Package 'vennplot'

Data on human encounters with great white sharks.

Description

Data on human encounters with great white sharks.

Usage

sharks

Format

A dataset with 65 rows and 11 columns.

Year

Years encounter sharks

Sex

Sex of victims

Age

Age of victims

Time

Encounter sharks in AM or PM

Australia

Encounter in Australia

USA

Encounter in the United States

Surfing

Surfing incident

Scuba

Scuba-diving incident

Fatality

Whether or not there was a fatality

Injury

Whether or not there was an injury

Length

The length of great white sharks

Source

http://sharkattackinfo.com/shark_attack_news_sas.html. Data collected by Professor Pierre-Jerome Bergeron, University of Ottawa.

Examples

vennplot(disjoint.combinations = sharks, vars = c("Au","USA","Fa","Ti"))

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Draw Venn and Euler diagram in 2D or 3D

Description

Draw Venn and Euler diagram in 2D or 3D

Usage

vennplot(disjoint.combinations = NULL, vars = NULL, Delta = 0.1,
  ThreeD = FALSE, lambda = NULL, stressWay = c("sum", "combine"),
  delta = 0.01, weight = NULL, expand = NULL, twoWayGenerate = FALSE,
  scaleSearch = c("NelderMead", "lineSearch", "goldenSectionSearch", "BFGS",
  "CG", "L-BFGS-B", "SANN", "Brent"), twoWaySearch = c("lineSearch",
  "NelderMead", "goldenSectionSearch", "BFGS", "CG", "L-BFGS-B", "SANN",
  "Brent"), scaleSeachTolerance = list(value = 1e-05, proportional = FALSE),
  distanceTolerance = list(value = 1e-05, proportional = FALSE),
  lossTolerance = list(ToleranceofLoss = 1e-10, maximumStep = 10, ALPHA =
  0.01, ToleranceofStepsize = 1e-05, proportional = FALSE),
  stressBound = 0.001, maximumStep = 50, planeSize = 50, lower = -Inf,
  upper = Inf, control = list(), hessian = FALSE, mar = rep(1, 4),
  cols = NULL, alpha = 0.3, smooth = FALSE, ...)

Arguments

disjoint.combinations	Named numeric vector or data.frame where each column should be factor. See Details.
vars	Extract specific variables of data.frame as disjoint.combinations. If vars = NULL, all the information of data.frame will be extracted.
Delta	The length of step for method "lineSearch" or the initial interval of test points for method "NelderMead".
ThreeD	Draw Venn diagram in 3D. See Examples.
lambda	It can be NULL or a numeric vector. If lambda = NULL, the loss function optimize lambda, else, based on the given lambda, loss function will calculate stress respectively then return the minimum one and corresponding lambdas.
stressWay	If data set can be separated into a few groups, there will be two ways to express stress: one is to sum up all the stress (named "sum"; default), the other is to use total TSS divide by total RSS (named "combine").
delta	Closeness between groups.
weight	The weight of disjoint.combinations. It should have the same length with disjoint.combinations.
expand	If some balls should not intersect and the code fails to detect it. It is possible to be fixed manually but sacrificing stress.
twoWayGenerate	Boolean factor, if false, any missing intersections are set as zero.
scaleSearch	Provide multiple methods to optimize scale lambda. The default method is "NelderMead". See Details.
twoWaySearch	If two way intersections are missing, multiple methods are available to generate two way intersections. The default method is "lineSearch". See Details.
scaleSeachTolerance	A list with tolerance value and boolean factor " proportional". The loop of NelderMead and lineSearch in scaleSearch will end when the difference or proportional difference matches the tolerance value.
distanceTolerance	A list with tolerance value and boolean factor " proportional". The Newton method of finding distance will end when the difference or proportional difference matches the tolerance value.
lossTolerance	A list with ToleranceofLoss, maximumStep, ALPHA, ToleranceofStepsize and boolean factor "proportional". If ALPHA is null, the step size will be searched through Newton method and it will stop when step reaches the maximum step or the difference matches ToleranceofStepsize; else step size will be fixed with ALPHA . The loss will end when the difference or proportional difference or the total loss value matches the "ToleranceofLoss".
stressBound	The loop of method NelderMead will stop when stress is beyond the stressBound.
maximumStep	The maximum searching step for method NelderMead and Newton method of calculating distance.
planeSize	The plane size of calculating disjoint intersections numerically.
lower	The lower bound of the interval to be searched for the "goldenSectionSearch" and "L-BFGS-B". See Details.
upper	The upper bound of the interval to be searched for the "goldenSectionSearch" and "Brent". See Details.
control	A list of control parameters. See Details
hessian	Logical. A numerically differentiated Hessian matrix be returned or not. See Details.
mar	Plot margins.
cols	Color of balls. If NULL, rainbow color will be set.
alpha	Color darkness.
smooth	For 3D plot, if true, the balls will be much more smoother. However, based on the high resolution, if the number of balls is too much, when rotating, the new window stumbles.
...	Any further graphical parameters to be passed to the plot function.

Details

1. One way sets must be given in disjoint.combination. e.g.disjoint.combination = c( B=2, AB=0.5) is not allowed. disjoint.combination = c(A = 0, B=2, AB=0.5) works. 2. Except "NelderMead" and "lineSearch", "goldenSectionSearch" in scaleSearch and twoWaySearch is based on optimize and the rest methods are based on optim. 3. lower, upper, control and hessian share the same parameters with optim, and lower, upper can also be used in optimize

Value

An object of the class vennplot with following components:

xy

centres of the balls (columns are (x, y) or (x, y, z) coordinates).

radius

radii of the balls.

loss

total loss of vennplot.

stress

stress value for solution.

Examples

# 3D Venn plot with arbitray sets
disjoint.combinations = c(A=80, B=50,C=100, D = 100,E = 100,
                          "A&C"=30, "A&D"= 30,"B&E" = 30, "A&E" = 40, h = 40, "B&h" = 10)
ve = vennplot(disjoint.combinations, ThreeD = TRUE)

# data frame
vennplot(disjoint.combinations = sharks, vars = c("Au","USA","Fa","Sex"),
         scaleSearch = "lineSearch", expand = 1.1)

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