bivariate (version 0.7.0)

16_PD_bimodal: Bimodal Distributions

Description

Bivariate bimodal distributions.

NOTE THAT THE ORDER OR THE FUNCTION ARGUMENTS HAS CHANGED. (In version 0.7.x)

Usage

bmbvpdf (
    mean.X1=0, mean.X2=0,
    mean.Y1=0, mean.Y2=0,
      sd.X1=1,   sd.X2=1,
      sd.Y1=1,   sd.Y2=1)
bmbvcdf (
    mean.X1=0, mean.X2=0,
    mean.Y1=0, mean.Y2=0,
      sd.X1=1,   sd.X2=1,
      sd.Y1=1,   sd.Y2=1)
bmbvpdf.2 (
    mean.X1=0, mean.X2=0,
    mean.Y1=0, mean.Y2=0,
     var.X1=1,  var.X2=1,
     var.Y1=1,  var.Y2=1)
bmbvcdf.2 (
    mean.X1=0, mean.X2=0,
    mean.Y1=0, mean.Y2=0,
     var.X1=1,  var.X2=1,
     var.Y1=1,  var.Y2=1)

Arguments

mean.X1, mean.Y1

Numeric values, giving the means of the first X and Y components.

sd.X1, sd.Y1

Positive numeric values, giving the standard deviations of the first X and Y components.

var.X1, var.Y1

Positive numeric values, giving the variances of the first X and Y components.

mean.X2, mean.Y2

Numeric values, giving the means of the second X and Y components.

sd.X2, sd.Y2

Positive numeric values, giving the standard deviations of the second X and Y components.

var.X2, var.Y2

Positive numeric values, giving the variances of the second X and Y components.

Value

Self-referencing S4-based function objects.

Refer to Function Objects.

References

Refer to the vignette for an overview, references, theoretical background and better examples.

See Also

Uniform For uniform distributions.

Binomial, Poisson and Categorical For other probability distributions of discrete random variables.

Normal, Dirichlet and Nonparametric For other probability distributions of continuous random variables.

Main Plotting Functions

Density Matrices

Examples

# NOT RUN {
f <- bmbvpdf (3.5, 6.5)

plot (f, axes = c (TRUE, FALSE), ref.arrows=FALSE, xat = c (3.5, 6.5) )
f (3.5, 0)
# }