.
all	Vector of all elements.
m	Total number of elements.

Details

See ebc_allmeasures for the description of the measures.

Value

An integer.

See Also

ebc_TPR, ebc_tidy, ebc_allmeasures

Examples

ebc_TP(detected = c("A", "C", "D"), true = c("A", "B", "C"))
ebc_FP(detected = c("A", "C", "D"), true = c("A", "B", "C"))
ebc_FN(detected = c("A", "C", "D"), true = c("A", "B", "C"))
ebc_TN(detected = c("A", "C", "D"), true = c("A", "B", "C"),
       all = LETTERS[1:6])
ebc_TN(detected = c("A", "C", "D"), true = c("A", "B", "C"), m = 6)

---

Derived measures.

Description

Measures derived from confusion matrix.

Usage

ebc_TPR(detected, true)

ebc_TNR(detected, true, all, m = length(all))

ebc_PPV(detected, true)

ebc_NPV(detected, true, all, m = length(all))

ebc_FNR(detected, true)

ebc_FPR(detected, true, all, m = length(all))

ebc_FDR(detected, true)

ebc_FOR(detected, true, all, m = length(all))

ebc_ACC(detected, true, all, m = length(all))

ebc_BACC(detected, true, all, m = length(all))

ebc_F1(detected, true)

ebc_PLR(detected, true, all, m = length(all))

ebc_NLR(detected, true, all, m = length(all))

ebc_DOR(detected, true, all, m = length(all))

Arguments

detected	Vector of elements that are detected.
true	Vector of element that are supposed to be detected.
all	Vector of all elements.
m	Total number of elements.

Details

See ebc_allmeasures for the description of the measures.

Value

A numeric.

See Also

ebc_TP, ebc_tidy, ebc_allmeasures

Examples

ebc_TPR(detected = c("A", "C", "D"), true = c("A", "B", "C"))
ebc_ACC(detected = c("A", "C", "D"), true = c("A", "B", "C"),
        all = LETTERS[1:5])

---