Learn R Programming

censorcopula (version 2.0)

intervalFitb: Using censor method to break ties

Description

Estimate the parameter of copula with interval censor method to break ties in data.

Usage

intervalFitb(copula, method, x, start, lower, upper, optim.control, estimate.variance, hideWarnings, bound.eps)

Arguments

copula
Type of copula to fit the data
method
Method used in the 'optim' function
x
Data with ties
start
Initial value used in 'optim' function
lower,upper
Bounds on the variables for the "L-BFGS-B" method or method "Brent"
optim.control
A list of control parameters
estimate.variance
Estimate variance
hideWarnings
Hide warnings in procedure of estimation
bound.eps
Minimum finite distance

Value

fit
Estimation of parameter

Details

Except the 'copula', 'x' and 'method', other inputs of the intervalFitb function has default value.

For method,

Method "BFGS" is a quasi-Newton method (also known as a variable metric algorithm), specifically that published simultaneously in 1970 by Broyden, Fletcher, Goldfarb and Shanno. This uses function values and gradients to build up a picture of the surface to be optimized.

Method "L-BFGS-B" is that of Byrd et. al. (1995) which allows box constraints, that is each variable can be given a lower and/or upper bound. The initial value must satisfy the constraints. This uses a limited-memory modification of the BFGS quasi-Newton method. If non-trivial bounds are supplied, this method will be selected, with a warning.

Method "Brent" is for one-dimensional problems only, using 'optimize' function. It can be useful in cases where optim() is used inside other functions where only method can be specified, such as in mle from package stats4.

References

None

Examples

Run this code
library(copula)

## Generate sample and introduce ties
data <- rCopula(50, claytonCopula(2))
data[, 1] <- round(data[, 1], digit=1)

## Estimate parameter of clayton copula from the sample
intervalFitb(copula=claytonCopula(2), method="BFGS", data)

Run the code above in your browser using DataLab