| Type: | Package |
| Title: | Tool for Phi Delta Analysis of Features |
| Version: | 1.0.1 |
| Author: | Nikolas Rothe and Ursula Neumann |
| Maintainer: | Ursula Neumann <ursula.neumann@uni-marburg.de> |
| Description: | Analysis of features by phi delta diagrams. In particular, functions for reading data and calculating phi and delta as well as the functionality to plot it. Moreover it is possible to do further analysis on the data by generating rankings. For more information on phi delta diagrams, see also Giuliano Armano (2015) <doi:10.1016/j.ins.2015.07.028>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1.9000 |
| NeedsCompilation: | no |
| Packaged: | 2018-05-07 13:25:59 UTC; ursula |
| Repository: | CRAN |
| Date/Publication: | 2018-05-08 08:27:22 UTC |
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
RainBoolclassification variable: if it has not rained: 0, if it has rained: 1
dateindex variable from 1 to 29
Tmintemperature minimum of the day
Tmaxtemperature maximum of the day
SunAvgsunshine duration of the day
RelHumAvgaverage relative humidity of the day
WindForceAvgaverage 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)
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])
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)
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))