simex (version 1.2)

build.mc.matrix: Build misclassification matrices from empirical estimations

Description

Empirical misclassification matrices to the power of lambda my not exist for small values of lambda. This function provides methods to estimate the nearst version of the misclassification matrix that satisfies the conditions a misclassification matrix has to fulfill

Usage

build.mc.matrix(mc.matrix, method = "series", tuning = sqrt(.Machine$double.eps), diag.cor = FALSE, tol = .Machine$double.eps, max.iter = 100)

Arguments

mc.matrix

an empirical misclassification matrix

method

method used to estimate the generator for the misclassification matrix (see Details)

tuning

security parameter for numerical reasons

diag.cor

should corrections substracted from the diagonal or from all values corresponding to the size

tol

tollerance level for series method for convegence

max.iter

maximal number of iterations for the serie method to converge

Value

• returns a misclassification matrix that is the closest estimate for a working misclassification matrix

Details

Method "series" constructs a generator via the series $(Pi-I) - (Pi-I)^2/2 + (Pi-I)^3/3 - ...$ Method "log" constructs the generator via taking the log of the misclassification matrix small negative off diagonal values are corrected and set to (0 + tuning). The amount used to correct for negative values is added to the diagonal element if diag.cor =T and distributed among all values if diag.cor =F Method "jlt" uses the method described by Jarro et al. (see Israel et al.)

References

Israel, Robert B., Rosenthal, Jeffrey S., Wei, Jason Z., Finding Generators for Markov Chains via Empirical Transition Matrices, with Applications to Credit Ratings, Mathematical Finance, 11, 245--265

See Also

check.mc.matrix,mcsimex

Examples

Pi <- matrix(data = c(0.989,0.01,0.001,0.17,0.829,0.001,0.001,0.18,0.819), nrow =3, byrow =FALSE) 
check.mc.matrix(list(Pi))
check.mc.matrix(list(build.mc.matrix(Pi)))
build.mc.matrix(Pi)


pi3 <- matrix(c(0.8,0.2,0,0,0,0.8,0.1,0.1,0,0.1,0.8,0.1,0,0,0.3,0.7), nrow =4)
check.mc.matrix(list(pi3))
build.mc.matrix(pi3)
check.mc.matrix(list(build.mc.matrix(pi3)))