Package 'phiDelta'

borders of the phi delta space

Description

calculates the corners of the phi delta space

Usage

borders(ratio)

Arguments

ratio

is the ratio of positive and negative of the data. The default is 1

Value

a matrix. Each row represents a corner in the following order: top, right, bottom, left

Author(s)

rothe

Examples

borders(1.0)
borders(0.5)
borders(2)

---

confusion matrices

Description

calculates the confusion matrices from the c_statistics

Usage

c_matrices(stats)

Arguments

stats

c_statistics

Value

a matrix. Each column represents a feature. Each row describes in this order: true negative, FALSE negative, true positive, FALSE negative

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
cmat <- c_matrices(x)

---

Raw Confusion Statistics

Description

reformarts the raw file data to c_statistics data so it can be used for most of the functions in this package. it can be used directly after loading data from a file like .csv

Usage

c_statistics(file)

Arguments

file	raw data from a file, for example the output of read.csv. the file must be formarted as follows: The first column contains tho output of the classifier. It should only be 1 or 0 The other columns represent the features. The names of the columns 2.. are considered as the names of the features

Value

dataframe, first column are the labels, 0 is a negative sample, 1 a positve the other columns contain the

Author(s)

rothe

Examples

data("climate_data")
x <- c_statistics(climate_data)

---

calculate delta

Description

calculates delta out of specificity and sensitivity depending on the ratio

Usage

calculate_delta(spec, sens, ratio = 1)

Arguments

spec	is the specificity, the true negative rate
sens	is the sensitivity, the true positive rate
ratio	is the ratio of positive and negative of the data. The default is 1

Value

delta

Author(s)

rothe

Examples

calculate_delta(1,0)
calculate_delta(0.5,0.3)

---

calculate entropy

Description

calculates the entropy of a specificity and sensitivity tuple considering the ratio

Usage

calculate_entropy(spec, sens, ratio = 1)

Arguments

spec	numeric, is the specificity, the true negative rate
sens	numeric, is the sensitivity, the true positive rate
ratio	numeric, is the ratio of positive and negative of the data

Value

entropy of the tuple

Author(s)

rothe

Examples

calculate_entropy(1,0)
calculate_entropy(0.5,0.6,0.7)

---

calculate phi

Description

calculates phi out of specificity and sensitivity depending on the ratio

Usage

calculate_phi(spec, sens, ratio = 1)

Arguments

spec	is the specificity, the true negative rate
sens	is the sensitivity, the true positive rate
ratio	is the ratio of positive and negative of the data. The default is 1

Value

phi

Author(s)

rothe

Examples

calculate_phi(1,0)
calculate_phi(0.5,0.3)

---

calculate ratio

Description

calculates the ratio between positive and negative samples

Usage

calculate_ratio(stats)

Arguments

stats

c_statistics

Value

ratio

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
ratio <- calculate_ratio(x)

---

Meteorological data for feature selection analysis

Description

A dataset with meteorological data from a weather station in Frankfurt (Oder), Germany from february 2016

Usage

climate_data

Format

a data frame with 29 entries and following 7 variables

RainBool

classification variable: if it has not rained: 0, if it has rained: 1

date

index variable from 1 to 29

Tmin

temperature minimum of the day

Tmax

temperature maximum of the day

SunAvg

sunshine duration of the day

RelHumAvg

average relative humidity of the day

WindForceAvg

average wind force of the day

References

modified data from http://wetterstationen.meteomedia.de/

---

Diagram crossings

Description

adds crossings to the plot depending on the ratio

Usage

crossings(ratio, col = "darkblue", ...)

Arguments

ratio	is the ratio of positive and negative of the data
col	the color of the lines. Default is darkblue
...	further graphical parameters, see par

Author(s)

Neumann

Examples

x <- c_statistics(climate_data)
ratio <- calculate_ratio(x)
phiDelta_plot_from_data(x, crossing = FALSE)
crossings(ratio, col = "green")

---

distance to the middle of the space

Description

calculates the euclidic distance of a phi delta tuple to the middle of the phi delta space. This could be used for a rating of the features

Usage

dist_to_middle(phi, delta, ratio)

Arguments

phi	numeric value or vector of phi
delta	numeric value or vector of delta
ratio	is the ratio of positive and negative of the data. The default is 1

Value

the euclidic distance of the tuple to the middle

Author(s)

rothe

Examples

dist_to_middle(1,0,1)
dist_to_middle(0.5,0.3,1)

---

distance to top or bottom

Description

calculates the distance of the tuple to the closer corner of top and bottom of the phi delta space with ratio 1. This can be used for a ranking of the features

Usage

dist_to_top(phi, delta)

Arguments

phi	numeric value or vector of phi
delta	numeric value or vector of delta

Value

distance to the top or the bottom corner

Author(s)

rothe

Examples

dist_to_top(1,0)
dist_to_top(0.5,0.3)

---

isometric accuracy lines

Description

adds isometric lines for the accuracy to the plot depending on the ratio

Usage

iso_accuracy(ratio = 1, granularity = 0.25, lty = "longdash",
  col = "blue", ...)

Arguments

ratio	numeric value for the ratio of positive and negative of the data
granularity	numeric value between 0 and 1 for the granularity of the lines. It is a value for the distance between 2 lines
lty	the type of line, see par
col	the color of the lines
...	further graphical parameters, see par

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
ratio <- calculate_ratio(x)
phiDelta_plot_from_data(x)
iso_accuracy(ratio, col = "green")

---

isometric entropy

Description

draws isometric curves for the entropy by calculating the entropy for all points in a grid and connecting those within a epsilon enviroment of the value

Usage

iso_entropy_curve(x, ratio = 1, eps = 0.001, grid_granularity = 0.01)

Arguments

x	numeric, is the offset for the points
ratio	numeric, is the ratio
eps	numeric, the epsilon for entropies to be selected
grid_granularity	numeric between 0 and 1, defines the granularity of the grid

Author(s)

Neumann

---

isometric negative predictive value lines

Description

adds isometric lines for the negative predictive value to the plot depending on the ratio

Usage

iso_negative_predictive_value(ratio = 1, granularity = 0.25,
  lty = "longdash", col = "blue", ...)

Arguments

ratio	numeric value for the ratio of positive and negative of the data
granularity	numeric value between 0 and 1 for the granularity of the lines. It is a value for the distance between 2 lines
lty	the type of line, see par
col	the color of the lines
...	further graphical parameters, see par

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
ratio <- calculate_ratio(x)
phiDelta_plot_from_data(x)
iso_negative_predictive_value(ratio, col = "green")

---

isometric precision lines

Description

adds isometric lines for the precision to the plot depending on the ratio

Usage

iso_precision(ratio = 1, granularity = 0.25, lty = "longdash",
  col = "blue", ...)

Arguments

ratio	numeric value for the ratio of positive and negative of the data
granularity	numeric value between 0 and 1 for the granularity of the lines. It is a value for the distance between 2 lines
lty	the type of line, see par
col	the color of the lines
...	further graphical parameters, see par

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
ratio <- calculate_ratio(x)
phiDelta_plot_from_data(x)
iso_precision(ratio, col = "green")

---

isometric sensitivity lines

Description

adds isometric lines for the sensitivity to the plot depending on the ratio

Usage

iso_sensitivity(ratio = 1, granularity = 0.25, col = "blue",
  lty = "longdash", ...)

Arguments

ratio	numeric value for the ratio of positive and negative of the data
granularity	numeric value between 0 and 1 for the granularity of the lines. It is a value for the distance between 2 lines
col	the color of the lines
lty	the type of line, see par
...	further graphical parameters, see par

Author(s)

Neumann

Examples

x <- c_statistics(climate_data)
ratio <- calculate_ratio(x)
phiDelta_plot_from_data(x)
iso_sensitivity(ratio, col = "green")

---

isometric specificity lines

Description

adds isometric lines for the specificity to the plot depending on the ratio

Usage

iso_specificity(ratio = 1, granularity = 0.25, col = "blue",
  lty = "longdash", ...)

Arguments

ratio	numeric value for the ratio of positive and negative of the data
granularity	numeric value between 0 and 1 for the granularity of the lines. It is a value for the distance between 2 lines
col	the color of the lines
lty	the type of line, see par
...	further graphical parameters, see par

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
ratio <- calculate_ratio(x)
phiDelta_plot_from_data(x)
iso_specificity(ratio, col = "green")

---

normalized confusion matrices

Description

normalizes the confusion matrices

Usage

n_matrices(c_matrices)

Arguments

c_matrices

confusion matrices

Value

a matrix. Each column represents a feature. Each row describes in this order: true negative rate, FALSE negative rate, true positive rate, FALSE negative rate

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
cmat <- c_matrices(x)
nmat <- n_matrices(cmat)

---

phi delta matrix

Description

calculates phi and delta directly from the stats and generates a matrix with the names of the features, their phi and their delta value

Usage

phiDelta_from_data(stats, ratio_corrected = TRUE)

Arguments

stats	c_statistics
ratio_corrected	locigal, if true phi and delta will be calculated in respect to the ratio of positive and negative samples

Value

dataframe, first column are the names of the features second column the phi values third column the delta values

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
phiDelta <- phiDelta_from_data(x, ratio_corrected = FALSE)
with_ratio <- phiDelta_from_data(x)

---

phi delta plot of raw statistic data

Description

this will create a basic plot directly out of the statistic data (c_statistics)

Usage

phiDelta_plot_from_data(stats, names = NULL, ratio_corrected = TRUE, ...)

Arguments

stats	matrix of the statistic data of the features and the classifier
names	vector with feature names
ratio_corrected	logical, if true the plot will concider the ratio of the positive and negative data samples
...	further parameters for the diagram see phiDelta.plot

Author(s)

rothe

Examples

x <- c_statistics(climate_data)
phiDelta_plot_from_data(x)
phiDelta_plot_from_data(x, ratio_corrected = FALSE, iso_spec = TRUE, iso_sens = TRUE)

---

Convertion of specificity and sensitivity to phi and delta

Description

converts specificity and sensitivity to phi and delta depending on the ratio

Usage

phiDelta.convert(spec, sens, ratio = 1)

Arguments

spec	is the specificity, the true negative rate
sens	is the sensitivity, the true positive rate
ratio	is the ratio of positive and negative of the data. The default is 1

Value

List with phi and delta vectors

Author(s)

neumann

Examples

phiDelta.convert(1,0)
phiDelta.convert(0.5,0.3, ratio = 0.8)

---

Plot of phi delta diagram

Description

Plots delta against phi within the phi delta diagram shape

Usage

phiDelta.plot(phi, delta, ratio = 1, names = NULL, border = "red",
  filling = "grey", crossing = TRUE, iso_specificity = FALSE,
  iso_sensitivity = FALSE, iso_neg_predictive_value = FALSE,
  iso_precision = FALSE, iso_accuracy = FALSE, highlighted = NULL)

Arguments

phi	numeric value or vector of phi
delta	numeric value or vector of delta
ratio	numeric, is the ratio of positive and negative of the data
names	string with feature names
border	the color of the border of the shape. NA for no border
filling	the color to fill the shape with
crossing	logical, if the crossing should be drawn
iso_specificity	logical, if isometric lines of the specificity should be drawn
iso_sensitivity	logical, if isometric lines of the sensitivity should be drawn
iso_neg_predictive_value	logical, if isometric lines of the negative predictive value should be drawn
iso_precision	logical, if isometric lines of the precision should be drawn
iso_accuracy	logical, if isometric lines of the accuracy should be drawn
highlighted	numeric vector, indices of the points to higlight highlighted points will be orange

Author(s)

rothe

Examples

x <- climate_data
phiDelta <- phiDelta.stats(x[,-1],x[,1])
phiDelta.plot(phiDelta$phi, phiDelta$delta)
phiDelta.plot(phiDelta$phi, phiDelta$delta,
  ratio = phiDelta$ratio,
  border = "green",
  iso_neg_predictive_value = TRUE,
  crossing = FALSE)

---

Phi delta statistics from dataframe

Description

calculates phi, delta and the ratio directly from the dataframe with provided information and generates a list with the names of the features, their phi and delta value and the ratio

Usage

phiDelta.stats(data, labels, ratio_corrected = TRUE)

Arguments

data	dataframe without labels
labels	vector of labels
ratio_corrected	locigal, if true phi and delta will be calculated in respect to the ratio of positive and negative samples

Value

dataframe, first column are the names of the features second column the phi values third column the delta values

Author(s)

rothe

Examples

x <- climate_data
phiDelta <- phiDelta.stats(x[,-1],x[,1], ratio_corrected = FALSE)
with_ratio <- phiDelta.stats(x[,-1],x[,1])

---

ranking of the features

Description

this function puts together a number of rankings of the features

Usage

rank_stats(stats, ratio_corrected = FALSE, delta_dist = 1)

Arguments

stats	c_statistics, the data input
ratio_corrected	logical, true if ratio shoud be considerd
delta_dist	numeric, the delta value of the anchor for the geometrical ranking see symmetric_distance

Author(s)

rothe

---

X symmetric distance of a point

Description

calculates the Distance from the positive anchor and the negative anchor to the point and returns the smaller one. That means, if y is positive the distance to the positive anchor will be return, if it is negative, the negative anchor distance will be calculated

Usage

symmetric_distance(x, y, anchor)

Arguments

x, y	numerical, in this case phi and delta but in general the input coordinates
anchor	vector (x,y) the anchor for the calculation of the distance

Value

the smaller distance of (x,y) to eather the positive or negative anchor

Examples

symmetric_distance(0.5,0.5,c(0,0))

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